From e26724457ea2c9785baaa94a15c5e88a07b411a4 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Mon, 30 Sep 2024 10:10:23 -0700 Subject: [PATCH 01/79] Base model trainer (#1521) * Base model trainer * flake8 * add testing notebook * add param validation & set defaults * Implement simple train method --- src/sagemaker/modules/__init__.py | 18 ++ src/sagemaker/modules/configs.py | 68 +++++ src/sagemaker/modules/constants.py | 16 ++ src/sagemaker/modules/image_spec.py | 96 +++++++ src/sagemaker/modules/scripts/train.sh | 1 + .../base_model_trainer.ipynb | 55 ++++ src/sagemaker/modules/train/__init__.py | 14 + src/sagemaker/modules/train/model_trainer.py | 249 ++++++++++++++++++ src/sagemaker/modules/types.py | 19 ++ src/sagemaker/modules/utils.py | 122 +++++++++ 10 files changed, 658 insertions(+) create mode 100644 src/sagemaker/modules/__init__.py create mode 100644 src/sagemaker/modules/configs.py create mode 100644 src/sagemaker/modules/constants.py create mode 100644 src/sagemaker/modules/image_spec.py create mode 100644 src/sagemaker/modules/scripts/train.sh create mode 100644 src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb create mode 100644 src/sagemaker/modules/train/__init__.py create mode 100644 src/sagemaker/modules/train/model_trainer.py create mode 100644 src/sagemaker/modules/types.py create mode 100644 src/sagemaker/modules/utils.py diff --git a/src/sagemaker/modules/__init__.py b/src/sagemaker/modules/__init__.py new file mode 100644 index 0000000000..67758a7af1 --- /dev/null +++ b/src/sagemaker/modules/__init__.py @@ -0,0 +1,18 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""SageMaker modules directory.""" +from __future__ import absolute_import + +from sagemaker_core.main.utils import logger as sagemaker_core_logger + +logger = sagemaker_core_logger diff --git a/src/sagemaker/modules/configs.py b/src/sagemaker/modules/configs.py new file mode 100644 index 0000000000..566436a3fa --- /dev/null +++ b/src/sagemaker/modules/configs.py @@ -0,0 +1,68 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Configuration classes.""" +from __future__ import absolute_import + +from typing import Optional +from pydantic import BaseModel + +from sagemaker_core.shapes import ( + ResourceConfig, + StoppingCondition, + OutputDataConfig, + AlgorithmSpecification, + Channel, + S3DataSource, + FileSystemDataSource, + TrainingImageConfig, + VpcConfig, +) + +__all__ = [ + "SourceCodeConfig", + "ResourceConfig", + "StoppingCondition", + "OutputDataConfig", + "AlgorithmSpecification", + "Channel", + "S3DataSource", + "FileSystemDataSource", + "TrainingImageConfig", + "VpcConfig", +] + + +class SourceCodeConfig(BaseModel): + """SourceCodeConfig. + + This config allows the user to specify the source code location, dependencies, + entry script, or commands to be executed in the training job container. + + Attributes: + command (Optional[str]): + The command(s) to execute in the training job container. Example: "python my_script.py". + If not specified, entry_script must be provided + source_dir (Optional[str]): + The local directory containing the source code to be used in the training job container. + requirements (Optional[str]): + The path within `source_dir` to a `requirements.txt` file. If specified, the listed + requirements will be installed in the training job container. + entry_script (Optional[str]): + The path within `source_dir` to the entry script that will be executed in the training + job container. If not specified, command must be provided. + """ + + command: Optional[str] + source_dir: Optional[str] + requirements: Optional[str] + entry_script: Optional[str] diff --git a/src/sagemaker/modules/constants.py b/src/sagemaker/modules/constants.py new file mode 100644 index 0000000000..fbc3fd82bc --- /dev/null +++ b/src/sagemaker/modules/constants.py @@ -0,0 +1,16 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Constants module.""" +from __future__ import absolute_import + +DEFAULT_INSTANCE_TYPE = "ml.m5.xlarge" diff --git a/src/sagemaker/modules/image_spec.py b/src/sagemaker/modules/image_spec.py new file mode 100644 index 0000000000..7d744bca78 --- /dev/null +++ b/src/sagemaker/modules/image_spec.py @@ -0,0 +1,96 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""ImageSpec class module.""" +from __future__ import absolute_import + +from typing import Optional + +from sagemaker import image_uris, Session +from sagemaker.serverless import ServerlessInferenceConfig +from sagemaker.training_compiler.config import TrainingCompilerConfig + + +class ImageSpec: + """ImageSpec class to get image URI for a specific framework version.""" + + def __init__( + self, + framework_name: str, + version: str, + image_scope: Optional[str] = None, + instance_type: Optional[str] = None, + py_version: Optional[str] = None, + region: Optional[str] = "us-west-2", + accelerator_type: Optional[str] = None, + container_version: Optional[str] = None, + distribution: Optional[dict] = None, + base_framework_version: Optional[str] = None, + training_compiler_config: Optional[TrainingCompilerConfig] = None, + model_id: Optional[str] = None, + model_version: Optional[str] = None, + hub_arn: Optional[str] = None, + tolerate_vulnerable_model: Optional[bool] = False, + tolerate_deprecated_model: Optional[bool] = False, + sdk_version: Optional[str] = None, + inference_tool: Optional[str] = None, + serverless_inference_config: Optional[ServerlessInferenceConfig] = None, + config_name: Optional[str] = None, + sagemaker_session: Optional[Session] = None, + ): + self.framework_name = framework_name + self.version = version + self.image_scope = image_scope + self.instance_type = instance_type + self.py_version = py_version + self.region = region + self.accelerator_type = accelerator_type + self.container_version = container_version + self.distribution = distribution + self.base_framework_version = base_framework_version + self.training_compiler_config = training_compiler_config + self.model_id = model_id + self.model_version = model_version + self.hub_arn = hub_arn + self.tolerate_vulnerable_model = tolerate_vulnerable_model + self.tolerate_deprecated_model = tolerate_deprecated_model + self.sdk_version = sdk_version + self.inference_tool = inference_tool + self.serverless_inference_config = serverless_inference_config + self.config_name = config_name + self.sagemaker_session = sagemaker_session + + def get_image_uri(self): + """Get image URI for a specific framework version.""" + return image_uris.retrieve( + framework=self.framework_name, + image_scope=self.image_scope, + instance_type=self.instance_type, + py_version=self.py_version, + region=self.region, + version=self.version, + accelerator_type=self.accelerator_type, + container_version=self.container_version, + distribution=self.distribution, + base_framework_version=self.base_framework_version, + training_compiler_config=self.training_compiler_config, + model_id=self.model_id, + model_version=self.model_version, + hub_arn=self.hub_arn, + tolerate_vulnerable_model=self.tolerate_vulnerable_model, + tolerate_deprecated_model=self.tolerate_deprecated_model, + sdk_version=self.sdk_version, + inference_tool=self.inference_tool, + serverless_inference_config=self.serverless_inference_config, + config_name=self.config_name, + sagemaker_session=self.sagemaker_session, + ) diff --git a/src/sagemaker/modules/scripts/train.sh b/src/sagemaker/modules/scripts/train.sh new file mode 100644 index 0000000000..21fce8ebd5 --- /dev/null +++ b/src/sagemaker/modules/scripts/train.sh @@ -0,0 +1 @@ +# place holder for training script \ No newline at end of file diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb new file mode 100644 index 0000000000..5d639f0350 --- /dev/null +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -0,0 +1,55 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys, os\n", + "# Get the absolute path of the root directory\n", + "root_dir = os.path.abspath(os.path.join(os.getcwd(), '../../..'))\n", + "sys.path.insert(0, root_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.train.model_trainer import ModelTrainer\n", + "\n", + "model_trainer = ModelTrainer(training_image=\"python:3.10.15-slim\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/sagemaker/modules/train/__init__.py b/src/sagemaker/modules/train/__init__.py new file mode 100644 index 0000000000..c5b5d01ed4 --- /dev/null +++ b/src/sagemaker/modules/train/__init__.py @@ -0,0 +1,14 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Sagemaker modules train directory.""" +from __future__ import absolute_import diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py new file mode 100644 index 0000000000..11867a3c0e --- /dev/null +++ b/src/sagemaker/modules/train/model_trainer.py @@ -0,0 +1,249 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""ModelTrainer class module.""" +from __future__ import absolute_import + +from typing import Optional, List, Union, Dict +from pydantic import BaseModel, ConfigDict, model_validator +from pydantic_core import PydanticCustomError + +from sagemaker_core.resources import TrainingJob +from sagemaker_core.shapes import AlgorithmSpecification + +from sagemaker import get_execution_role, Session +from sagemaker.modules.configs import ( + ResourceConfig, + StoppingCondition, + OutputDataConfig, + SourceCodeConfig, + TrainingImageConfig, + Channel, + VpcConfig, +) +from sagemaker.modules.utils import ( + _get_repo_name_from_image, + _get_unique_name, +) +from sagemaker.modules.types import DataSourceType +from sagemaker.modules.constants import DEFAULT_INSTANCE_TYPE +from sagemaker.modules.image_spec import ImageSpec +from sagemaker.modules import logger + + +class ModelTrainer(BaseModel): + """Class that trains a model using AWS SageMaker. + + Attributes: + session (Optiona(Session)): + The SageMaker session. + If not specified, a new session will be created. + role (Optional(str)): + The IAM role ARN for the training job. + If not specified, the default SageMaker execution role will be used. + base_name (Optional[str]): + The base name for the training job. + If not specified, a default name will be generated using the algorithm name + or training image. + resource_config (Optional[ResourceConfig]): + The resource configuration. This is used to specify the compute resources for + the training job. + If not specified, will default to 1 instance of ml.m5.xlarge. + stopping_condition (Optional[StoppingCondition]): + The stopping condition. This is used to specify the different stopping + conditions for the training job. + If not specified, will default to 1 hour max run time. + output_data_config (Optional[OutputDataConfig]): + The output data configuration. This is used to specify the output data location + for the training job. + If not specified, will default to s3:////output/. + input_data_channels (Optional[Union[List[Channel], Dict[str, DataSourceType]]]): + The input data channels for the training job. + Takes a list of Channel objects or a dictionary of channel names to DataSourceType. + DataSourceType can be an S3 URI string, local file path string, + S3DataSource object, or FileSystemDataSource object. + source_code_config (Optional[SourceCodeConfig]): + The source code configuration. This is used to configure the source code for + running the training job. + algorithm_name (Optional[str]): + The SageMaker marketplace algorithm name to use for the training job. + algorithm_name cannot be specified if training_image is specified. + training_image (Optional[Union[str, ImageSpec]]): + The training image URI to use for the training job container. + training_image cannot be specified if algorithm_name is specified. + training_input_mode (Optional[str]): + The input mode for the training job. Valid values are "Pipe", "File", "FastFile". + Defaults to "File". + training_image_config (Optional[TrainingImageConfig]: + Training image Config. This is the configuration to use an image from a private + Docker registry for a training job. + environment (Optional[Dict[str, str]]): + The environment variables for the training job. + hyper_parameters (Optional[Dict[str, str]]): + The hyperparameters for the training job. + vpc_config: (Optional[VpcConfig]): + The VPC configuration. + """ + + model_config = ConfigDict(arbitrary_types_allowed=True) + + session: Optional[Session] = None + role: Optional[str] = None + base_name: Optional[str] = None + resource_config: Optional[ResourceConfig] = None + stopping_condition: Optional[StoppingCondition] = None + output_data_config: Optional[OutputDataConfig] = None + input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] = None + source_code_config: Optional[SourceCodeConfig] = None + algorithm_name: Optional[str] = None + training_image: Optional[Union[str, ImageSpec]] = None + training_input_mode: Optional[str] = None + training_image_config: Optional[TrainingImageConfig] = None + environment: Optional[Dict[str, str]] = None + hyper_parameters: Optional[Dict[str, str]] = None + vpc_config: Optional[VpcConfig] = None + + @model_validator(mode="after") + def _set_defaults(self): + """Set default values for session, role, base_name, and other interdependent fields.""" + + if not self.training_image and not self.algorithm_name: + raise PydanticCustomError( + "validation_error", + "Atleast one of 'training_image' or 'algorithm_name' must be provided.", + ) + + if self.training_image and self.algorithm_name: + raise PydanticCustomError( + "validation_error", + "Only one of 'training_image' or 'algorithm_name' must be provided.", + ) + + if self.session is None: + self.session = Session() + logger.warn("Session not provided. Using default Session.") + + if self.role is None: + self.role = get_execution_role() + logger.warn(f"Role not provided. Using default role:\n{self.role}") + + if self.base_name is None: + if self.algorithm_name: + self.base_name = self.algorithm_name + elif self.training_image: + self.base_name = _get_repo_name_from_image(self.training_image) + + logger.warn(f"Base name not provided. Using default name:\n{self.base_name}") + + if self.resource_config is None: + self.resource_config = ResourceConfig( + volume_size_in_gb=30, + instance_count=1, + instance_type=DEFAULT_INSTANCE_TYPE, + volume_kms_key_id=None, + keep_alive_period_in_seconds=None, + instance_groups=None, + ) + logger.warn(f"ResourceConfig not provided. Using default:\n{self.resource_config}") + + if self.stopping_condition is None: + self.stopping_condition = StoppingCondition( + max_runtime_in_seconds=3600, + max_pending_time_in_seconds=None, + max_wait_time_in_seconds=None, + ) + logger.warn( + f"StoppingCondition not provided. Using default:\n{self.stopping_condition}" + ) + + if self.output_data_config is None: + session = self.session + base_name = self.base_name + self.output_data_config = OutputDataConfig( + s3_output_path=f"s3://{session.default_bucket()}/{base_name}/output", + compression_type="NONE", + kms_key_id=None, + ) + logger.warn(f"OutputDataConfig not provided. Using default:\n{self.output_data_config}") + return self + + def train( + self, + input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] = None, + source_code_config: Optional[SourceCodeConfig] = None, + hyper_parameters: Optional[Dict[str, str]] = None, + environment: Optional[Dict[str, str]] = None, + wait: bool = True, + logs: bool = True, + ): + """Train a model using AWS SageMaker. + + Args: + input_data_channels (Optional[Union[List[Channel], Dict[str, DataSourceType]]]): + The input data channels for the training job. + Takes a list of Channel objects or a dictionary of channel names to DataSourceType. + DataSourceType can be an S3 URI string, local file path string, + S3DataSource object, or FileSystemDataSource object. + source_code_config (Optional[SourceCodeConfig]): + The source code configuration. This is used to configure the source code for + running the training job. + hyper_parameters (Optional[Dict[str, str]]): + The hyperparameters for the training job. + environment (Optional[Dict[str,str]]): + The environment variables for the training job. + wait (Optional[bool]): + Whether to wait for the training job to complete before returning. + Defaults to True. + logs (Optional[bool]): + Whether to display the training container logs while training. + Defaults to True. + """ + if input_data_channels: + self.input_data_channels = input_data_channels + if source_code_config: + self.source_code_config = source_code_config + if hyper_parameters: + self.hyper_parameters = hyper_parameters + if environment: + self.environment = environment + + algorithm_specification = AlgorithmSpecification( + algorithm_name=self.algorithm_name, + training_image=self.training_image, # TODO: Implement ImageSpec + training_input_mode=self.training_input_mode, + training_image_config=self.training_image_config, + ) + + if self.source_code_config: + # TODO: Implement creating the run.sh script + pass + if self.input_data_channels: + # TODO: Implement InputDataConfig + pass + + training_job = TrainingJob.create( + session=self.session.boto_session, + role_arn=self.role, + training_job_name=_get_unique_name(self.base_name), + algorithm_specification=algorithm_specification, + resource_config=self.resource_config, + stopping_condition=self.stopping_condition, + output_data_config=self.output_data_config, + environment=self.environment, + hyper_parameters=self.hyper_parameters, + vpc_config=self.vpc_config, + ) + + if wait: + training_job.wait(logs=logs) + if logs and not wait: + logger.warning("Not displaing the training container logs as 'wait' is set to False.") diff --git a/src/sagemaker/modules/types.py b/src/sagemaker/modules/types.py new file mode 100644 index 0000000000..18bdcce3bd --- /dev/null +++ b/src/sagemaker/modules/types.py @@ -0,0 +1,19 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Types module.""" +from __future__ import absolute_import + +from typing import Union +from sagemaker.modules.configs import S3DataSource, FileSystemDataSource + +DataSourceType = Union[str, S3DataSource, FileSystemDataSource] diff --git a/src/sagemaker/modules/utils.py b/src/sagemaker/modules/utils.py new file mode 100644 index 0000000000..dc70e86efb --- /dev/null +++ b/src/sagemaker/modules/utils.py @@ -0,0 +1,122 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Utils module.""" +from __future__ import absolute_import + +import re +import os +from datetime import datetime +from typing import Literal + + +def _is_valid_ecr_image(image: str) -> bool: + """Check if the image is a valid ECR image URI. + + Args: + image (str): The image URI sting to validate + + Returns: + bool: True if the image is a valid ECR image URI, False otherwise + """ + pattern = r"^\d{12}\.dkr\.ecr\.\w+-\d{1,2}\.amazonaws\.com\/[\w-]+(:[\w.-]+)?$" + return bool(re.match(pattern, image)) + + +def _is_valid_s3_uri(path: str, path_type: Literal["File", "Directory", "Any"] = "Any") -> bool: + """Check if the path is a valid S3 URI. + + This method checks if the path is a valid S3 URI. If the path_type is specified, + it will also check if the path is a file or a directory. + This method does not check if the S3 bucket or object exists. + + Args: + path (str): S3 URI to validate + path_type (Optional(Literal["File", "Directory", "Any"])): The type of the path to validate. + Defaults to "Any". + + Returns: + bool: True if the path is a valid S3 URI, False otherwise + """ + # Check if the path is a valid S3 URI + if not path.startswith("s3://"): + return False + + if path_type == "File": + # If it's a file, it should not end with a slash + return not path.endswith("/") + if path_type == "Directory": + # If it's a directory, it should end with a slash + return path.endswith("/") + + return path_type == "Any" + + +def _is_valid_path(path: str, path_type: Literal["File", "Directory", "Any"] = "Any") -> bool: + """Check if the path is a valid local path. + + Args: + path (str): Local path to validate + path_type (Optional(Literal["File", "Directory", "Any"])): The type of the path to validate. + Defaults to "Any". + + Returns: + bool: True if the path is a valid local path, False otherwise + """ + if not os.path.exists(path): + return False + + if path_type == "File": + return os.path.isfile(path) + if path_type == "Directory": + return os.path.isdir(path) + + return path_type == "Any" + + +def _get_unique_name(base, max_length=63): + """Generate a unique name based on the base name. + + This method generates a unique name based on the base name. + The unique name is generated by appending the current timestamp + to the base name. + + Args: + base (str): The base name to use + max_length (int): The maximum length of the unique name. Defaults to 63. + + Returns: + str: The unique name + """ + current_time = datetime.now().strftime("%Y%m%d%H%M%S") + unique_name = f"{base}-{current_time}" + unique_name = re.sub(r"[^a-zA-Z0-9-]", "", unique_name) # Remove invalid characters + unique_name = unique_name[:max_length] # Truncate to max_length + return unique_name + + +def _get_repo_name_from_image(image: str) -> str: + """Get the repository name from the image URI. + + Example: + ``` python + _get_repo_name_from_image("123456789012.dkr.ecr.us-west-2.amazonaws.com/my-repo:latest") + # Returns "my-repo" + ``` + + Args: + image (str): The image URI + + Returns: + str: The repository name + """ + return image.split("/")[-1].split(":")[0] From 9774f9ed0605c0099c9ecee0018d5b490375257f Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Wed, 2 Oct 2024 17:31:18 -0700 Subject: [PATCH 02/79] feature: support script mode with local train.sh (#1523) * feature: support script mode with local train.sh * Stop tracking train.sh and add it to .gitignore * update message * make dir if not exist * fix docs * fix: docstyle * Address comments * fix hyperparams * Revert pydantic custom error * pylint --- .gitignore | 1 + src/sagemaker/modules/configs.py | 22 +- src/sagemaker/modules/constants.py | 14 + src/sagemaker/modules/image_spec.py | 7 +- src/sagemaker/modules/scripts/train.sh | 1 - src/sagemaker/modules/templates.py | 51 +++ .../base_model_trainer.ipynb | 26 +- src/sagemaker/modules/train/model_trainer.py | 308 ++++++++++++++++-- 8 files changed, 386 insertions(+), 44 deletions(-) delete mode 100644 src/sagemaker/modules/scripts/train.sh create mode 100644 src/sagemaker/modules/templates.py diff --git a/.gitignore b/.gitignore index ad6e488dbd..9b8f732547 100644 --- a/.gitignore +++ b/.gitignore @@ -32,6 +32,7 @@ env/ .python-version *.html **/_repack_script_launcher.sh +src/sagemaker/modules/scripts/train.sh tests/data/**/_repack_model.py tests/data/experiment/sagemaker-dev-1.0.tar.gz src/sagemaker/serve/tmp_workspace \ No newline at end of file diff --git a/src/sagemaker/modules/configs.py b/src/sagemaker/modules/configs.py index 566436a3fa..c53449a1d3 100644 --- a/src/sagemaker/modules/configs.py +++ b/src/sagemaker/modules/configs.py @@ -10,7 +10,15 @@ # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. -"""Configuration classes.""" +"""This module provides the configuration classes used in `sagemaker.modules`. + +Some of these classes are re-exported from `sagemaker-core.shapes`. For convinence, +users can import these classes directly from `sagemaker.modules.configs`. + +For more documentation on `sagemaker-core.shapes`, see: + - https://sagemaker-core.readthedocs.io/en/stable/#sagemaker-core-shapes +""" + from __future__ import absolute_import from typing import Optional @@ -20,8 +28,8 @@ ResourceConfig, StoppingCondition, OutputDataConfig, - AlgorithmSpecification, Channel, + DataSource, S3DataSource, FileSystemDataSource, TrainingImageConfig, @@ -33,8 +41,8 @@ "ResourceConfig", "StoppingCondition", "OutputDataConfig", - "AlgorithmSpecification", "Channel", + "DataSource", "S3DataSource", "FileSystemDataSource", "TrainingImageConfig", @@ -62,7 +70,7 @@ class SourceCodeConfig(BaseModel): job container. If not specified, command must be provided. """ - command: Optional[str] - source_dir: Optional[str] - requirements: Optional[str] - entry_script: Optional[str] + command: Optional[str] = None + source_dir: Optional[str] = None + requirements: Optional[str] = None + entry_script: Optional[str] = None diff --git a/src/sagemaker/modules/constants.py b/src/sagemaker/modules/constants.py index fbc3fd82bc..7cdd10e970 100644 --- a/src/sagemaker/modules/constants.py +++ b/src/sagemaker/modules/constants.py @@ -12,5 +12,19 @@ # language governing permissions and limitations under the License. """Constants module.""" from __future__ import absolute_import +import os DEFAULT_INSTANCE_TYPE = "ml.m5.xlarge" + +SOURCE_CODE_CONTAINER_PATH = "/opt/ml/input/data/code" + +SM_CODE_CONTAINER_PATH = "/opt/ml/input/data/sm_code" +SM_CODE_LOCAL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "scripts") +TRAIN_SCRIPT = "train.sh" + +DEFAULT_CONTAINER_ENTRYPOINT = ["/bin/bash"] +DEFAULT_CONTAINER_ARGUMENTS = [ + "-c", + f"chmod +x {SM_CODE_CONTAINER_PATH}/{TRAIN_SCRIPT} " + + f"&& {SM_CODE_CONTAINER_PATH}/{TRAIN_SCRIPT}", +] diff --git a/src/sagemaker/modules/image_spec.py b/src/sagemaker/modules/image_spec.py index 7d744bca78..7d5d38cb8c 100644 --- a/src/sagemaker/modules/image_spec.py +++ b/src/sagemaker/modules/image_spec.py @@ -69,8 +69,13 @@ def __init__( self.config_name = config_name self.sagemaker_session = sagemaker_session - def get_image_uri(self): + def get_image_uri( + self, image_scope: Optional[str] = None, instance_type: Optional[str] = None + ) -> str: """Get image URI for a specific framework version.""" + + self.image_scope = image_scope or self.image_scope + self.instance_type = instance_type or self.instance_type return image_uris.retrieve( framework=self.framework_name, image_scope=self.image_scope, diff --git a/src/sagemaker/modules/scripts/train.sh b/src/sagemaker/modules/scripts/train.sh deleted file mode 100644 index 21fce8ebd5..0000000000 --- a/src/sagemaker/modules/scripts/train.sh +++ /dev/null @@ -1 +0,0 @@ -# place holder for training script \ No newline at end of file diff --git a/src/sagemaker/modules/templates.py b/src/sagemaker/modules/templates.py new file mode 100644 index 0000000000..f040919829 --- /dev/null +++ b/src/sagemaker/modules/templates.py @@ -0,0 +1,51 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Templates module.""" +from __future__ import absolute_import + +TRAIN_SCRIPT_TEMPLATE = """ +#!/bin/bash +echo "Starting training script" + +echo "/opt/ml/input/config/resourceconfig.json:" +cat /opt/ml/input/config/resourceconfig.json +echo + +echo "/opt/ml/input/config/inputdataconfig.json:" +cat /opt/ml/input/config/inputdataconfig.json +echo + +echo "/opt/ml/input/config/hyperparameters.json:" +cat /opt/ml/input/config/hyperparameters.json +echo + +python --version +{working_dir} +{install_requirements} +CMD="{command}" +echo "Running command: $CMD" +eval $CMD +EXIT_STATUS=$? + +if [ $EXIT_STATUS -ne 0 ]; then + echo "Command failed with exit status $EXIT_STATUS" + if [ ! -s /opt/ml/output/failure ]; then + echo "Command failed with exit code $EXIT_STATUS. +For more details, see CloudWatch logs at 'aws/sagemaker/TrainingJobs'. +TrainingJob - $TRAINING_JOB_NAME" >> /opt/ml/output/failure + fi + exit $EXIT_STATUS +else + echo "Command succeeded" +fi +""" diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index 5d639f0350..0b832d540e 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -19,8 +19,32 @@ "outputs": [], "source": [ "from sagemaker.modules.train.model_trainer import ModelTrainer\n", + "from sagemaker.modules.image_spec import ImageSpec\n", "\n", - "model_trainer = ModelTrainer(training_image=\"python:3.10.15-slim\")\n" + "pytorch_image = ImageSpec(\n", + " framework_name=\"pytorch\",\n", + " version=\"1.13.1\",\n", + " py_version=\"py39\"\n", + ")\n", + "\n", + "python_ecr_image = \"public.ecr.aws/docker/library/python:3.10.15-slim\"\n", + "python_docker_image = \"python:3.10.15-slim\"\n", + "\n", + "model_trainer = ModelTrainer(training_image=pytorch_image)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.configs import SourceCodeConfig\n", + "\n", + "source_code_config = SourceCodeConfig(\n", + " command=\"echo 'Hello World' && env\",\n", + ")\n", + "model_trainer.train(source_code_config=source_code_config)" ] }, { diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 11867a3c0e..3dd3e58fb7 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -13,9 +13,10 @@ """ModelTrainer class module.""" from __future__ import absolute_import +import os + from typing import Optional, List, Union, Dict -from pydantic import BaseModel, ConfigDict, model_validator -from pydantic_core import PydanticCustomError +from pydantic import BaseModel, ConfigDict from sagemaker_core.resources import TrainingJob from sagemaker_core.shapes import AlgorithmSpecification @@ -28,14 +29,27 @@ SourceCodeConfig, TrainingImageConfig, Channel, + DataSource, + S3DataSource, + FileSystemDataSource, VpcConfig, ) from sagemaker.modules.utils import ( _get_repo_name_from_image, _get_unique_name, + _is_valid_path, + _is_valid_s3_uri, ) from sagemaker.modules.types import DataSourceType -from sagemaker.modules.constants import DEFAULT_INSTANCE_TYPE +from sagemaker.modules.constants import ( + DEFAULT_INSTANCE_TYPE, + SOURCE_CODE_CONTAINER_PATH, + SM_CODE_LOCAL_PATH, + TRAIN_SCRIPT, + DEFAULT_CONTAINER_ENTRYPOINT, + DEFAULT_CONTAINER_ARGUMENTS, +) +from sagemaker.modules.templates import TRAIN_SCRIPT_TEMPLATE from sagemaker.modules.image_spec import ImageSpec from sagemaker.modules import logger @@ -75,7 +89,7 @@ class ModelTrainer(BaseModel): The source code configuration. This is used to configure the source code for running the training job. algorithm_name (Optional[str]): - The SageMaker marketplace algorithm name to use for the training job. + The SageMaker marketplace algorithm name/arn to use for the training job. algorithm_name cannot be specified if training_image is specified. training_image (Optional[Union[str, ImageSpec]]): The training image URI to use for the training job container. @@ -106,43 +120,85 @@ class ModelTrainer(BaseModel): source_code_config: Optional[SourceCodeConfig] = None algorithm_name: Optional[str] = None training_image: Optional[Union[str, ImageSpec]] = None - training_input_mode: Optional[str] = None + training_input_mode: Optional[str] = "File" training_image_config: Optional[TrainingImageConfig] = None environment: Optional[Dict[str, str]] = None hyper_parameters: Optional[Dict[str, str]] = None vpc_config: Optional[VpcConfig] = None - @model_validator(mode="after") - def _set_defaults(self): - """Set default values for session, role, base_name, and other interdependent fields.""" - - if not self.training_image and not self.algorithm_name: - raise PydanticCustomError( - "validation_error", + def _validate_training_image_and_algorithm_name( + self, training_image: Optional[str], algorithm_name: Optional[str] + ): + """Validate that only one of 'training_image' or 'algorithm_name' is provided.""" + if not training_image and not algorithm_name: + raise ValueError( "Atleast one of 'training_image' or 'algorithm_name' must be provided.", ) - - if self.training_image and self.algorithm_name: - raise PydanticCustomError( - "validation_error", + if training_image and algorithm_name: + raise ValueError( "Only one of 'training_image' or 'algorithm_name' must be provided.", ) + def _validate_source_code_config(self, source_code_config: Optional[SourceCodeConfig]): + """Validate the source code configuration.""" + if source_code_config: + if source_code_config.requirements or source_code_config.entry_script: + source_dir = source_code_config.source_dir + requirements = source_code_config.requirements + entry_script = source_code_config.entry_script + if not source_dir: + raise ValueError( + "If 'requirements' or 'entry_script' is provided in 'source_code_config', " + + "'source_dir' must also be provided.", + ) + if not _is_valid_path(source_dir, path_type="Directory"): + raise ValueError( + f"Invalid 'source_dir' path: {source_dir}. " + "Must be a valid directory.", + ) + if requirements: + if not _is_valid_path( + f"{source_dir}/{entry_script}", + path_type="File", + ): + raise ValueError( + f"Invalid 'requirements': {requirements}. " + + "Must be a valid file within the 'source_dir'.", + ) + if entry_script: + if not _is_valid_path( + f"{source_dir}/{entry_script}", + path_type="File", + ): + raise ValueError( + f"Invalid 'entry_script': {entry_script}. " + + "Must be a valid file within the 'source_dir'.", + ) + + def __init__(self, **data): + """Set default values for session, role, base_name, and other interdependent fields.""" + # Validate types with pydantic + super().__init__(**data) + + self._validate_training_image_and_algorithm_name(self.training_image, self.algorithm_name) + self._validate_source_code_config(self.source_code_config) + if self.session is None: self.session = Session() - logger.warn("Session not provided. Using default Session.") + logger.warning("Session not provided. Using default Session.") if self.role is None: self.role = get_execution_role() - logger.warn(f"Role not provided. Using default role:\n{self.role}") + logger.warning(f"Role not provided. Using default role:\n{self.role}") if self.base_name is None: if self.algorithm_name: - self.base_name = self.algorithm_name + self.base_name = f"{self.algorithm_name}-job" elif self.training_image: - self.base_name = _get_repo_name_from_image(self.training_image) - - logger.warn(f"Base name not provided. Using default name:\n{self.base_name}") + if isinstance(self.training_image, ImageSpec): + self.base_name = f"{self.training_image.framework_name}-job" + else: + self.base_name = f"{_get_repo_name_from_image(self.training_image)}-job" + logger.warning(f"Base name not provided. Using default name:\n{self.base_name}") if self.resource_config is None: self.resource_config = ResourceConfig( @@ -153,7 +209,7 @@ def _set_defaults(self): keep_alive_period_in_seconds=None, instance_groups=None, ) - logger.warn(f"ResourceConfig not provided. Using default:\n{self.resource_config}") + logger.warning(f"ResourceConfig not provided. Using default:\n{self.resource_config}") if self.stopping_condition is None: self.stopping_condition = StoppingCondition( @@ -161,7 +217,7 @@ def _set_defaults(self): max_pending_time_in_seconds=None, max_wait_time_in_seconds=None, ) - logger.warn( + logger.warning( f"StoppingCondition not provided. Using default:\n{self.stopping_condition}" ) @@ -173,8 +229,16 @@ def _set_defaults(self): compression_type="NONE", kms_key_id=None, ) - logger.warn(f"OutputDataConfig not provided. Using default:\n{self.output_data_config}") - return self + logger.warning( + f"OutputDataConfig not provided. Using default:\n{self.output_data_config}" + ) + + if self.training_image: + if isinstance(self.training_image, ImageSpec): + self.training_image = self.training_image.get_image_uri( + image_scope="training", instance_type=self.resource_config.instance_type + ) + logger.info(f"Training image URI: {self.training_image}") def train( self, @@ -216,19 +280,60 @@ def train( if environment: self.environment = environment + input_data_config = self._get_input_data_config(self.input_data_channels) + + container_entrypoint = None + container_arguments = None + if self.source_code_config: + if not input_data_config: + input_data_config = [] + + # If source code is provided, create a channel for the source code + # The source code will be mounted at /opt/ml/input/data/code in the container + # and set as the working directory + working_dir = "" + if self.source_code_config.source_dir: + source_code_channel = self.create_input_data_channel( + "code", self.source_code_config.source_dir + ) + input_data_config.append(source_code_channel) + working_dir = SOURCE_CODE_CONTAINER_PATH + + # Get the commands to execute in the training job container + # and prepare the train.sh script + commands = self._get_script_mode_command(self.source_code_config) + self._prepare_train_script( + command=commands, + requirements=self.source_code_config.requirements, + working_dir=working_dir, + ) + + # Create an input channel for scripts packaged by the sdk + sm_scripts_channel = self.create_input_data_channel("sm_code", SM_CODE_LOCAL_PATH) + input_data_config.append(sm_scripts_channel) + + # If source_code_config is provided, we will always use + # the default container entrypoint and arguments + # to execute the train.sh script. + # Any commands generated from the source_code_config will be + # executed from the train.sh script. + container_entrypoint = DEFAULT_CONTAINER_ENTRYPOINT + container_arguments = DEFAULT_CONTAINER_ARGUMENTS + algorithm_specification = AlgorithmSpecification( algorithm_name=self.algorithm_name, - training_image=self.training_image, # TODO: Implement ImageSpec + training_image=self.training_image, training_input_mode=self.training_input_mode, training_image_config=self.training_image_config, + container_entrypoint=container_entrypoint, + container_arguments=container_arguments, ) - if self.source_code_config: - # TODO: Implement creating the run.sh script - pass - if self.input_data_channels: - # TODO: Implement InputDataConfig - pass + # Unfortunately, API requires hyperparameters to be strings + string_hyper_parameters = {} + if hyper_parameters: + for hyper_parameter, value in hyper_parameters.items(): + string_hyper_parameters[hyper_parameter] = str(value) training_job = TrainingJob.create( session=self.session.boto_session, @@ -237,9 +342,10 @@ def train( algorithm_specification=algorithm_specification, resource_config=self.resource_config, stopping_condition=self.stopping_condition, + input_data_config=input_data_config, output_data_config=self.output_data_config, environment=self.environment, - hyper_parameters=self.hyper_parameters, + hyper_parameters=string_hyper_parameters, vpc_config=self.vpc_config, ) @@ -247,3 +353,137 @@ def train( training_job.wait(logs=logs) if logs and not wait: logger.warning("Not displaing the training container logs as 'wait' is set to False.") + + def create_input_data_channel(self, channel_name: str, data_source: DataSourceType) -> Channel: + """Create an input data channel for the training job. + + Args: + channel_name (str): The name of the input data channel. + data_source (DataSourceType): The data source for the input data channel. + DataSourceType can be an S3 URI string, local file path string, + S3DataSource object, or FileSystemDataSource object. + """ + channel = None + if isinstance(data_source, str): + if _is_valid_s3_uri(data_source): + channel = Channel( + channel_name=channel_name, + data_source=DataSource( + s3_data_source=S3DataSource( + s3_data_type="S3Prefix", + s3_uri=data_source, + s3_data_distribution_type="FullyReplicated", + ), + ), + input_mode="File", + ) + elif _is_valid_path(data_source): + s3_uri = self.session.upload_data( + path=data_source, + bucket=self.session.default_bucket(), + key_prefix=f"{self.base_name}/input/{channel_name}", + ) + channel = Channel( + channel_name=channel_name, + data_source=DataSource( + s3_data_source=S3DataSource( + s3_data_type="S3Prefix", + s3_uri=s3_uri, + s3_data_distribution_type="FullyReplicated", + ), + ), + input_mode="File", + ) + else: + raise ValueError(f"Not a valid S3 URI or local file path: {data_source}.") + elif isinstance(data_source, S3DataSource): + channel = Channel(channel_name=channel_name, data_source=data_source) + elif isinstance(data_source, FileSystemDataSource): + channel = Channel(channel_name=channel_name, data_source=data_source) + return channel + + def _get_input_data_config( + self, input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] + ) -> List[Channel]: + """Get the input data configuration for the training job. + + Args: + input_data_channels (Optional[Union[List[Channel], Dict[str, DataSourceType]]]): + The input data channels for the training job. + Takes a list of Channel objects or a dictionary of channel names to DataSourceType. + DataSourceType can be an S3 URI string, local file path string, + S3DataSource object, or FileSystemDataSource object. + """ + if input_data_channels is None: + return [] + + if isinstance(input_data_channels, list): + return input_data_channels + return [ + self.create_input_data_channel(channel_name, data_source) + for channel_name, data_source in input_data_channels.items() + ] + + def _get_script_mode_command(self, source_code_config: SourceCodeConfig) -> str: + """Get the command to execute in the training job container for script mode. + + Args: + source_code_config (SourceCodeConfig): The source code configuration. + """ + if source_code_config.command: + if source_code_config.entry_script: + logger.warning( + "Both 'command' and 'entry_script' are provided in the SourceCodeConfig. " + + "Defaulting to 'command'." + ) + commands = source_code_config.command.split() + return " ".join(commands) + + assert ( + source_code_config.entry_script + ), "Either 'command' or 'entry_script' must be provided." + + if source_code_config.entry_script.endswith(".py"): + return f"python {source_code_config.entry_script}" + if source_code_config.entry_script.endswith(".sh"): + return ( + f"chmod +x {source_code_config.entry_script} && " + f"bash {source_code_config.entry_script}" + ) + + raise ValueError( + f"Unsupported entry script: {source_code_config.entry_script}." + + "Only .py and .sh scripts are supported." + ) + + def _prepare_train_script( + self, command: str, requirements: Optional[str], working_dir: Optional[str] + ): + """Prepare the training script to be executed in the training job container. + + Args: + command (str): The command to execute in the training job container. + requirements (str): The path to the requirements file within the source code directory. + working_dir (str): The working directory for the training job container + """ + install_requirements = "" + if requirements: + install_requirements = "echo 'Installing requirements'\n" + install_requirements = f"pip install -r {requirements}" + + if working_dir: + working_dir = f"cd {working_dir}" + + train_script = TRAIN_SCRIPT_TEMPLATE.format( + working_dir=working_dir, + install_requirements=install_requirements, + command=command, + ) + + os.makedirs(SM_CODE_LOCAL_PATH, exist_ok=True) + with open(os.path.join(SM_CODE_LOCAL_PATH, TRAIN_SCRIPT), "w") as f: + f.write(train_script) + + def get_training_image_uri(self) -> str: + """Get the training image URI for the training job.""" + return self.training_image From fba3285c421c1eeccf45e9acb4b7a08bd8788300 Mon Sep 17 00:00:00 2001 From: Gokul Anantha Narayanan <166456257+nargokul@users.noreply.github.com> Date: Thu, 3 Oct 2024 15:05:46 -0700 Subject: [PATCH 03/79] Image Spec refactoring and updates (#1525) * Image Spec refactoring and updates * Unit tests and update function for Image Spec * Fix hugging face test * Fix Tests --- src/sagemaker/modules/image_spec.py | 296 ++++++++++++++---- tests/unit/sagemaker/modules/__init__.py | 0 .../unit/sagemaker/modules/test_image_spec.py | 42 +++ 3 files changed, 270 insertions(+), 68 deletions(-) create mode 100644 tests/unit/sagemaker/modules/__init__.py create mode 100644 tests/unit/sagemaker/modules/test_image_spec.py diff --git a/src/sagemaker/modules/image_spec.py b/src/sagemaker/modules/image_spec.py index 7d5d38cb8c..775c7e691f 100644 --- a/src/sagemaker/modules/image_spec.py +++ b/src/sagemaker/modules/image_spec.py @@ -13,41 +13,71 @@ """ImageSpec class module.""" from __future__ import absolute_import +import re +from enum import Enum from typing import Optional -from sagemaker import image_uris, Session -from sagemaker.serverless import ServerlessInferenceConfig -from sagemaker.training_compiler.config import TrainingCompilerConfig +from sagemaker import utils +from sagemaker.image_uris import _validate_version_and_set_if_needed, _version_for_config, \ + _config_for_framework_and_scope, _validate_py_version_and_set_if_needed, _registry_from_region, ECR_URI_TEMPLATE, \ + _get_latest_versions, _validate_instance_deprecation, _get_image_tag, _validate_arg +from packaging.version import Version + +DEFAULT_TOLERATE_MODEL = False + + +class Framework(Enum): + HUGGING_FACE = "huggingface" + HUGGING_FACE_NEURON = "huggingface-neuron" + HUGGING_FACE_NEURON_X = "huggingface-neuronx" + HUGGING_FACE_LLM = "huggingface-llm" + HUGGING_FACE_TEI_GPU = "huggingface-tei" + HUGGING_FACE_TEI_CPU = "huggingface-tei-cpu" + HUGGING_FACE_LLM_NEURONX = "huggingface-llm-neuronx" + HUGGING_FACE_TRAINING_COMPILER = "huggingface-training-compiler" + XGBOOST = "xgboost" + XG_BOOST_NEO = "xg-boost-neo" + SKLEARN = "sklearn" + PYTORCH = "pytorch" + PYTORCH_TRAINING_COMPILER = "pytorch-training-compiler" + DATA_WRANGLER = "data-wrangler" + STABILITYAI = "stabilityai" + SAGEMAKER_TRITONSERVER = "sagemaker-tritonserver" + + +class ImageScope(Enum): + TRAINING = "training" + INFERENCE = "inference" + INFERENCE_GRAVITON = "inference-graviton" + + +class Processor(Enum): + INF = "inf" + NEURON = "neuron" + GPU = "gpu" + CPU = "cpu" + TRN = "trn" class ImageSpec: """ImageSpec class to get image URI for a specific framework version.""" - def __init__( - self, - framework_name: str, - version: str, - image_scope: Optional[str] = None, - instance_type: Optional[str] = None, - py_version: Optional[str] = None, - region: Optional[str] = "us-west-2", - accelerator_type: Optional[str] = None, - container_version: Optional[str] = None, - distribution: Optional[dict] = None, - base_framework_version: Optional[str] = None, - training_compiler_config: Optional[TrainingCompilerConfig] = None, - model_id: Optional[str] = None, - model_version: Optional[str] = None, - hub_arn: Optional[str] = None, - tolerate_vulnerable_model: Optional[bool] = False, - tolerate_deprecated_model: Optional[bool] = False, - sdk_version: Optional[str] = None, - inference_tool: Optional[str] = None, - serverless_inference_config: Optional[ServerlessInferenceConfig] = None, - config_name: Optional[str] = None, - sagemaker_session: Optional[Session] = None, - ): - self.framework_name = framework_name + def __init__(self, + framework: Framework, + processor: Optional[Processor] = Processor.CPU, + region: Optional[str] = "us-west-2", + version=None, + py_version=None, + instance_type=None, + accelerator_type=None, + image_scope: ImageScope = ImageScope.TRAINING, + container_version=None, + distribution=None, + base_framework_version=None, + sdk_version=None, + inference_tool=None): + self.framework = framework + self.processor = processor self.version = version self.image_scope = image_scope self.instance_type = instance_type @@ -57,45 +87,175 @@ def __init__( self.container_version = container_version self.distribution = distribution self.base_framework_version = base_framework_version - self.training_compiler_config = training_compiler_config - self.model_id = model_id - self.model_version = model_version - self.hub_arn = hub_arn - self.tolerate_vulnerable_model = tolerate_vulnerable_model - self.tolerate_deprecated_model = tolerate_deprecated_model self.sdk_version = sdk_version self.inference_tool = inference_tool - self.serverless_inference_config = serverless_inference_config - self.config_name = config_name - self.sagemaker_session = sagemaker_session - - def get_image_uri( - self, image_scope: Optional[str] = None, instance_type: Optional[str] = None - ) -> str: - """Get image URI for a specific framework version.""" - - self.image_scope = image_scope or self.image_scope - self.instance_type = instance_type or self.instance_type - return image_uris.retrieve( - framework=self.framework_name, - image_scope=self.image_scope, - instance_type=self.instance_type, - py_version=self.py_version, - region=self.region, - version=self.version, - accelerator_type=self.accelerator_type, - container_version=self.container_version, - distribution=self.distribution, - base_framework_version=self.base_framework_version, - training_compiler_config=self.training_compiler_config, - model_id=self.model_id, - model_version=self.model_version, - hub_arn=self.hub_arn, - tolerate_vulnerable_model=self.tolerate_vulnerable_model, - tolerate_deprecated_model=self.tolerate_deprecated_model, - sdk_version=self.sdk_version, - inference_tool=self.inference_tool, - serverless_inference_config=self.serverless_inference_config, - config_name=self.config_name, - sagemaker_session=self.sagemaker_session, - ) + + def update_image_spec(self, **kwargs): + for key, value in kwargs.items(): + if hasattr(self, key): + setattr(self, key, value) + + def retrieve(self) -> str: + """Retrieves the ECR URI for the Docker image matching the given arguments. + + Ideally this function should not be called directly, rather it should be called from the + fit() function inside framework estimator. + + Args: + framework (Framework): The name of the framework or algorithm. + processor (Processor): The name of the processor (CPU, GPU, etc.). + region (str): The AWS region. + version (str): The framework or algorithm version. This is required if there is + more than one supported version for the given framework or algorithm. + py_version (str): The Python version. This is required if there is + more than one supported Python version for the given framework version. + instance_type (str): The SageMaker instance type. For supported types, see + https://aws.amazon.com/sagemaker/pricing. This is required if + there are different images for different processor types. + accelerator_type (str): Elastic Inference accelerator type. For more, see + https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html. + image_scope (str): The image type, i.e. what it is used for. + Valid values: "training", "inference", "inference_graviton", "eia". + If ``accelerator_type`` is set, ``image_scope`` is ignored. + container_version (str): the version of docker image. + Ideally the value of parameter should be created inside the framework. + For custom use, see the list of supported container versions: + https://github.com/aws/deep-learning-containers/blob/master/available_images.md + (default: None). + distribution (dict): A dictionary with information on how to run distributed training + sdk_version (str): the version of python-sdk that will be used in the image retrieval. + (default: None). + inference_tool (str): the tool that will be used to aid in the inference. + Valid values: "neuron, neuronx, None" + (default: None). + + Returns: + str: The ECR URI for the corresponding SageMaker Docker image. + + Raises: + NotImplementedError: If the scope is not supported. + ValueError: If the combination of arguments specified is not supported or + any PipelineVariable object is passed in. + VulnerableJumpStartModelError: If any of the dependencies required by the script have + known security vulnerabilities. + DeprecatedJumpStartModelError: If the version of the model is deprecated. + """ + config = _config_for_framework_and_scope(self.framework.value, + self.image_scope.value, + self.accelerator_type) + + original_version = self.version + try: + version = _validate_version_and_set_if_needed(self.version, config, self.framework.value) + except ValueError: + version = None + if not version: + version = self._fetch_latest_version_from_config(config) + + version_config = config["versions"][_version_for_config(version, config)] + + if "huggingface" in self.framework.value: + if version_config.get("version_aliases"): + full_base_framework_version = version_config["version_aliases"].get( + self.base_framework_version, self.base_framework_version + ) + _validate_arg(full_base_framework_version, list(version_config.keys()), "base framework") + version_config = version_config.get(full_base_framework_version) + + self.py_version = _validate_py_version_and_set_if_needed(self.py_version, + version_config, + self.framework.value) + version_config = version_config.get(self.py_version) or version_config + + registry = _registry_from_region(self.region, version_config["registries"]) + endpoint_data = utils._botocore_resolver().construct_endpoint("ecr", self.region) + if self.region == "il-central-1" and not endpoint_data: + endpoint_data = {"hostname": "ecr.{}.amazonaws.com".format(self.region)} + hostname = endpoint_data["hostname"] + + repo = version_config["repository"] + + # if container version is available in .json file, utilize that + if version_config.get("container_version"): + self.container_version = version_config["container_version"][self.processor.value] + + # Append sdk version in case of trainium instances + if repo in ["pytorch-training-neuron"]: + if not self.sdk_version: + sdk_version = _get_latest_versions(version_config["sdk_versions"]) + self.container_version = self.sdk_version + "-" + self.container_version + + if self.framework == Framework.HUGGING_FACE: + pt_or_tf_version = ( + re.compile("^(pytorch|tensorflow)(.*)$").match(self.base_framework_version).group(2) + ) + _version = original_version + + if repo in [ + "huggingface-pytorch-trcomp-training", + "huggingface-tensorflow-trcomp-training", + ]: + _version = version + if repo in [ + "huggingface-pytorch-inference-neuron", + "huggingface-pytorch-inference-neuronx", + ]: + if not sdk_version: + self.sdk_version = _get_latest_versions(version_config["sdk_versions"]) + self.container_version = self.sdk_version + "-" + self.container_version + if config.get("version_aliases").get(original_version): + _version = config.get("version_aliases")[original_version] + if ( + config.get("versions", {}) + .get(_version, {}) + .get("version_aliases", {}) + .get(self.base_framework_version, {}) + ): + _base_framework_version = config.get("versions")[_version]["version_aliases"][ + self.base_framework_version + ] + pt_or_tf_version = ( + re.compile("^(pytorch|tensorflow)(.*)$").match(_base_framework_version).group(2) + ) + + tag_prefix = f"{pt_or_tf_version}-transformers{_version}" + else: + tag_prefix = version_config.get("tag_prefix", version) + + if repo == f"{self.framework.value}-inference-graviton": + self.container_version = f"{self.container_version}-sagemaker" + _validate_instance_deprecation(self.framework, + self.instance_type, + version) + + tag = _get_image_tag( + self.container_version, + self.distribution, + self.image_scope.value, + self.framework, + self.inference_tool, + self.instance_type, + self.processor.value, + self.py_version, + tag_prefix, + version) + + if tag: + repo += ":{}".format(tag) + + return ECR_URI_TEMPLATE.format(registry=registry, hostname=hostname, repository=repo) + + def _fetch_latest_version_from_config(self, + framework_config: dict) -> str: + if self.image_scope.value in framework_config: + if image_scope_config := framework_config[self.image_scope.value]: + if version_aliases := image_scope_config["version_aliases"]: + if latest_version := version_aliases["latest"]: + return latest_version + versions = list(framework_config["versions"].keys()) + top_version = versions[0] + bottom_version = versions[-1] + + if Version(top_version) >= Version(bottom_version): + return top_version + return bottom_version diff --git a/tests/unit/sagemaker/modules/__init__.py b/tests/unit/sagemaker/modules/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/unit/sagemaker/modules/test_image_spec.py b/tests/unit/sagemaker/modules/test_image_spec.py new file mode 100644 index 0000000000..23d70f133c --- /dev/null +++ b/tests/unit/sagemaker/modules/test_image_spec.py @@ -0,0 +1,42 @@ +import unittest + +from sagemaker.modules.image_spec import ImageSpec, Framework, Processor + + +class TestImageSpec(unittest.TestCase): + + def test_image_spec_update(self): + image_spec = ImageSpec(framework=Framework.HUGGING_FACE) + assert image_spec.version == None + image_spec.update_image_spec(version="v3") + assert image_spec.version == "v3" + + def test_image_spec_retrive(self): + # Asserting substrings because full string uri can change with newer versions + image_spec = ImageSpec(framework=Framework.XGBOOST) + xgboost_uri = image_spec.retrieve() + assert "dkr.ecr.us-west-2.amazonaws.com" in xgboost_uri + assert "sagemaker-xgboost" in xgboost_uri + + image_spec = ImageSpec(framework=Framework.HUGGING_FACE, + processor=Processor.GPU, + base_framework_version="pytorch2.1.0") + hugging_face_uri = image_spec.retrieve() + assert "dkr.ecr.us-west-2.amazonaws.com" in hugging_face_uri + assert "huggingface-pytorch-training" in hugging_face_uri + + image_spec = ImageSpec(framework=Framework.PYTORCH) + pytorch_uri = image_spec.retrieve() + assert "dkr.ecr.us-west-2.amazonaws.com" in pytorch_uri + assert "pytorch-training" in pytorch_uri + + image_spec = ImageSpec(framework=Framework.SKLEARN) + sklearn_uri = image_spec.retrieve() + assert "dkr.ecr.us-west-2.amazonaws.com" in sklearn_uri + assert "sagemaker-scikit-learn" in sklearn_uri + + def test_image_spec_retrive_with_version(self): + image_spec = ImageSpec(framework=Framework.XGBOOST, + version="0.90-1") + xgboost_uri = image_spec.retrieve() + assert xgboost_uri == '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:0.90-1-cpu-py3' From 6a0224f0272064cefbc06ab1d77a4ec532ece287 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Thu, 3 Oct 2024 15:37:41 -0700 Subject: [PATCH 04/79] Add unit tests for ModelTrainer (#1527) * Add unit tests for ModelTrainer * Flake8 * format --- src/sagemaker/modules/train/model_trainer.py | 18 +- src/sagemaker/modules/utils.py | 13 - tests/unit/sagemaker/modules/test_utils.py | 140 ++++++++ .../unit/sagemaker/modules/train/__init__.py | 0 .../modules/train/test_model_trainer.py | 332 ++++++++++++++++++ 5 files changed, 484 insertions(+), 19 deletions(-) create mode 100644 tests/unit/sagemaker/modules/test_utils.py create mode 100644 tests/unit/sagemaker/modules/train/__init__.py create mode 100644 tests/unit/sagemaker/modules/train/test_model_trainer.py diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 3dd3e58fb7..9df9f57b56 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -108,7 +108,7 @@ class ModelTrainer(BaseModel): The VPC configuration. """ - model_config = ConfigDict(arbitrary_types_allowed=True) + model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid") session: Optional[Session] = None role: Optional[str] = None @@ -274,19 +274,20 @@ def train( if input_data_channels: self.input_data_channels = input_data_channels if source_code_config: + self._validate_source_code_config(source_code_config) self.source_code_config = source_code_config if hyper_parameters: self.hyper_parameters = hyper_parameters if environment: self.environment = environment - input_data_config = self._get_input_data_config(self.input_data_channels) + input_data_config = [] + if self.input_data_channels: + input_data_config = self._get_input_data_config(self.input_data_channels) container_entrypoint = None container_arguments = None if self.source_code_config: - if not input_data_config: - input_data_config = [] # If source code is provided, create a channel for the source code # The source code will be mounted at /opt/ml/input/data/code in the container @@ -397,9 +398,14 @@ def create_input_data_channel(self, channel_name: str, data_source: DataSourceTy else: raise ValueError(f"Not a valid S3 URI or local file path: {data_source}.") elif isinstance(data_source, S3DataSource): - channel = Channel(channel_name=channel_name, data_source=data_source) + channel = Channel( + channel_name=channel_name, data_source=DataSource(s3_data_source=data_source) + ) elif isinstance(data_source, FileSystemDataSource): - channel = Channel(channel_name=channel_name, data_source=data_source) + channel = Channel( + channel_name=channel_name, + data_source=DataSource(file_system_data_source=data_source), + ) return channel def _get_input_data_config( diff --git a/src/sagemaker/modules/utils.py b/src/sagemaker/modules/utils.py index dc70e86efb..a1e391f45c 100644 --- a/src/sagemaker/modules/utils.py +++ b/src/sagemaker/modules/utils.py @@ -19,19 +19,6 @@ from typing import Literal -def _is_valid_ecr_image(image: str) -> bool: - """Check if the image is a valid ECR image URI. - - Args: - image (str): The image URI sting to validate - - Returns: - bool: True if the image is a valid ECR image URI, False otherwise - """ - pattern = r"^\d{12}\.dkr\.ecr\.\w+-\d{1,2}\.amazonaws\.com\/[\w-]+(:[\w.-]+)?$" - return bool(re.match(pattern, image)) - - def _is_valid_s3_uri(path: str, path_type: Literal["File", "Directory", "Any"] = "Any") -> bool: """Check if the path is a valid S3 URI. diff --git a/tests/unit/sagemaker/modules/test_utils.py b/tests/unit/sagemaker/modules/test_utils.py new file mode 100644 index 0000000000..efe43f1792 --- /dev/null +++ b/tests/unit/sagemaker/modules/test_utils.py @@ -0,0 +1,140 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Utils Tests.""" +from __future__ import absolute_import + +import pytest + +from tests.unit import DATA_DIR +from sagemaker.modules.utils import ( + _is_valid_s3_uri, + _is_valid_path, + _get_unique_name, + _get_repo_name_from_image, +) + + +@pytest.mark.parametrize( + "test_case", + [ + { + "path": "s3://bucket/key", + "path_type": "Any", + "expected": True, + }, + { + "path": "s3://bucket/key", + "path_type": "File", + "expected": True, + }, + { + "path": "s3://bucket/key/", + "path_type": "Directory", + "expected": True, + }, + { + "path": "s3://bucket/key/", + "path_type": "File", + "expected": False, + }, + { + "path": "s3://bucket/key", + "path_type": "Directory", + "expected": False, + }, + { + "path": "/bucket/key", + "path_type": "Any", + "expected": False, + }, + ], +) +def test_is_valid_s3_uri(test_case): + assert _is_valid_s3_uri(test_case["path"], test_case["path_type"]) == test_case["expected"] + + +@pytest.mark.parametrize( + "test_case", + [ + { + "path": DATA_DIR, + "path_type": "Any", + "expected": True, + }, + { + "path": DATA_DIR, + "path_type": "Directory", + "expected": True, + }, + { + "path": f"{DATA_DIR}/dummy_input.txt", + "path_type": "File", + "expected": True, + }, + { + "path": f"{DATA_DIR}/dummy_input.txt", + "path_type": "Directory", + "expected": False, + }, + { + "path": f"{DATA_DIR}/non_existent", + "path_type": "Any", + "expected": False, + }, + ], +) +def test_is_valid_path(test_case): + assert _is_valid_path(test_case["path"], test_case["path_type"]) == test_case["expected"] + + +@pytest.mark.parametrize( + "test_case", + [ + { + "base": "test", + "max_length": 5, + }, + { + "base": "1111111111" * 7, + "max_length": None, + }, + ], +) +def test_get_unique_name(test_case): + assert ( + len(_get_unique_name(test_case["base"], test_case.get("max_length"))) + <= test_case["max_length"] + if test_case.get("max_length") + else 63 + ) + + +@pytest.mark.parametrize( + "test_case", + [ + { + "image": "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:latest", + "expected": "my-custom-image", + }, + { + "image": "my-custom-image:latest", + "expected": "my-custom-image", + }, + { + "image": "public.ecr.aws/docker/library/my-custom-image:latest", + "expected": "my-custom-image", + }, + ], +) +def test_get_repo_name_from_image(test_case): + assert _get_repo_name_from_image(test_case["image"]) == test_case["expected"] diff --git a/tests/unit/sagemaker/modules/train/__init__.py b/tests/unit/sagemaker/modules/train/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/unit/sagemaker/modules/train/test_model_trainer.py b/tests/unit/sagemaker/modules/train/test_model_trainer.py new file mode 100644 index 0000000000..5d4f57c937 --- /dev/null +++ b/tests/unit/sagemaker/modules/train/test_model_trainer.py @@ -0,0 +1,332 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""ModelTrainer Tests.""" +from __future__ import absolute_import + +import pytest +from unittest.mock import patch, MagicMock, call + +from sagemaker.session import Session +from sagemaker.modules.train.model_trainer import ModelTrainer +from sagemaker.modules.constants import DEFAULT_INSTANCE_TYPE, SM_CODE_LOCAL_PATH +from sagemaker.modules.configs import ( + ResourceConfig, + StoppingCondition, + OutputDataConfig, + SourceCodeConfig, + S3DataSource, + FileSystemDataSource, +) +from tests.unit import DATA_DIR + +DEFAULT_BASE_NAME = "dummy-image-job" +DEFAULT_IMAGE = "000000000000.dkr.ecr.us-west-2.amazonaws.com/dummy-image:latest" +DEFAULT_BUCKET = "sagemaker-us-west-2-000000000000" +DEFAULT_ROLE = "arn:aws:iam::000000000000:role/test-role" +DEFAULT_RESOURCE_CONFIG = ResourceConfig( + instance_count=1, + instance_type=DEFAULT_INSTANCE_TYPE, + volume_size_in_gb=30, + volume_kms_key_id=None, + keep_alive_period_in_seconds=None, + instance_groups=None, +) +DEFAULT_OUTPUT_DATA_CONFIG = OutputDataConfig( + s3_output_path=f"s3://{DEFAULT_BUCKET}/{DEFAULT_BASE_NAME}/output", + compression_type="NONE", + kms_key_id=None, +) +DEFAULT_STOPPING_CONDITION = StoppingCondition( + max_runtime_in_seconds=3600, + max_pending_time_in_seconds=None, + max_wait_time_in_seconds=None, +) +DEFAULT_SOURCE_CODE_CONFIG = SourceCodeConfig( + source_dir="test-data", + entry_point="train.py", +) +UNSUPPORTED_SOURCE_CODE_CONFIG = SourceCodeConfig( + entry_script="train.py", +) + + +@pytest.fixture(scope="module", autouse=True) +def modules_session(): + with patch("sagemaker.session.Session", spec=Session) as session_mock: + session_instance = session_mock.return_value + session_instance.default_bucket.return_value = DEFAULT_BUCKET + session_instance.get_caller_identity_arn.return_value = DEFAULT_ROLE + yield session_instance + + +@pytest.fixture +def model_trainer(): + trainer = ModelTrainer( + training_image=DEFAULT_IMAGE, + role=DEFAULT_ROLE, + resource_config=MagicMock(spec=ResourceConfig), + stopping_condition=MagicMock(spec=StoppingCondition), + output_data_config=MagicMock(spec=OutputDataConfig), + ) + return trainer + + +@pytest.mark.parametrize( + "test_case", + [ + { + "init_params": {}, + "should_throw": True, + }, + { + "init_params": { + "training_image": DEFAULT_IMAGE, + }, + "should_throw": False, + }, + { + "init_params": { + "training_image": DEFAULT_IMAGE, + "algorithm_name": "dummy-arn", + }, + "should_throw": True, + }, + { + "init_params": { + "training_image": DEFAULT_IMAGE, + "source_code_config": UNSUPPORTED_SOURCE_CODE_CONFIG, + }, + "should_throw": True, + }, + { + "init_params": { + "training_image": DEFAULT_IMAGE, + "source_code_config": DEFAULT_SOURCE_CODE_CONFIG, + }, + "should_throw": False, + }, + ], + ids=[ + "no_params", + "training_image_and_algorithm_name", + "only_training_image", + "unsupported_source_code_config", + "supported_source_code_config", + ], +) +def test_model_trainer_param_validation(test_case, modules_session): + if test_case["should_throw"]: + with pytest.raises(ValueError): + ModelTrainer(**test_case["init_params"], session=modules_session) + else: + trainer = ModelTrainer(**test_case["init_params"], session=modules_session) + assert trainer is not None + assert trainer.training_image == DEFAULT_IMAGE + assert trainer.resource_config == DEFAULT_RESOURCE_CONFIG + assert trainer.output_data_config == DEFAULT_OUTPUT_DATA_CONFIG + assert trainer.stopping_condition == DEFAULT_STOPPING_CONDITION + assert trainer.base_name == DEFAULT_BASE_NAME + + +@patch("sagemaker.modules.train.model_trainer.TrainingJob") +def test_train_with_default_params(mock_training_job, model_trainer): + model_trainer.train() + + mock_training_job.create.assert_called_once() + + training_job_instance = mock_training_job.create.return_value + training_job_instance.wait.assert_called_once_with(logs=True) + + +@patch("sagemaker.modules.train.model_trainer.TrainingJob") +@patch.object(ModelTrainer, "_get_input_data_config") +def test_train_with_input_data_channels(mock_get_input_config, mock_training_job, model_trainer): + mock_input_data_channels = {"train": "train/dir", "test": "test/dir"} + + model_trainer.train(input_data_channels=mock_input_data_channels) + + mock_get_input_config.assert_called_once_with(mock_input_data_channels) + mock_training_job.create.assert_called_once() + + +@pytest.mark.parametrize( + "source_code_config", + [ + None, + SourceCodeConfig( + source_dir="test-data", + entry_script="train.py", + ), + SourceCodeConfig( + command="python train.py", + ), + ], + ids=[ + "no_source_code_config", + "supported_source_code_config", + "unsupported_source_code_config", + ], +) +@patch("sagemaker.modules.train.model_trainer.TrainingJob") +@patch.object(ModelTrainer, "create_input_data_channel") +@patch.object(ModelTrainer, "_validate_source_code_config") +def test_train_with_source_code_config( + mock_validate_source_code_config, + mock_create_input_data_channel, + mock_training_job, + model_trainer, + source_code_config, +): + mock_validate_source_code_config.return_value = None + mock_create_input_data_channel.return_value = None + + model_trainer.train(source_code_config=source_code_config) + + input_channel_calls = [] + if source_code_config: + if source_code_config.source_dir: + input_channel_calls.append(call("code", source_code_config.source_dir)) + mock_validate_source_code_config.assert_called_once_with(source_code_config) + input_channel_calls.append(call("sm_code", SM_CODE_LOCAL_PATH)) + else: + mock_validate_source_code_config.assert_not_called() + mock_create_input_data_channel.assert_not_called() + + mock_create_input_data_channel.assert_has_calls(input_channel_calls) + mock_training_job.create.assert_called_once() + + +@pytest.mark.parametrize( + "test_case", + [ + { + "channel_name": "test", + "data_source": DATA_DIR, + "valid": True, + }, + { + "channel_name": "test", + "data_source": f"s3://{DEFAULT_BUCKET}/{DEFAULT_BASE_NAME}-job/input/test", + "valid": True, + }, + { + "channel_name": "test", + "data_source": S3DataSource( + s3_data_type="S3Prefix", + s3_uri=f"s3://{DEFAULT_BUCKET}/{DEFAULT_BASE_NAME}-job/input/test", + s3_data_distribution_type="FullyReplicated", + ), + "valid": True, + }, + { + "channel_name": "test", + "data_source": FileSystemDataSource( + file_system_id="fs-000000000000", + file_system_access_mode="ro", + file_system_type="EFS", + directory_path="/data/test", + ), + "valid": True, + }, + { + "channel_name": "test", + "data_source": "fake/path", + "valid": False, + }, + ], + ids=[ + "valid_local_path", + "valid_s3_path", + "valid_s3_data_source", + "valid_file_system_data_source", + "invalid_path", + ], +) +@patch("sagemaker.modules.train.model_trainer.Session.upload_data") +def test_create_input_data_channel(mock_upload_data, model_trainer, test_case): + expected_s3_uri = f"s3://{DEFAULT_BUCKET}/{DEFAULT_BASE_NAME}-job/input/test" + mock_upload_data.return_value = expected_s3_uri + + if not test_case["valid"]: + with pytest.raises(ValueError): + model_trainer.create_input_data_channel( + test_case["channel_name"], test_case["data_source"] + ) + else: + channel = model_trainer.create_input_data_channel( + test_case["channel_name"], test_case["data_source"] + ) + assert channel.channel_name == test_case["channel_name"] + + if isinstance(test_case["data_source"], S3DataSource): + assert channel.data_source.s3_data_source == test_case["data_source"] + elif isinstance(test_case["data_source"], FileSystemDataSource): + assert channel.data_source.file_system_data_source == test_case["data_source"] + else: + assert channel.data_source.s3_data_source.s3_uri == expected_s3_uri + + +@pytest.mark.parametrize( + "test_case", + [ + { + "source_code_config": SourceCodeConfig( + source_dir="test-data", + entry_script="train.py", + ), + "expected_command": "python train.py", + "should_throw": False, + }, + { + "source_code_config": SourceCodeConfig( + command="python train.py", + ), + "expected_command": "python train.py", + "should_throw": False, + }, + { + "source_code_config": SourceCodeConfig( + source_dir="test-data", + entry_script="other.py", + command="python train.py", + ), + "expected_command": "python train.py", + "should_throw": False, + }, + { + "source_code_config": SourceCodeConfig(source_dir="test-data", entry_script="train.sh"), + "expected_command": "chmod +x train.sh && bash train.sh", + "should_throw": False, + }, + { + "source_code_config": SourceCodeConfig( + source_dir="test-data", + entry_script="train.ts", + ), + "should_throw": True, + }, + { + "source_code_config": SourceCodeConfig( + source_dir="test-data", + ), + "should_throw": True, + }, + ], +) +def test_get_script_mode_command(model_trainer, test_case): + if test_case["should_throw"]: + with pytest.raises((ValueError, AssertionError)): + model_trainer._get_script_mode_command(test_case["source_code_config"]) + else: + command = model_trainer._get_script_mode_command(test_case["source_code_config"]) + assert command == test_case["expected_command"] From 7446b09a0715945ae159dc36a28577855c8a456b Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Mon, 7 Oct 2024 11:37:56 -0700 Subject: [PATCH 05/79] Add example notebook (#1528) * Add testing notebook * format * use smaller data * remove large dataset * update * pylint * flake8 * ignore docstyle in directories with test * format * format --- .pydocstylerc | 1 + src/sagemaker/modules/image_spec.py | 164 ++++---- .../base_model_trainer.ipynb | 369 ++++++++++++++++- .../basic-script-mode/custom_script.py | 166 ++++++++ .../basic-script-mode/data/test/x_test.npy | Bin 0 -> 19456 bytes .../basic-script-mode/data/test/y_test.npy | Bin 0 -> 2544 bytes .../basic-script-mode/data/train/x_train.npy | Bin 0 -> 77184 bytes .../basic-script-mode/data/train/y_train.npy | Bin 0 -> 9760 bytes .../basic-script-mode/pytorch_model_def.py | 23 ++ .../basic-script-mode/requirements.txt | 3 + .../scripts/merge_peft_adapters.py | 46 +++ .../scripts/requirements.txt | 5 + .../scripts/run_clm_no_trainer.py | 372 ++++++++++++++++++ .../distributed-training/scripts/utils.py | 85 ++++ src/sagemaker/modules/train/model_trainer.py | 15 +- .../unit/sagemaker/modules/test_image_spec.py | 19 +- 16 files changed, 1176 insertions(+), 92 deletions(-) create mode 100644 src/sagemaker/modules/testing_notebooks/basic-script-mode/custom_script.py create mode 100644 src/sagemaker/modules/testing_notebooks/basic-script-mode/data/test/x_test.npy create mode 100644 src/sagemaker/modules/testing_notebooks/basic-script-mode/data/test/y_test.npy create mode 100644 src/sagemaker/modules/testing_notebooks/basic-script-mode/data/train/x_train.npy create mode 100644 src/sagemaker/modules/testing_notebooks/basic-script-mode/data/train/y_train.npy create mode 100644 src/sagemaker/modules/testing_notebooks/basic-script-mode/pytorch_model_def.py create mode 100644 src/sagemaker/modules/testing_notebooks/basic-script-mode/requirements.txt create mode 100644 src/sagemaker/modules/testing_notebooks/distributed-training/scripts/merge_peft_adapters.py create mode 100644 src/sagemaker/modules/testing_notebooks/distributed-training/scripts/requirements.txt create mode 100644 src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py create mode 100644 src/sagemaker/modules/testing_notebooks/distributed-training/scripts/utils.py diff --git a/.pydocstylerc b/.pydocstylerc index a5083c0d63..9ed879a760 100644 --- a/.pydocstylerc +++ b/.pydocstylerc @@ -2,3 +2,4 @@ inherit = false ignore = D104,D107,D202,D203,D213,D214,D400,D401,D404,D406,D407,D411,D413,D414,D415,D417 match = (?!record_pb2).*\.py +match-dir = (?!.*test).* \ No newline at end of file diff --git a/src/sagemaker/modules/image_spec.py b/src/sagemaker/modules/image_spec.py index 775c7e691f..60b417aaac 100644 --- a/src/sagemaker/modules/image_spec.py +++ b/src/sagemaker/modules/image_spec.py @@ -16,17 +16,28 @@ import re from enum import Enum from typing import Optional +from packaging.version import Version from sagemaker import utils -from sagemaker.image_uris import _validate_version_and_set_if_needed, _version_for_config, \ - _config_for_framework_and_scope, _validate_py_version_and_set_if_needed, _registry_from_region, ECR_URI_TEMPLATE, \ - _get_latest_versions, _validate_instance_deprecation, _get_image_tag, _validate_arg -from packaging.version import Version +from sagemaker.image_uris import ( + _validate_version_and_set_if_needed, + _version_for_config, + _config_for_framework_and_scope, + _validate_py_version_and_set_if_needed, + _registry_from_region, + ECR_URI_TEMPLATE, + _get_latest_versions, + _validate_instance_deprecation, + _get_image_tag, + _validate_arg, +) DEFAULT_TOLERATE_MODEL = False class Framework(Enum): + """Framework enum class.""" + HUGGING_FACE = "huggingface" HUGGING_FACE_NEURON = "huggingface-neuron" HUGGING_FACE_NEURON_X = "huggingface-neuronx" @@ -46,12 +57,16 @@ class Framework(Enum): class ImageScope(Enum): + """ImageScope enum class.""" + TRAINING = "training" INFERENCE = "inference" INFERENCE_GRAVITON = "inference-graviton" class Processor(Enum): + """Processor enum class.""" + INF = "inf" NEURON = "neuron" GPU = "gpu" @@ -60,22 +75,53 @@ class Processor(Enum): class ImageSpec: - """ImageSpec class to get image URI for a specific framework version.""" - - def __init__(self, - framework: Framework, - processor: Optional[Processor] = Processor.CPU, - region: Optional[str] = "us-west-2", - version=None, - py_version=None, - instance_type=None, - accelerator_type=None, - image_scope: ImageScope = ImageScope.TRAINING, - container_version=None, - distribution=None, - base_framework_version=None, - sdk_version=None, - inference_tool=None): + """ImageSpec class to get image URI for a specific framework version. + + Attributes: + framework (Framework): The name of the framework or algorithm. + processor (Processor): The name of the processor (CPU, GPU, etc.). + region (str): The AWS region. + version (str): The framework or algorithm version. This is required if there is + more than one supported version for the given framework or algorithm. + py_version (str): The Python version. This is required if there is + more than one supported Python version for the given framework version. + instance_type (str): The SageMaker instance type. For supported types, see + https://aws.amazon.com/sagemaker/pricing. This is required if + there are different images for different processor types. + accelerator_type (str): Elastic Inference accelerator type. For more, see + https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html. + image_scope (str): The image type, i.e. what it is used for. + Valid values: "training", "inference", "inference_graviton", "eia". + If ``accelerator_type`` is set, ``image_scope`` is ignored. + container_version (str): the version of docker image. + Ideally the value of parameter should be created inside the framework. + For custom use, see the list of supported container versions: + https://github.com/aws/deep-learning-containers/blob/master/available_images.md + (default: None). + distribution (dict): A dictionary with information on how to run distributed training + sdk_version (str): the version of python-sdk that will be used in the image retrieval. + (default: None). + inference_tool (str): the tool that will be used to aid in the inference. + Valid values: "neuron, neuronx, None" + (default: None). + """ + + def __init__( + self, + framework: Framework, + processor: Optional[Processor] = Processor.CPU, + region: Optional[str] = "us-west-2", + version=None, + py_version=None, + instance_type=None, + accelerator_type=None, + image_scope: ImageScope = ImageScope.TRAINING, + container_version=None, + distribution=None, + base_framework_version=None, + sdk_version=None, + inference_tool=None, + ): self.framework = framework self.processor = processor self.version = version @@ -91,6 +137,7 @@ def __init__(self, self.inference_tool = inference_tool def update_image_spec(self, **kwargs): + """Update the ImageSpec object with the given arguments.""" for key, value in kwargs.items(): if hasattr(self, key): setattr(self, key, value) @@ -98,37 +145,6 @@ def update_image_spec(self, **kwargs): def retrieve(self) -> str: """Retrieves the ECR URI for the Docker image matching the given arguments. - Ideally this function should not be called directly, rather it should be called from the - fit() function inside framework estimator. - - Args: - framework (Framework): The name of the framework or algorithm. - processor (Processor): The name of the processor (CPU, GPU, etc.). - region (str): The AWS region. - version (str): The framework or algorithm version. This is required if there is - more than one supported version for the given framework or algorithm. - py_version (str): The Python version. This is required if there is - more than one supported Python version for the given framework version. - instance_type (str): The SageMaker instance type. For supported types, see - https://aws.amazon.com/sagemaker/pricing. This is required if - there are different images for different processor types. - accelerator_type (str): Elastic Inference accelerator type. For more, see - https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html. - image_scope (str): The image type, i.e. what it is used for. - Valid values: "training", "inference", "inference_graviton", "eia". - If ``accelerator_type`` is set, ``image_scope`` is ignored. - container_version (str): the version of docker image. - Ideally the value of parameter should be created inside the framework. - For custom use, see the list of supported container versions: - https://github.com/aws/deep-learning-containers/blob/master/available_images.md - (default: None). - distribution (dict): A dictionary with information on how to run distributed training - sdk_version (str): the version of python-sdk that will be used in the image retrieval. - (default: None). - inference_tool (str): the tool that will be used to aid in the inference. - Valid values: "neuron, neuronx, None" - (default: None). - Returns: str: The ECR URI for the corresponding SageMaker Docker image. @@ -140,13 +156,14 @@ def retrieve(self) -> str: known security vulnerabilities. DeprecatedJumpStartModelError: If the version of the model is deprecated. """ - config = _config_for_framework_and_scope(self.framework.value, - self.image_scope.value, - self.accelerator_type) - + config = _config_for_framework_and_scope( + self.framework.value, self.image_scope.value, self.accelerator_type + ) original_version = self.version try: - version = _validate_version_and_set_if_needed(self.version, config, self.framework.value) + version = _validate_version_and_set_if_needed( + self.version, config, self.framework.value + ) except ValueError: version = None if not version: @@ -159,12 +176,14 @@ def retrieve(self) -> str: full_base_framework_version = version_config["version_aliases"].get( self.base_framework_version, self.base_framework_version ) - _validate_arg(full_base_framework_version, list(version_config.keys()), "base framework") + _validate_arg( + full_base_framework_version, list(version_config.keys()), "base framework" + ) version_config = version_config.get(full_base_framework_version) - self.py_version = _validate_py_version_and_set_if_needed(self.py_version, - version_config, - self.framework.value) + self.py_version = _validate_py_version_and_set_if_needed( + self.py_version, version_config, self.framework.value + ) version_config = version_config.get(self.py_version) or version_config registry = _registry_from_region(self.region, version_config["registries"]) @@ -206,16 +225,18 @@ def retrieve(self) -> str: if config.get("version_aliases").get(original_version): _version = config.get("version_aliases")[original_version] if ( - config.get("versions", {}) - .get(_version, {}) - .get("version_aliases", {}) - .get(self.base_framework_version, {}) + config.get("versions", {}) + .get(_version, {}) + .get("version_aliases", {}) + .get(self.base_framework_version, {}) ): _base_framework_version = config.get("versions")[_version]["version_aliases"][ self.base_framework_version ] pt_or_tf_version = ( - re.compile("^(pytorch|tensorflow)(.*)$").match(_base_framework_version).group(2) + re.compile("^(pytorch|tensorflow)(.*)$") + .match(_base_framework_version) + .group(2) ) tag_prefix = f"{pt_or_tf_version}-transformers{_version}" @@ -224,29 +245,28 @@ def retrieve(self) -> str: if repo == f"{self.framework.value}-inference-graviton": self.container_version = f"{self.container_version}-sagemaker" - _validate_instance_deprecation(self.framework, - self.instance_type, - version) + _validate_instance_deprecation(self.framework, self.instance_type, version) tag = _get_image_tag( self.container_version, self.distribution, self.image_scope.value, - self.framework, + self.framework.value, self.inference_tool, self.instance_type, self.processor.value, self.py_version, tag_prefix, - version) + version, + ) if tag: repo += ":{}".format(tag) return ECR_URI_TEMPLATE.format(registry=registry, hostname=hostname, repository=repo) - def _fetch_latest_version_from_config(self, - framework_config: dict) -> str: + def _fetch_latest_version_from_config(self, framework_config: dict) -> str: + """Fetches the latest version from the framework config.""" if self.image_scope.value in framework_config: if image_scope_config := framework_config[self.image_scope.value]: if version_aliases := image_scope_config["version_aliases"]: diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index 0b832d540e..58c13f7084 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -12,6 +12,17 @@ "sys.path.insert(0, root_dir)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Case Minimally Setup ModelTrainer and Execute Commands\n", + "\n", + "1. No longer have an issue about API Limit\n", + "2. Maintain contract with container (no secret handshake)\n", + "3. Decouple Training Toolkit" + ] + }, { "cell_type": "code", "execution_count": null, @@ -19,17 +30,12 @@ "outputs": [], "source": [ "from sagemaker.modules.train.model_trainer import ModelTrainer\n", - "from sagemaker.modules.image_spec import ImageSpec\n", + "from sagemaker.modules.image_spec import ImageSpec, Framework\n", "\n", "pytorch_image = ImageSpec(\n", - " framework_name=\"pytorch\",\n", - " version=\"1.13.1\",\n", - " py_version=\"py39\"\n", + " framework=Framework.PYTORCH\n", ")\n", "\n", - "python_ecr_image = \"public.ecr.aws/docker/library/python:3.10.15-slim\"\n", - "python_docker_image = \"python:3.10.15-slim\"\n", - "\n", "model_trainer = ModelTrainer(training_image=pytorch_image)\n" ] }, @@ -47,12 +53,359 @@ "model_trainer.train(source_code_config=source_code_config)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Script Mode Case - 1\n", + "\n", + "1. No more code smell of user having to use /opt/ml/input/data/code/custom_script.py\n", + "```python\n", + "source_code_config = SourceCodeConfig(\n", + " source_dir=\"script-mode-basic\",\n", + " command=\"python /opt/ml/input/data/code/custom_script.py\",\n", + ")\n", + "```\n", + "\n", + "2. No more risk of API Limit being reached" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.configs import SourceCodeConfig\n", + "\n", + "source_code_config = SourceCodeConfig(\n", + " source_dir=\"script-mode-basic\",\n", + " command=\"python custom_script.py\",\n", + ")\n", + "model_trainer.train(\n", + " source_code_config=source_code_config,\n", + " input_data_channels={\n", + " \"train\": \"script-mode-basic/data/train\",\n", + " \"test\": \"script-mode-basic/data/test\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Script Mode Case - 2\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.configs import SourceCodeConfig\n", + "\n", + "source_code_config = SourceCodeConfig(\n", + " source_dir=\"script-mode-basic\",\n", + " requirements=\"requirements.txt\",\n", + " entry_script=\"custom_script.py\",\n", + ")\n", + "model_trainer.train(\n", + " source_code_config=source_code_config,\n", + " input_data_channels={\n", + " \"train\": \"script-mode-basic/data/train\",\n", + " \"test\": \"script-mode-basic/data/test\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Distributed Training Case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"datasets[s3]\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "access_token = os.environ.get(\"HF_TOKEN\", \"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\")\n", + "model_id = \"meta-llama/Llama-2-7b-hf\"\n", + "\n", + "dataset_name = \"tatsu-lab/alpaca\"\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load Data Set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "from transformers import AutoTokenizer \n", + "\n", + "from huggingface_hub.hf_api import HfFolder;\n", + "\n", + "# Load Tokenizer \n", + "tokenizer = AutoTokenizer.from_pretrained(model_id, access_token=access_token)\n", + "\n", + "\n", + "# Load dataset from huggingface.co\n", + "dataset = load_dataset(dataset_name)\n", + "\n", + "dataset = dataset.shuffle(seed=42)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if \"validation\" not in dataset.keys():\n", + " dataset[\"validation\"] = load_dataset(\n", + " dataset_name,\n", + " split=\"train[:1%]\"\n", + " )\n", + "\n", + " dataset[\"train\"] = load_dataset(\n", + " dataset_name,\n", + " split=\"train[1%:]\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prepare Dataset" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from itertools import chain\n", + "from functools import partial\n", + "\n", + "def group_texts(examples,block_size = 2048):\n", + " # Concatenate all texts.\n", + " concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}\n", + " total_length = len(concatenated_examples[list(examples.keys())[0]])\n", + " # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n", + " # customize this part to your needs.\n", + " if total_length >= block_size:\n", + " total_length = (total_length // block_size) * block_size\n", + " # Split by chunks of max_len.\n", + " result = {\n", + " k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n", + " for k, t in concatenated_examples.items()\n", + " }\n", + " result[\"labels\"] = result[\"input_ids\"].copy()\n", + " return result\n", + "\n", + "column_names = dataset[\"train\"].column_names\n", + "\n", + "lm_dataset = dataset.map(\n", + " lambda sample: tokenizer(sample[\"text\"],return_token_type_ids=False), batched=True, remove_columns=list(column_names)\n", + ").map(\n", + " partial(group_texts, block_size=2048),\n", + " batched=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Save Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#save data locally\n", + "\n", + "training_input_path = f'distributed-training/processed/data/'\n", + "lm_dataset.save_to_disk(training_input_path)\n", + "\n", + "print(f\"Saved data to: {training_input_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Model Trainer Torchrun" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.train.model_trainer import ModelTrainer\n", + "from sagemaker.modules.image_spec import ImageSpec, Framework, Processor\n", + "from sagemaker.modules.configs import ResourceConfig\n", + "\n", + "env = {}\n", + "env['FI_PROVIDER'] = 'efa'\n", + "env['NCCL_PROTO'] = 'simple'\n", + "env['NCCL_SOCKET_IFNAME'] = \"eth0\"\n", + "env['NCCL_IB_DISABLE'] = \"1\"\n", + "env['NCCL_DEBUG'] = \"WARN\"\n", + "env['FI_EFA_USE_DEVICE_RDMA'] = '1'\n", + "env['RDMAV_FORK_SAFE'] = '1'\n", + "\n", + "\n", + "resource_config = ResourceConfig(\n", + " instance_count=2,\n", + " instance_type=\"ml.p4d.24xlarge\" ,\n", + " volume_size_in_gb=96,\n", + ")\n", + "\n", + "hugging_face_image = ImageSpec(\n", + " framework=Framework.HUGGING_FACE,\n", + " processor=Processor.GPU,\n", + " version=\"4.28.1\",\n", + " base_framework_version=\"pytorch2.0.0\",\n", + " py_version=\"py310\",\n", + " distribution={\"torch_distributed\": {\"enabled\": True}}\n", + ")\n", + "\n", + "model_trainer = ModelTrainer(\n", + " training_image=hugging_face_image,\n", + " environment=env,\n", + " resource_config=resource_config\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.configs import SourceCodeConfig\n", + "\n", + "\n", + "hyper_parameters={\n", + " 'dataset_path': '/opt/ml/input/data/dataset',\n", + " 'epochs': 1,\n", + " 'max_steps':100,\n", + " 'fsdp': '\"full_shard auto_wrap\"',\n", + " 'fsdp_transformer_layer_cls_to_wrap': \"LlamaDecoderLayer\",\n", + " 'gradient_checkpointing': True,\n", + " 'optimizer': \"adamw_torch\",\n", + " 'per_device_train_batch_size': 1,\n", + " 'model_id': model_id,\n", + " 'access_token': access_token\n", + "}\n", + "\n", + "source_code_config = SourceCodeConfig(\n", + " source_dir=\"distributed-training/scripts\",\n", + " requirements=\"requirements.txt\",\n", + " command=\"torchrun --nnodes 2 \\\n", + " --nproc_per_node $SM_NUM_GPUS \\\n", + " --master_addr algo-1 \\\n", + " --master_port 7777 \\\n", + " --node_rank $SM_CURRENT_HOST_INDEX \\\n", + " run_clm_no_trainer.py\",\n", + ")\n", + "\n", + "model_trainer.train(\n", + " source_code_config=source_code_config,\n", + " hyper_parameters=hyper_parameters,\n", + " input_data_channels={\"dataset\": training_input_path},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Problems to Solve\n", + "\n", + "### How can we make it easier for user to set a sagemaker distributed image?\n", + "* Would be very nice if user could just provide framework name and we somehow automatically pick latest and resolve some things from ModelTrainer. Many low level technical challanges with this due to legacy image uri configs and conditions that are not predictable from the the `json` (ie, hardcoded prefixes/substrings used for certain specific conditions when building image)\n", + "\n", + "```python\n", + "pytorch_image = ImageSpec(\"pytorch\")\n", + "```\n", + "\n", + "---\n", + "\n", + "\n", + "### How can we make it easier for user to build an image?\n", + "* There are challenges here because of some of the OS differences in installing some ML frameworks which depend on some lower level dependencies being installed like CUDA - https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Amazon-Linux&target_version=2023&target_type=rpm_network\n", + "\n", + "```python\n", + "custom_image = custom_image_builder(\n", + " image_name=\"my_custom_image\",\n", + " requirements=requirements.txt,\n", + " base_image=\"python:3.10.15-slim\",\n", + " docker_file=\"Dockerfile\",\n", + " enviornment={\"SOME_VAR\": 4},\n", + " upload_ecr=True,\n", + " local_snapshot=True,\n", + ")\n", + "\n", + "model_trainer = ModelTrainer(training_image=custom_image)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.train.model_trainer import ModelTrainer\n", + "\n", + "python_ecr_image = \"public.ecr.aws/docker/library/python:3.10.15-slim\"\n", + "python_docker_image = \"python:3.10.15-slim\"\n", + "\n", + "# This does not work\n", + "model_trainer = ModelTrainer(training_image=python_ecr_image)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "source_code_config = SourceCodeConfig(\n", + " command=\"echo 'Hello World'\",\n", + ")\n", + "model_trainer.train(source_code_config=source_code_config)" + ] } ], "metadata": { diff --git a/src/sagemaker/modules/testing_notebooks/basic-script-mode/custom_script.py b/src/sagemaker/modules/testing_notebooks/basic-script-mode/custom_script.py new file mode 100644 index 0000000000..8b8fdb9ec4 --- /dev/null +++ b/src/sagemaker/modules/testing_notebooks/basic-script-mode/custom_script.py @@ -0,0 +1,166 @@ +# flake8: noqa +import argparse +import numpy as np +import os +import sys +import logging +import json +import shutil +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, TensorDataset +from pytorch_model_def import get_model + + +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) +logger.addHandler(logging.StreamHandler(sys.stdout)) + + +def parse_args(): + """ + Parse arguments passed from the SageMaker API + to the container + """ + + parser = argparse.ArgumentParser() + + # Hyperparameters sent by the client are passed as command-line arguments to the script + parser.add_argument("--epochs", type=int, default=os.environ.get("SM_HP_EPOCHS", 1)) + parser.add_argument("--batch_size", type=int, default=os.environ.get("SM_HP_BATCH_SIZE", 64)) + parser.add_argument( + "--learning_rate", type=float, default=os.environ.get("SM_HP_LEARNING_RATE", 0.1) + ) + + # Data directories + parser.add_argument( + "--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN", "data/train") + ) + parser.add_argument("--test", type=str, default=os.environ.get("SM_CHANNEL_TEST", "data/test")) + + # Model directory: we will use the default set by SageMaker, /opt/ml/model/ + parser.add_argument("--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR")) + + return parser.parse_known_args() + + +def get_train_data(train_dir): + """ + Get the training data and convert to tensors + """ + + x_train = np.load(os.path.join(train_dir, "x_train.npy")) + y_train = np.load(os.path.join(train_dir, "y_train.npy")) + logger.info(f"x train: {x_train.shape}, y train: {y_train.shape}") + + return torch.from_numpy(x_train), torch.from_numpy(y_train) + + +def get_test_data(test_dir): + """ + Get the testing data and convert to tensors + """ + + x_test = np.load(os.path.join(test_dir, "x_test.npy")) + y_test = np.load(os.path.join(test_dir, "y_test.npy")) + logger.info(f"x test: {x_test.shape}, y test: {y_test.shape}") + + return torch.from_numpy(x_test), torch.from_numpy(y_test) + + +def model_fn(model_dir): + """ + Load the model for inference + """ + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = get_model() + model.load_state_dict(torch.load(model_dir + "/model.pth")) + model.eval() + return model.to(device) + + +def input_fn(request_body, request_content_type): + """ + Deserialize and prepare the prediction input + """ + + if request_content_type == "application/json": + request = json.loads(request_body) + train_inputs = torch.tensor(request) + return train_inputs + + +def predict_fn(input_data, model): + """ + Apply model to the incoming request + """ + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device) + model.eval() + with torch.no_grad(): + return model(input_data.float()).numpy()[0] + + +def train(): + """ + Train the PyTorch model + """ + + x_train, y_train = get_train_data(args.train) + x_test, y_test = get_test_data(args.test) + train_ds = TensorDataset(x_train, y_train) + + batch_size = args.batch_size + epochs = args.epochs + learning_rate = args.learning_rate + logger.info( + "batch_size = {}, epochs = {}, learning rate = {}".format(batch_size, epochs, learning_rate) + ) + + train_dl = DataLoader(train_ds, batch_size, shuffle=True) + + model = get_model() + model = model.to(device) + criterion = nn.MSELoss() + optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) + + for epoch in range(epochs): + for x_train_batch, y_train_batch in train_dl: + y = model(x_train_batch.float()) + loss = criterion(y.flatten(), y_train_batch.float()) + optimizer.zero_grad() + loss.backward() + optimizer.step() + epoch += 1 + logger.info(f"epoch: {epoch} -> loss: {loss}") + + # evalutate on test set + with torch.no_grad(): + y = model(x_test.float()).flatten() + mse = ((y - y_test) ** 2).sum() / y_test.shape[0] + print("\nTest MSE:", mse.numpy()) + + torch.save(model.state_dict(), args.model_dir + "/model.pth") + # PyTorch requires that the inference script must + # be in the .tar.gz model file and Step Functions SDK doesn't do this. + inference_code_path = args.model_dir + "/code/" + + if not os.path.exists(inference_code_path): + os.mkdir(inference_code_path) + logger.info("Created a folder at {}!".format(inference_code_path)) + + code_dir = os.environ.get("SM_CHANNEL_CODE") + shutil.copy(os.path.join(code_dir, "custom_script.py"), inference_code_path) + shutil.copy(os.path.join(code_dir, "pytorch_model_def.py"), inference_code_path) + logger.info("Saving models files to {}".format(inference_code_path)) + + +if __name__ == "__main__": + print("Running in the training container ...\n") + + args, _ = parse_args() + device = torch.device("cuda" if 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zuMEqNhg5ur+n{$z?c1}ODba)-%vTkbexM?k`r}jen)hEoJ=n-w^Y1-(mtr>6aAjk4 zSbw0C`fd%v9p;lVZJ=ib*g!p&^G)=0.21 +bitsandbytes +peft diff --git a/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py new file mode 100644 index 0000000000..48da810353 --- /dev/null +++ b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py @@ -0,0 +1,372 @@ +# flake8: noqa +import os +import argparse +import math +from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed, get_scheduler, SchedulerType +from datasets import load_from_disk +import torch +from utils import create_dataloaders, get_module_class_from_name, save_model +import time +from tqdm import tqdm +import torch.distributed as dist +from torch.distributed.fsdp import ( + FullyShardedDataParallel as FSDP, + MixedPrecision, + ShardingStrategy, + BackwardPrefetch, + CPUOffload, +) +from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( + checkpoint_wrapper, + CheckpointImpl, + apply_activation_checkpointing, +) + +from torch.distributed.fsdp.wrap import ( + transformer_auto_wrap_policy, +) +import functools + + +# Initialize the distributed environment +dist.init_process_group(backend="nccl") + + +def parse_arge(): + """Parse the arguments.""" + parser = argparse.ArgumentParser() + # add model id and dataset path argument + parser.add_argument( + "--model_id", + type=str, + default=os.environ.get("SM_HP_MODEL_ID", "google/flan-t5-xl"), + help="Model id to use for training.", + ) + parser.add_argument( + "--dataset_path", + type=str, + default=os.environ.get("SM_CHANNEL_DATASET", "lm_dataset"), + help="Path to dataset.", + ) + # add training hyperparameters for epochs, batch size, learning rate, and seed + parser.add_argument( + "--epochs", + type=int, + default=os.environ.get("SM_HP_EPOCHS", 1), + help="Number of epochs to train for.", + ) + parser.add_argument( + "--max_steps", + type=int, + default=os.environ.get("SM_HP_MAX_STEPS", None), + help="Number of epochs to train for.", + ) + parser.add_argument( + "--per_device_train_batch_size", + type=int, + default=os.environ.get("SM_HP_PER_DEVICE_TRAIN_BATCH_SIZE", 1), + help="Batch size to use for training.", + ) + parser.add_argument( + "--lr", + type=float, + default=os.environ.get("SM_HP_LR", 3e-5), + help="Learning rate to use for training.", + ) + parser.add_argument( + "--optimizer", + type=str, + default=os.environ.get("SM_HP_OPTIMIZER", "adamw_hf"), + help="Learning rate to use for training.", + ) + parser.add_argument( + "--seed", + type=int, + default=os.environ.get("SM_HP_SEED", 42), + help="Seed to use for training.", + ) + parser.add_argument( + "--access_token", + type=str, + default=os.environ.get("SM_HP_ACCESS_TOKEN", None), + ) + parser.add_argument( + "--num_train_epochs", + type=int, + default=os.environ.get("SM_HP_NUM_TRAIN_EPOCHS", 1), + help="Total number of training epochs to perform.", + ) + + parser.add_argument( + "--gradient_checkpointing", + type=bool, + default=os.environ.get("SM_HP_GRADIENT_CHECKPOINTING", True), + help="Path to deepspeed config file.", + ) + parser.add_argument( + "--bf16", + type=bool, + default=True if torch.cuda.get_device_capability()[0] == 8 else False, + help="Whether to use bf16.", + ) + parser.add_argument( + "--fsdp", type=str, default=os.environ.get("SM_HP_FSDP", None), help="Whether to use fsdp." + ) + parser.add_argument( + "--fsdp_transformer_layer_cls_to_wrap", + type=str, + default=os.environ.get("SM_HP_FSDP_TRANSFORMER_LAYER_CLS_TO_WRAP", None), + help="Which transformer layer to wrap with fsdp.", + ) + parser.add_argument( + "--max_train_steps", + type=int, + default=os.environ.get("SM_HP_MAX_TRAIN_STEPS", None), + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=os.environ.get("SM_HP_LEARNING_RATE", 5e-5), + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=os.environ.get("SM_HP_GRADIENT_ACCUMULATION_STEPS", 1), + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--lr_scheduler_type", + type=SchedulerType, + default=os.environ.get("SM_HP_LR_SCHEDULER_TYPE", "linear"), + help="The scheduler type to use.", + choices=[ + "linear", + "cosine", + "cosine_with_restarts", + "polynomial", + "constant", + "constant_with_warmup", + ], + ) + parser.add_argument( + "--num_warmup_steps", + type=int, + default=os.environ.get("SM_HP_NUM_WARMUP_STEPS", 0), + help="Number of steps for the warmup in the lr scheduler.", + ) + parser.add_argument( + "--limit_all_gathers", + type=bool, + default=os.environ.get("SM_HP_LIMIT_ALL_GATHERS", False), + ) + parser.add_argument( + "--forward_prefetch", + type=bool, + default=os.environ.get("SM_HP_FORWARD_PREFETCH", False), + ) + parser.add_argument( + "--weight_decay", + type=float, + default=os.environ.get("SM_HP_WEIGHT_DECAY", 0.0), + help="Weight decay to use.", + ) + parser.add_argument( + "--model_dir", + type=str, + default=os.environ.get("SM_MODEL_DIR", "/opt/ml/model"), + ) + parser.add_argument( + "--cache_dir", + type=str, + default=os.environ.get("SM_HP_CACHE_DIR", None), + ) + + args = parser.parse_known_args() + return args + + +def training_function(args): + # set seed + set_seed(args.seed) + + from huggingface_hub.hf_api import HfFolder + + HfFolder.save_token(args.access_token) + + dataset = load_from_disk(args.dataset_path) + + dist.barrier() + + # load model from the hub + model = AutoModelForCausalLM.from_pretrained( + args.model_id, + # token=args.access_token, + cache_dir=args.cache_dir, + use_cache=( + False if args.gradient_checkpointing else True + ), # this is needed for gradient checkpointing + ) + + tokenizer = AutoTokenizer.from_pretrained(args.model_id) + + train_dataset = dataset["train"] + eval_dataset = dataset["validation"] + + # Create dataloaders for training and evaluation + train_dataloader, eval_dataloader = create_dataloaders( + train_dataset, + eval_dataset, + args.rank, + args.world_size, + args.seed, + args.per_device_train_batch_size, + args.per_device_train_batch_size, + ) + + auto_wrap_policy = functools.partial( + transformer_auto_wrap_policy, + transformer_layer_cls={ + get_module_class_from_name(model, args.fsdp_transformer_layer_cls_to_wrap) + }, + ) + + torch.cuda.set_device(args.local_rank) + + dtype = torch.bfloat16 + + mixed_precision_policy = MixedPrecision( + param_dtype=dtype, reduce_dtype=dtype, buffer_dtype=dtype + ) + + model = FSDP( + model, + auto_wrap_policy=auto_wrap_policy, + mixed_precision=mixed_precision_policy, + sharding_strategy=ShardingStrategy.FULL_SHARD, # SHARD_GRAD_OP #for Zero2 and FULL_SHARD for Zero3, + cpu_offload=CPUOffload(offload_params=True), + backward_prefetch=BackwardPrefetch.BACKWARD_PRE, # BACKWARD_POST, BACKWARD_PRE + forward_prefetch=args.forward_prefetch, + limit_all_gathers=args.limit_all_gathers, + device_id=torch.cuda.current_device(), + ) + + non_reentrant_wrapper = functools.partial( + checkpoint_wrapper, offload_to_cpu=True, checkpoint_impl=CheckpointImpl.NO_REENTRANT + ) + check_fn_gpt = lambda submodule: isinstance( + submodule, get_module_class_from_name(model, args.fsdp_transformer_layer_cls_to_wrap) + ) + apply_activation_checkpointing( + model, checkpoint_wrapper_fn=non_reentrant_wrapper, check_fn=check_fn_gpt + ) + + # Optimizer + # Split weights in two groups, one with weight decay and the other not. + no_decay = ["bias", "LayerNorm.weight", "layer_norm.weight"] + optimizer_grouped_parameters = [ + { + "params": [ + p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay) + ], + "weight_decay": args.weight_decay, + }, + { + "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], + "weight_decay": 0.0, + }, + ] + + optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if args.rank == 0: + print(f"Number of update steps per epoch {num_update_steps_per_epoch}") + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + name=args.lr_scheduler_type, + optimizer=optimizer, + num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + ) + + start = time.time() + device = torch.device(f"cuda:{args.local_rank}") + + # Perform Training Loop for num_train_epochs times + for epoch in range(args.num_train_epochs): + + model.train() + total_steps = 0 + fsdp_loss = torch.zeros(2).to(args.local_rank) + + # Use train_dataloader to get the batch data + for _, batch in enumerate(tqdm(train_dataloader, disable=not (args.rank == 0))): + + batch = {k: v.to(device) for k, v in batch.items()} + output = model(**batch) + loss = output["loss"] + loss.backward() + fsdp_loss[0] += loss.item() + fsdp_loss[1] += len(batch["input_ids"]) + + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + total_steps += 1 + if total_steps > args.max_steps: + break + + # Reduce the loss across all processes + torch.distributed.all_reduce(fsdp_loss, op=torch.distributed.ReduceOp.SUM) + train_loss = fsdp_loss[0] / fsdp_loss[1] + train_ppl = torch.exp(train_loss) + + if args.rank == 0: + print(f"******{epoch=}: {train_ppl=} {train_loss=}******") + + model.eval() + eval_loss = 0 + fsdp_eval_loss = torch.zeros(2).to(args.local_rank) + for steps, batch in enumerate(tqdm(eval_dataloader, disable=not (args.rank == 0))): + batch = {k: v.to(device) for k, v in batch.items()} + with torch.no_grad(): + outputs = model(**batch) + loss = outputs["loss"] + + fsdp_eval_loss[0] += loss.item() + fsdp_eval_loss[1] += len(batch["input_ids"]) + if steps > args.max_steps: + break + + torch.distributed.all_reduce(fsdp_eval_loss, op=torch.distributed.ReduceOp.SUM) + eval_loss = fsdp_eval_loss[0] / fsdp_eval_loss[1] + eval_ppl = torch.exp(eval_loss) + + if args.rank == 0: + print(f"*******{epoch=}: {eval_ppl=} {eval_loss=}*******") + + save_model(model, tokenizer, args.model_dir, args.rank) + if args.rank == 0: + print("Training done!") + dist.barrier() + + +def main(): + args, _ = parse_arge() + args.local_rank = int(os.environ["LOCAL_RANK"]) + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ["WORLD_SIZE"]) + + print(f"Running with args: {args}") + training_function(args) + + +if __name__ == "__main__": + main() diff --git a/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/utils.py b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/utils.py new file mode 100644 index 0000000000..6c25ea22a8 --- /dev/null +++ b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/utils.py @@ -0,0 +1,85 @@ +# flake8: noqa +import torch +from torch.utils.data import DataLoader +from transformers import default_data_collator +import os + + +def create_dataloaders( + train_dataset, eval_dataset, rank, world_size, seed, train_batch_size, eval_batch_size +): + + train_sampler = torch.utils.data.DistributedSampler( + train_dataset, + shuffle=True, + seed=seed, + rank=rank, + num_replicas=world_size, + drop_last=True, + ) + + eval_sampler = torch.utils.data.DistributedSampler( + eval_dataset, + shuffle=True, + seed=seed, + rank=rank, + num_replicas=world_size, + drop_last=True, + ) + + train_dataloader = DataLoader( + train_dataset, + sampler=train_sampler, + collate_fn=default_data_collator, + batch_size=train_batch_size, + pin_memory=True, + drop_last=True, + ) + + eval_dataloader = DataLoader( + eval_dataset, + sampler=eval_sampler, + collate_fn=default_data_collator, + batch_size=eval_batch_size, + pin_memory=True, + drop_last=True, + ) + + return train_dataloader, eval_dataloader + + +def save_model(model, tokenizer, output_dir, rank): + """Helper method to save model when using FSDP.""" + + from torch.distributed.fsdp import ( + FullyShardedDataParallel as FSDP, + FullStateDictConfig, + StateDictType, + ) + + save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) + with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, save_policy): + cpu_state_dict = model.state_dict() + if rank == 0: + torch.save(cpu_state_dict, os.path.join(output_dir, "model_weights.pt")) + tokenizer.save_pretrained(output_dir) + + +def get_module_class_from_name(module, name): + """ + Gets a class from a module by its name. + + Args: + module (`torch.nn.Module`): The module to get the class from. + name (`str`): The name of the class. + """ + modules_children = list(module.children()) + if module.__class__.__name__ == name: + return module.__class__ + elif len(modules_children) == 0: + return + else: + for child_module in modules_children: + module_class = get_module_class_from_name(child_module, name) + if module_class is not None: + return module_class diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 9df9f57b56..cb844f95c9 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -50,7 +50,10 @@ DEFAULT_CONTAINER_ARGUMENTS, ) from sagemaker.modules.templates import TRAIN_SCRIPT_TEMPLATE -from sagemaker.modules.image_spec import ImageSpec +from sagemaker.modules.image_spec import ( + ImageSpec, + ImageScope, +) from sagemaker.modules import logger @@ -157,7 +160,7 @@ def _validate_source_code_config(self, source_code_config: Optional[SourceCodeCo ) if requirements: if not _is_valid_path( - f"{source_dir}/{entry_script}", + f"{source_dir}/{requirements}", path_type="File", ): raise ValueError( @@ -195,7 +198,7 @@ def __init__(self, **data): self.base_name = f"{self.algorithm_name}-job" elif self.training_image: if isinstance(self.training_image, ImageSpec): - self.base_name = f"{self.training_image.framework_name}-job" + self.base_name = f"{self.training_image.framework.value}-job" else: self.base_name = f"{_get_repo_name_from_image(self.training_image)}-job" logger.warning(f"Base name not provided. Using default name:\n{self.base_name}") @@ -235,9 +238,11 @@ def __init__(self, **data): if self.training_image: if isinstance(self.training_image, ImageSpec): - self.training_image = self.training_image.get_image_uri( - image_scope="training", instance_type=self.resource_config.instance_type + self.training_image.update_image_spec( + image_scope=ImageScope.TRAINING, + instance_type=self.resource_config.instance_type, ) + self.training_image = self.training_image.retrieve() logger.info(f"Training image URI: {self.training_image}") def train( diff --git a/tests/unit/sagemaker/modules/test_image_spec.py b/tests/unit/sagemaker/modules/test_image_spec.py index 23d70f133c..a6bff4cb31 100644 --- a/tests/unit/sagemaker/modules/test_image_spec.py +++ b/tests/unit/sagemaker/modules/test_image_spec.py @@ -1,3 +1,4 @@ +from __future__ import absolute_import import unittest from sagemaker.modules.image_spec import ImageSpec, Framework, Processor @@ -7,7 +8,7 @@ class TestImageSpec(unittest.TestCase): def test_image_spec_update(self): image_spec = ImageSpec(framework=Framework.HUGGING_FACE) - assert image_spec.version == None + assert image_spec.version is None image_spec.update_image_spec(version="v3") assert image_spec.version == "v3" @@ -18,9 +19,11 @@ def test_image_spec_retrive(self): assert "dkr.ecr.us-west-2.amazonaws.com" in xgboost_uri assert "sagemaker-xgboost" in xgboost_uri - image_spec = ImageSpec(framework=Framework.HUGGING_FACE, - processor=Processor.GPU, - base_framework_version="pytorch2.1.0") + image_spec = ImageSpec( + framework=Framework.HUGGING_FACE, + processor=Processor.GPU, + base_framework_version="pytorch2.1.0", + ) hugging_face_uri = image_spec.retrieve() assert "dkr.ecr.us-west-2.amazonaws.com" in hugging_face_uri assert "huggingface-pytorch-training" in hugging_face_uri @@ -36,7 +39,9 @@ def test_image_spec_retrive(self): assert "sagemaker-scikit-learn" in sklearn_uri def test_image_spec_retrive_with_version(self): - image_spec = ImageSpec(framework=Framework.XGBOOST, - version="0.90-1") + image_spec = ImageSpec(framework=Framework.XGBOOST, version="0.90-1") xgboost_uri = image_spec.retrieve() - assert xgboost_uri == '246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:0.90-1-cpu-py3' + assert ( + xgboost_uri + == "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:0.90-1-cpu-py3" + ) From cb7af786c2a9cfe4b4765bd40fc86290dd87435e Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Tue, 8 Oct 2024 16:11:40 -0700 Subject: [PATCH 06/79] Add enviornment variable bootstrapping script (#1530) * Add enviornment variables scripts * format * fix comment * add docstrings * fix comment --- src/sagemaker/modules/scripts/environment.py | 232 +++++++++++++++++++ src/sagemaker/modules/templates.py | 4 + 2 files changed, 236 insertions(+) create mode 100644 src/sagemaker/modules/scripts/environment.py diff --git a/src/sagemaker/modules/scripts/environment.py b/src/sagemaker/modules/scripts/environment.py new file mode 100644 index 0000000000..db54f001f3 --- /dev/null +++ b/src/sagemaker/modules/scripts/environment.py @@ -0,0 +1,232 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module is used to define the environment variables for the training job container.""" + +from __future__ import absolute_import + +from typing import Dict, Any +import multiprocessing +import subprocess +import json +import os +import sys +import logging + +# Initialize logger +SM_LOG_LEVEL = os.environ.get("SM_LOG_LEVEL", 20) +logger = logging.getLogger(__name__) +console_handler = logging.StreamHandler(sys.stdout) +logger.addHandler(console_handler) +logger.setLevel(SM_LOG_LEVEL) + +SM_MODEL_DIR = "/opt/ml/model" + +SM_INPUT_DIR = "/opt/ml/input" +SM_INPUT_DATA_DIR = "/opt/ml/input/data" +SM_INPUT_CONFIG_DIR = "/opt/ml/input/config" + +SM_OUTPUT_DIR = "/opt/ml/output" +SM_OUTPUT_FAILURE = "/opt/ml/output/failure" +SM_OUTPUT_DATA_DIR = "/opt/ml/output/data" + +SM_MASTER_ADDR = "algo-1" +SM_MASTER_PORT = 7777 + +RESOURCE_CONFIG = f"{SM_INPUT_CONFIG_DIR}/resourceconfig.json" +INPUT_DATA_CONFIG = f"{SM_INPUT_CONFIG_DIR}/inputdataconfig.json" +HYPERPARAMETERS_CONFIG = f"{SM_INPUT_CONFIG_DIR}/hyperparameters.json" + +ENV_OUTPUT_FILE = "sm_training.env" + + +def num_cpus(): + """Return the number of CPUs available in the current container. + + Returns: + int: Number of CPUs available in the current container. + """ + return multiprocessing.cpu_count() + + +def num_gpus(): + """Return the number of GPUs available in the current container. + + Returns: + int: Number of GPUs available in the current container. + """ + try: + cmd = ["nvidia-smi", "--list-gpus"] + output = subprocess.check_output(cmd).decode("utf-8") + return sum(1 for line in output.splitlines() if line.startswith("GPU ")) + except (OSError, subprocess.CalledProcessError): + logger.info("No GPUs detected (normal if no gpus installed)") + return 0 + + +def num_neurons(): + """Return the number of neuron cores available in the current container. + + Returns: + int: Number of Neuron Cores available in the current container. + """ + try: + cmd = ["neuron-ls", "-j"] + output = subprocess.check_output(cmd, stderr=subprocess.STDOUT).decode("utf-8") + j = json.loads(output) + neuron_cores = 0 + for item in j: + neuron_cores += item.get("nc_count", 0) + logger.info(f"Found {neuron_cores} neurons on this instance") + return neuron_cores + except OSError: + logger.info("No Neurons detected (normal if no neurons installed)") + return 0 + except subprocess.CalledProcessError as e: + if e.output is not None: + try: + msg = e.output.decode("utf-8").partition("error=")[2] + logger.info( + "No Neurons detected (normal if no neurons installed). \ + If neuron installed then {}".format( + msg + ) + ) + except AttributeError: + logger.info("No Neurons detected (normal if no neurons installed)") + else: + logger.info("No Neurons detected (normal if no neurons installed)") + + return 0 + + +def set_env( + resource_config: Dict[str, Any] = {}, + input_data_config: Dict[str, Any] = {}, + hyperparameters_config: Dict[str, Any] = {}, + output_file: str = "sm_training.env", + write_to_etc: bool = False, +): + """Set environment variables for the training job container. + + Args: + resource_config (Dict[str, Any]): Resource configuration for the training job. + input_data_config (Dict[str, Any]): Input data configuration for the training job. + hyperparameters_config (Dict[str, Any]): Hyperparameters configuration for the training job. + output_file (str): Output file to write the environment variables. + write_to_etc (bool): Whether to write the environment variables to /etc/environment. + """ + # Constants + env_vars = { + "SM_MODEL_DIR": SM_MODEL_DIR, + "SM_INPUT_DIR": SM_INPUT_DIR, + "SM_INPUT_DATA_DIR": SM_INPUT_DATA_DIR, + "SM_INPUT_CONFIG_DIR": SM_INPUT_CONFIG_DIR, + "SM_OUTPUT_DIR": SM_OUTPUT_DIR, + "SM_OUTPUT_FAILURE": SM_OUTPUT_FAILURE, + "SM_OUTPUT_DATA_DIR": SM_OUTPUT_DATA_DIR, + "SM_LOG_LEVEL": SM_LOG_LEVEL, + "SM_MASTER_ADDR": SM_MASTER_ADDR, + "SM_MASTER_PORT": SM_MASTER_PORT, + } + + # Data Channels + channels = list(input_data_config.keys()) + for channel in channels: + env_vars[f"SM_CHANNEL_{channel.upper()}"] = f"{SM_INPUT_DATA_DIR}/{channel}" + env_vars["SM_CHANNELS"] = channels + + # Hyperparameters + env_vars["SM_HPS"] = hyperparameters_config + for key, value in hyperparameters_config.items(): + env_vars[f"SM_HP_{key.upper()}"] = value + + # Host Variables + current_host = resource_config["current_host"] + hosts = resource_config["hosts"] + sorted_hosts = sorted(hosts) + + env_vars["SM_CURRENT_HOST"] = current_host + env_vars["SM_HOSTS"] = sorted_hosts + env_vars["SM_NETWORK_INTERFACE_NAME"] = resource_config["network_interface_name"] + env_vars["SM_HOST_COUNT"] = len(sorted_hosts) + env_vars["SM_CURRENT_HOST_RANK"] = sorted_hosts.index(current_host) + + env_vars["SM_NUM_CPUS"] = num_cpus() + env_vars["SM_NUM_GPUS"] = num_gpus() + env_vars["SM_NUM_NEURONS"] = num_neurons() + + # Misc. + env_vars["SM_RESOURCE_CONFIG"] = resource_config + env_vars["SM_INPUT_DATA_CONFIG"] = input_data_config + + # All Training Environment Variables + env_vars["SM_TRAINING_ENV"] = { + "channel_input_dirs": { + channel: env_vars[f"SM_CHANNEL_{channel.upper()}"] for channel in channels + }, + "current_host": env_vars["SM_CURRENT_HOST"], + "hosts": env_vars["SM_HOSTS"], + "master_addr": env_vars["SM_MASTER_ADDR"], + "master_port": env_vars["SM_MASTER_PORT"], + "hyperparameters": env_vars["SM_HPS"], + "input_data_config": input_data_config, + "input_config_dir": env_vars["SM_INPUT_CONFIG_DIR"], + "input_data_dir": env_vars["SM_INPUT_DATA_DIR"], + "input_dir": env_vars["SM_INPUT_DIR"], + "job_name": os.environ["TRAINING_JOB_NAME"], + "log_level": env_vars["SM_LOG_LEVEL"], + "model_dir": env_vars["SM_MODEL_DIR"], + "network_interface_name": env_vars["SM_NETWORK_INTERFACE_NAME"], + "num_cpus": env_vars["SM_NUM_CPUS"], + "num_gpus": env_vars["SM_NUM_GPUS"], + "num_neurons": env_vars["SM_NUM_NEURONS"], + "output_data_dir": env_vars["SM_OUTPUT_DATA_DIR"], + "resource_config": env_vars["SM_RESOURCE_CONFIG"], + } + try: + cur_dir = os.path.dirname(os.path.abspath(__file__)) + except NameError: + # Fallback to current working directory + cur_dir = os.getcwd() + with open(os.path.join(cur_dir, output_file), "w") as f: + for key, value in env_vars.items(): + if isinstance(value, (list, dict)): + f.write(f"export {key}='{json.dumps(value)}'\n") + else: + f.write(f"export {key}='{value}'\n") + + # Need to write to /etc/environment for MPI to work + if write_to_etc: + with open("/etc/environment", "a") as f: + for key, value in env_vars.items(): + if isinstance(value, (list, dict)): + f.write(f"{key}='{json.dumps(value)}'\n") + else: + f.write(f"{key}='{value}'\n") + + +if __name__ == "__main__": + with open(RESOURCE_CONFIG, "r") as f: + resource_config = json.load(f) + with open(INPUT_DATA_CONFIG, "r") as f: + input_data_config = json.load(f) + with open(HYPERPARAMETERS_CONFIG, "r") as f: + hyperparameters_config = json.load(f) + + set_env( + resource_config=resource_config, + input_data_config=input_data_config, + hyperparameters_config=hyperparameters_config, + output_file=ENV_OUTPUT_FILE, + write_to_etc=True, + ) diff --git a/src/sagemaker/modules/templates.py b/src/sagemaker/modules/templates.py index f040919829..3040bef5f1 100644 --- a/src/sagemaker/modules/templates.py +++ b/src/sagemaker/modules/templates.py @@ -29,6 +29,10 @@ cat /opt/ml/input/config/hyperparameters.json echo +echo "Setting up environment variables" +python /opt/ml/input/data/sm_code/environment.py +source /opt/ml/input/data/sm_code/sm_training.env + python --version {working_dir} {install_requirements} From 80a1b897df817adc06c0f2c996494522146653ef Mon Sep 17 00:00:00 2001 From: pintaoz-aws <167920275+pintaoz-aws@users.noreply.github.com> Date: Tue, 8 Oct 2024 16:40:30 -0700 Subject: [PATCH 07/79] feature: add utility function to capture local snapshot (#1524) * local snapshot * Update pip list command * Remove function calls * Address comments * Address comments --- src/sagemaker/modules/train/local_snapshot.py | 320 ++++++++++++++++++ 1 file changed, 320 insertions(+) create mode 100644 src/sagemaker/modules/train/local_snapshot.py diff --git a/src/sagemaker/modules/train/local_snapshot.py b/src/sagemaker/modules/train/local_snapshot.py new file mode 100644 index 0000000000..dc694130fd --- /dev/null +++ b/src/sagemaker/modules/train/local_snapshot.py @@ -0,0 +1,320 @@ +import boto3 +import docker +import logging +import subprocess +import sys +from typing import Optional +import yaml + +logger = logging.getLogger(__name__) + +REQUIREMENT_TXT_PATH = "/tmp/requirements.txt" +ENVIRONMENT_YML_PATH = "/tmp/environment.yml" +DOCKERFILE_PATH = "/tmp/Dockerfile" + +CONDA_DOCKERFILE_TEMPLATE = """ +FROM {base_image_name} +ADD environment.yml . + +# Install prerequisites for conda +RUN apt-get update && \ + apt-get install -y wget bzip2 ca-certificates libglib2.0-0 libxext6 libsm6 libxrender1 && \ + apt-get clean + +# Download and install conda +RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \ + bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda && \ + rm Miniconda3-latest-Linux-x86_64.sh + +# Initialize Conda +ENV PATH=/opt/conda/bin:$PATH +RUN conda update -n base -c defaults conda && \ + conda config --add channels conda-forge + +# Create a conda environment from the environment.yml file +RUN conda env create -f environment.yml -n {env_name} + +# Activate the conda environment +RUN conda run -n {env_name} +""" + +PIP_DOCKERFILE_TEMPLATE = """ +FROM {base_image_name} +ADD requirements.txt . + +# Create a virtual environment +RUN python -m venv {env_name} + +# Activate the virtual environment +RUN . {env_name}/bin/activate + +RUN pip install --no-cache-dir -r requirements.txt +""" + + +def capture_local_environment( + image_name: str = "sm-local-capture", + env_name: str = "saved_local_env", + package_manager: str = "pip", + deploy_to_ecr: bool = False, + base_image_name: Optional[str] = None, + job_conda_env: Optional[str] = None, + additional_dependencies: Optional[str] = None, + ecr_repo_name: Optional[str] = None, + boto_session: Optional[boto3.Session] = None, +): + """ + Capture all dependency packages installed in the local environment and build a docker image. + When using this utility method, the docker daemon must be active in the environment. + Please note that this is an experimental feature. This utility function is not be able to detect the package + compatability between platforms. It is also not able to detect dependency conflicts between the local environment + and the additional dependencies. + + Args: + image_name (str): The name of the docker image. + env_name (str): The name of the virtual environment to be activated in the image, defaults to "saved_local_env". + package_manager (str): The package manager, must be one of "conda" or "pip". + deploy_to_ecr (bool): Whether to deploy the docker image to AWS ECR, defaults to False. If set to True, the AWS + credentials must be configured in the environment. + base_image_name (Optional[str]): If provided will be used as the base image, else the utility will evaluate + from local environment in following manner: + 1. If package manager is conda, it will use ubuntu:latest. + 2. If package manager is pip, it is resolved to base python image with the same python version + as the environment running the local code. + job_conda_env (Optional[str]): If set, the dependencies will be captured from this specific conda Env, + otherwise the dependencies will be the installed packages in the current active environment. This parameter + is only valid when the package manager is conda. + additional_dependencies (Optional[str]): Either the path to a dependencies file (conda environment.yml OR pip + requirements.txt file). Regardless of this setting utility will automatically generate the dependencies + file corresponding to the current active environment’s snapshot. In addition to this, additional dependencies + is configurable. + ecr_repo_name (Optional[str]): The AWS ECR repo to push the docker image. If not specified, it will use image_name as + the ECR repo name. This parameter is only valid when deploy_to_ecr is True. + boto_session (Optional[boto3.Session]): The boto3 session with AWS account info. If not provided, a new boto session + will be created. + + Exceptions: + docker.errors.DockerException: Error while fetching server API version: + The docker engine is not running in your environment. + docker.errors.BuildError: The docker failed to build the image. The most likely reason is: 1) Some packages are not + supported in the base image. 2) There are dependency conflicts between your local environment and additional dependencies. + botocore.exceptions.ClientError: AWS credentials are not configured. + """ + + if package_manager == "conda": + if job_conda_env: + subprocess.run( + f"conda env export -n {job_conda_env} > {ENVIRONMENT_YML_PATH} --no-builds", + shell=True, + check=True, + ) + else: + subprocess.run( + f"conda env export > {ENVIRONMENT_YML_PATH} --no-builds", shell=True, check=True + ) + + if additional_dependencies: + if not additional_dependencies.endswith( + ".yml" + ) and not additional_dependencies.endswith(".txt"): + raise ValueError( + "When package manager is conda, additional dependencies file must be a yml file or a txt file." + ) + if additional_dependencies.endswith(".yml"): + _merge_environment_ymls( + env_name, + ENVIRONMENT_YML_PATH, + additional_dependencies, + ENVIRONMENT_YML_PATH, + ) + elif additional_dependencies.endswith(".txt"): + _merge_environment_yml_with_requirement_txt( + env_name, + ENVIRONMENT_YML_PATH, + additional_dependencies, + ENVIRONMENT_YML_PATH, + ) + + if not base_image_name: + base_image_name = "ubuntu:latest" + dockerfile_contents = CONDA_DOCKERFILE_TEMPLATE.format( + base_image_name=base_image_name, + env_name=env_name, + ) + elif package_manager == "pip": + subprocess.run(f"pip list --format=freeze > {REQUIREMENT_TXT_PATH}", shell=True, check=True) + + if additional_dependencies: + if not additional_dependencies.endswith(".txt"): + raise ValueError( + "When package manager is pip, additional dependencies file must be a txt file." + ) + with open(additional_dependencies, "r") as f: + additional_requirements = f.read() + with open(REQUIREMENT_TXT_PATH, "a") as f: + f.write(additional_requirements) + logger.info(f"Merged requirements file saved to {REQUIREMENT_TXT_PATH}") + + if not base_image_name: + version = sys.version_info + base_image_name = f"python:{version.major}.{version.minor}.{version.micro}" + dockerfile_contents = PIP_DOCKERFILE_TEMPLATE.format( + base_image_name=base_image_name, + env_name=env_name, + ) + + else: + raise ValueError( + "The provided package manager is not supported. Use conda or pip as the package manager." + ) + + # Create the Dockerfile + with open(DOCKERFILE_PATH, "w") as f: + f.write(dockerfile_contents) + + client = docker.from_env() + image, logs = client.images.build( + path="/tmp", + dockerfile=DOCKERFILE_PATH, + rm=True, + tag=image_name, + ) + for log in logs: + logger.info(log.get("stream", "").strip()) + logger.info(f"Docker image {image_name} built successfully") + + if deploy_to_ecr: + if boto_session is None: + boto_session = boto3.Session() + _push_image_to_ecr(image_name, ecr_repo_name, boto_session) + + +def _merge_environment_ymls(env_name: str, env_file1: str, env_file2: str, output_file: str): + """ + Merge two environment.yml files and save to a new environment.yml file. + + Args: + env_name (str): The name of the virtual environment to be activated in the image. + env_file1 (str): The path of the first environment.yml file. + env_file2 (str): The path of the second environment.yml file. + output_file (str): The path of the output environment.yml file. + """ + + # Load the YAML files + with open(env_file1, "r") as f: + env1 = yaml.safe_load(f) + with open(env_file2, "r") as f: + env2 = yaml.safe_load(f) + + # Combine dependencies and channels from both files + dependencies = [] + pip_dependencies = [] + channels = set() + + for env in [env1, env2]: + if "dependencies" in env: + for dep in env["dependencies"]: + if isinstance(dep, str): + # Conda package, e.g., 'python=3.7' + dependencies.append(dep) + elif isinstance(dep, dict): + # Pip package list, e.g., {'pip': ['requests>=2.22.0']} + for pip_package in dep.get("pip", []): + pip_dependencies.append(pip_package) + if "channels" in env: + channels.update(env["channels"]) + + if pip_dependencies: + dependencies.append({"pip": pip_dependencies}) + # Create the merged environment file + merged_env = {"name": env_name, "channels": list(channels), "dependencies": dependencies} + + with open(output_file, "w") as f: + yaml.dump(merged_env, f, sort_keys=False) + + logger.info(f"Merged environment file saved to '{output_file}'") + + +def _merge_environment_yml_with_requirement_txt( + env_name: str, env_file: str, req_txt: str, output_file: str +): + """ + Merge an environment.yml file with a requirements.txt file and save to a new environment.yml file. + + Args: + env_name (str): The name of the virtual environment to be activated in the image. + env_file (str): The path of the environment.yml file. + req_txt (str): The path of the requirements.txt file. + output_file (str): The path of the output environment.yml file. + """ + # Load the files + with open(env_file, "r") as f: + env = yaml.safe_load(f) + with open(req_txt, "r") as f: + requirements = f.read().splitlines() + # Combine pip dependencies from both files + dependencies = [] + pip_dependencies = [] + + if "dependencies" in env: + for dep in env["dependencies"]: + if isinstance(dep, str): + # Conda package, e.g., 'python=3.7' + dependencies.append(dep) + elif isinstance(dep, dict): + # Pip package list, e.g., {'pip': ['requests>=2.22.0']} + for pip_package in dep.get("pip", []): + pip_dependencies.append(pip_package) + + for req in requirements: + if req and not req.startswith("#"): + pip_dependencies.append(req) + + if pip_dependencies: + dependencies.append({"pip": pip_dependencies}) + # Create the merged environment file + merged_env = {"name": env_name, "channels": env["channels"], "dependencies": dependencies} + + with open(output_file, "w") as f: + yaml.dump(merged_env, f, sort_keys=False) + + logger.info(f"Merged environment file saved to '{output_file}'") + + +def _push_image_to_ecr(image_name: str, ecr_repo_name: str, boto_session: Optional[boto3.Session]): + """ + Push the docker image to AWS ECR. + + Args: + image_name (str): The name of the docker image. + ecr_repo_name (str): The AWS ECR repo to push the docker image. + """ + region = boto_session.region_name + aws_account_id = boto_session.client("sts", region_name=region).get_caller_identity()["Account"] + ecr_client = boto3.client("ecr") + + # Authenticate Docker with ECR + registry_url = f"{aws_account_id}.dkr.ecr.{region}.amazonaws.com" + docker_login_cmd = ( + f"aws ecr get-login-password --region {region} " + f"| docker login --username AWS --password-stdin {aws_account_id}.dkr.ecr.{region}.amazonaws.com" + ) + subprocess.run(docker_login_cmd, shell=True, check=True) + + # Create a new ECR repository (if it doesn't already exist) + ecr_repo_name = ecr_repo_name or image_name + try: + ecr_client.create_repository(repositoryName=ecr_repo_name) + except ecr_client.exceptions.RepositoryAlreadyExistsException: + pass + + # Tag the local Docker image + ecr_image_uri = f"{registry_url}/{ecr_repo_name}:latest" + docker_tag_cmd = f"docker tag {image_name}:latest {ecr_image_uri}" + subprocess.run(docker_tag_cmd, shell=True, check=True) + + # Push the Docker image to ECR + docker_push_cmd = f"docker push {ecr_image_uri}" + subprocess.run(docker_push_cmd, shell=True, check=True) + + logger.info(f"Image {image_name} pushed to {ecr_image_uri}") From 93a3c6dd007502905fdb85f5cb322098d433e1c1 Mon Sep 17 00:00:00 2001 From: pintaoz-aws <167920275+pintaoz-aws@users.noreply.github.com> Date: Tue, 15 Oct 2024 11:03:44 -0700 Subject: [PATCH 08/79] Support intelligent parameters (#1540) * Support intelligent parameters * fix codestyle --- .../modules/scripts/intelligent_params.py | 101 ++++++++++++++++ src/sagemaker/modules/train/local_snapshot.py | 88 ++++++++------ .../scripts/test_intelligent_params.py | 109 ++++++++++++++++++ 3 files changed, 264 insertions(+), 34 deletions(-) create mode 100644 src/sagemaker/modules/scripts/intelligent_params.py create mode 100644 tests/unit/sagemaker/modules/scripts/test_intelligent_params.py diff --git a/src/sagemaker/modules/scripts/intelligent_params.py b/src/sagemaker/modules/scripts/intelligent_params.py new file mode 100644 index 0000000000..71a94162a3 --- /dev/null +++ b/src/sagemaker/modules/scripts/intelligent_params.py @@ -0,0 +1,101 @@ +import argparse +import json +import os +import re + +HYPERPARAMETERS_FILE_PATH = "/opt/ml/input/config/hyperparameters.json" + + +def set_intelligent_params(path: str) -> None: + """ + Set intelligent parameters for all python files under the given path. + For python code with comment sm_hyper_param or sm_hp_{variable_name}, the value will be found in + /opt/ml/input/config/hyperparameters.json, and this function will rewrite lines with these comments. + + Args: + path (str): The folder path to set intellingent parameters + """ + with open(HYPERPARAMETERS_FILE_PATH, "r") as f: + hyperparameters = json.load(f) + for root, dirs, files in os.walk(path): + for file in files: + if file.endswith(".py"): + file_path = os.path.join(root, file) + rewrite_file(file_path, hyperparameters) + + +def rewrite_file(file_path: str, hyperparameters: dict) -> None: + """ + Rewrite a single python file with intelligent parameters. + + Args: + file_path (str): The file path to rewrite + hyperparameters (dict): The hyperparameter names and values + """ + with open(file_path, "r", encoding="utf-8") as f: + lines = f.readlines() + for i in range(len(lines)): + lines[i] = rewrite_line(lines[i], hyperparameters) + with open(file_path, "w", encoding="utf-8") as f: + f.writelines(lines) + + +def rewrite_line(line: str, hyperparameters: dict) -> None: + """ + Rewrite a single line of python code with intelligent parameters. + + Args: + line (str): The python code to rewrite + hyperparameters (dict): The hyperparameter names and values + """ + # Remove strings from the line to avoid = and # in strings + line_without_strings = re.sub(r'".*?"', '""', line.strip()) + line_without_strings = re.sub(r"'.*?'", '""', line_without_strings) + + # Match lines with format "a = 1 # comment" + assignment_pattern = r"^[a-zA-Z_][a-zA-Z0-9_]*\s*=.*#.*" + if re.match(assignment_pattern, line_without_strings): + indent = (len(line) - len(line.lstrip())) * " " + variable = line_without_strings.split("=")[0].strip() + comment = line_without_strings.split("#")[-1].strip() + value = get_parameter_value(variable, comment, hyperparameters) + if value is None: + return line + if isinstance(value, str): + new_line = f'{indent}{variable} = "{value}" # set by intelligent parameters\n' + else: + new_line = f"{indent}{variable} = {str(value)} # set by intelligent parameters\n" + return new_line + return line + + +def get_parameter_value(variable: str, comment: str, hyperparameters: dict) -> None: + """ + Get the parameter value by the variable name and comment. + + Args: + variable (str): The variable name + comment (str): The comment string in the python code + hyperparameters (dict): The hyperparameter names and values + """ + if comment == "sm_hyper_param": + # Get the hyperparameter value by the variable name + return hyperparameters.get(variable, None) + if comment.startswith("sm_hp_"): + # Get the hyperparameter value by the suffix of comment + return hyperparameters.get(comment[6:], None) + # Get the hyperparameter value from environment variables + if comment.startswith("sm_"): + return os.environ.get(comment.upper(), None) + return None + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Intelligent parameters") + parser.add_argument( + "-p", "--path", help="The folder path to set intellingent parameters", required=True + ) + + args = parser.parse_args() + + set_intelligent_params(args.path) diff --git a/src/sagemaker/modules/train/local_snapshot.py b/src/sagemaker/modules/train/local_snapshot.py index dc694130fd..b697286a88 100644 --- a/src/sagemaker/modules/train/local_snapshot.py +++ b/src/sagemaker/modules/train/local_snapshot.py @@ -1,9 +1,23 @@ -import boto3 -import docker +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Utility function to capture local environment""" import logging import subprocess import sys from typing import Optional + +import boto3 +import docker import yaml logger = logging.getLogger(__name__) @@ -66,38 +80,41 @@ def capture_local_environment( """ Capture all dependency packages installed in the local environment and build a docker image. When using this utility method, the docker daemon must be active in the environment. - Please note that this is an experimental feature. This utility function is not be able to detect the package - compatability between platforms. It is also not able to detect dependency conflicts between the local environment - and the additional dependencies. + Please note that this is an experimental feature. This utility function is not be able to + detect the package compatability between platforms. It is also not able to detect dependency + conflicts between the local environment and the additional dependencies. Args: image_name (str): The name of the docker image. - env_name (str): The name of the virtual environment to be activated in the image, defaults to "saved_local_env". + env_name (str): The name of the virtual environment to be activated in the image, + defaults to "saved_local_env". package_manager (str): The package manager, must be one of "conda" or "pip". - deploy_to_ecr (bool): Whether to deploy the docker image to AWS ECR, defaults to False. If set to True, the AWS - credentials must be configured in the environment. - base_image_name (Optional[str]): If provided will be used as the base image, else the utility will evaluate - from local environment in following manner: + deploy_to_ecr (bool): Whether to deploy the docker image to AWS ECR, defaults to False. + If set to True, the AWS credentials must be configured in the environment. + base_image_name (Optional[str]): If provided will be used as the base image, else the + utility will evaluate from local environment in following manner: 1. If package manager is conda, it will use ubuntu:latest. - 2. If package manager is pip, it is resolved to base python image with the same python version - as the environment running the local code. - job_conda_env (Optional[str]): If set, the dependencies will be captured from this specific conda Env, - otherwise the dependencies will be the installed packages in the current active environment. This parameter - is only valid when the package manager is conda. - additional_dependencies (Optional[str]): Either the path to a dependencies file (conda environment.yml OR pip - requirements.txt file). Regardless of this setting utility will automatically generate the dependencies - file corresponding to the current active environment’s snapshot. In addition to this, additional dependencies - is configurable. - ecr_repo_name (Optional[str]): The AWS ECR repo to push the docker image. If not specified, it will use image_name as - the ECR repo name. This parameter is only valid when deploy_to_ecr is True. - boto_session (Optional[boto3.Session]): The boto3 session with AWS account info. If not provided, a new boto session - will be created. + 2. If package manager is pip, it is resolved to base python image with the same + python version as the environment running the local code. + job_conda_env (Optional[str]): If set, the dependencies will be captured from this specific + conda Env, otherwise the dependencies will be the installed packages in the current + active environment. This parameter is only valid when the package manager is conda. + additional_dependencies (Optional[str]): Either the path to a dependencies file (conda + environment.yml OR pip requirements.txt file). Regardless of this setting utility will + automatically generate the dependencies file corresponding to the current active + environment’s snapshot. In addition to this, additional dependencies is configurable. + ecr_repo_name (Optional[str]): The AWS ECR repo to push the docker image. If not specified, + it will use image_name as the ECR repo name. This parameter is only valid when + deploy_to_ecr is True. + boto_session (Optional[boto3.Session]): The boto3 session with AWS account info. If not + provided, a new boto session will be created. Exceptions: docker.errors.DockerException: Error while fetching server API version: The docker engine is not running in your environment. - docker.errors.BuildError: The docker failed to build the image. The most likely reason is: 1) Some packages are not - supported in the base image. 2) There are dependency conflicts between your local environment and additional dependencies. + docker.errors.BuildError: The docker failed to build the image. The most likely reason is: + 1) Some packages are not supported in the base image. 2) There are dependency conflicts + between your local environment and additional dependencies. botocore.exceptions.ClientError: AWS credentials are not configured. """ @@ -118,7 +135,8 @@ def capture_local_environment( ".yml" ) and not additional_dependencies.endswith(".txt"): raise ValueError( - "When package manager is conda, additional dependencies file must be a yml file or a txt file." + "When package manager is conda, additional dependencies " + "file must be a yml file or a txt file." ) if additional_dependencies.endswith(".yml"): _merge_environment_ymls( @@ -153,7 +171,7 @@ def capture_local_environment( additional_requirements = f.read() with open(REQUIREMENT_TXT_PATH, "a") as f: f.write(additional_requirements) - logger.info(f"Merged requirements file saved to {REQUIREMENT_TXT_PATH}") + logger.info("Merged requirements file saved to %s", REQUIREMENT_TXT_PATH) if not base_image_name: version = sys.version_info @@ -165,7 +183,8 @@ def capture_local_environment( else: raise ValueError( - "The provided package manager is not supported. Use conda or pip as the package manager." + "The provided package manager is not supported. " + "Use conda or pip as the package manager." ) # Create the Dockerfile @@ -173,7 +192,7 @@ def capture_local_environment( f.write(dockerfile_contents) client = docker.from_env() - image, logs = client.images.build( + _, logs = client.images.build( path="/tmp", dockerfile=DOCKERFILE_PATH, rm=True, @@ -181,7 +200,7 @@ def capture_local_environment( ) for log in logs: logger.info(log.get("stream", "").strip()) - logger.info(f"Docker image {image_name} built successfully") + logger.info("Docker image %s built successfully", image_name) if deploy_to_ecr: if boto_session is None: @@ -232,14 +251,15 @@ def _merge_environment_ymls(env_name: str, env_file1: str, env_file2: str, outpu with open(output_file, "w") as f: yaml.dump(merged_env, f, sort_keys=False) - logger.info(f"Merged environment file saved to '{output_file}'") + logger.info("Merged environment file saved to '%s'", output_file) def _merge_environment_yml_with_requirement_txt( env_name: str, env_file: str, req_txt: str, output_file: str ): """ - Merge an environment.yml file with a requirements.txt file and save to a new environment.yml file. + Merge an environment.yml file with a requirements.txt file and save to a new + environment.yml file. Args: env_name (str): The name of the virtual environment to be activated in the image. @@ -278,7 +298,7 @@ def _merge_environment_yml_with_requirement_txt( with open(output_file, "w") as f: yaml.dump(merged_env, f, sort_keys=False) - logger.info(f"Merged environment file saved to '{output_file}'") + logger.info("Merged environment file saved to '%s'", output_file) def _push_image_to_ecr(image_name: str, ecr_repo_name: str, boto_session: Optional[boto3.Session]): @@ -317,4 +337,4 @@ def _push_image_to_ecr(image_name: str, ecr_repo_name: str, boto_session: Option docker_push_cmd = f"docker push {ecr_image_uri}" subprocess.run(docker_push_cmd, shell=True, check=True) - logger.info(f"Image {image_name} pushed to {ecr_image_uri}") + logger.info("Image %s pushed to %s", image_name, ecr_image_uri) diff --git a/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py b/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py new file mode 100644 index 0000000000..95939cefae --- /dev/null +++ b/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py @@ -0,0 +1,109 @@ +import os +import pytest +from sagemaker.modules.scripts.intelligent_params import ( + rewrite_file, + rewrite_line, +) + + +@pytest.fixture() +def hyperparameters(): + return {"n_estimators": 3, "epochs": 4, "state": "In Progress", "list_value": [1, 2, 3]} + + +@pytest.fixture() +def python_code(): + return """ +import sagemaker + +def main(args): + n_estimators = 10 # sm_hp_n_estimators + state = "Not Started" # sm_hyper_param + random_seed = 0.1 # sm_hyper_param + + output_dir = "local/dir/" # sm_model_dir + epochs = 5 # sm_hp_epochs + input_data = [0, 0, 0] # sm_hp_list_value + + # Load the Iris dataset + iris = load_iris() + y = iris.target + + # Make predictions on the test set + y_pred = clf.predict(input_data) + + accuracy = accuracy_score(y, y_pred) # calculate the accuracy + print(f"# Model accuracy: {accuracy:.2f}") +""" + + +@pytest.fixture() +def expected_output_code(): + return """ +import sagemaker + +def main(args): + n_estimators = 3 # set by intelligent parameters + state = "In Progress" # set by intelligent parameters + random_seed = 0.1 # sm_hyper_param + + output_dir = "/opt/ml/input" # set by intelligent parameters + epochs = 4 # set by intelligent parameters + input_data = [1, 2, 3] # set by intelligent parameters + + # Load the Iris dataset + iris = load_iris() + y = iris.target + + # Make predictions on the test set + y_pred = clf.predict(input_data) + + accuracy = accuracy_score(y, y_pred) # calculate the accuracy + print(f"# Model accuracy: {accuracy:.2f}") +""" + + +def test_rewrite_line(hyperparameters): + line = "n_estimators = 4 # sm_hyper_param" + new_line = rewrite_line(line, hyperparameters) + assert new_line == "n_estimators = 3 # set by intelligent parameters\n" + + line = " epochs = 5 # sm_hp_epochs" + new_line = rewrite_line(line, hyperparameters) + assert new_line == " epochs = 4 # set by intelligent parameters\n" + + os.environ["SM_MODEL_DIR"] = "/opt/ml/input" + line = 'output_dir = "local/dir/" # sm_model_dir ' + new_line = rewrite_line(line, hyperparameters) + assert new_line == 'output_dir = "/opt/ml/input" # set by intelligent parameters\n' + + line = " random_state = 1 # not an intelligent parameter comment \n" + new_line = rewrite_line(line, hyperparameters) + assert new_line == " random_state = 1 # not an intelligent parameter comment \n" + + line = "not_an_intelligent_parameter = 4 # sm_hyper_param\n" + new_line = rewrite_line(line, hyperparameters) + assert new_line == "not_an_intelligent_parameter = 4 # sm_hyper_param\n" + + line = "not_found_in_hyper_params = 4 # sm_hp_not_found_in_hyper_params\n" + new_line = rewrite_line(line, hyperparameters) + assert new_line == "not_found_in_hyper_params = 4 # sm_hp_not_found_in_hyper_params\n" + + line = "list_value = [4, 5, 6] # sm_hyper_param" + new_line = rewrite_line(line, hyperparameters) + assert new_line == "list_value = [1, 2, 3] # set by intelligent parameters\n" + + +def test_rewrite_file(hyperparameters, python_code, expected_output_code): + test_file_path = "temp_test.py" + + os.environ["SM_MODEL_DIR"] = "/opt/ml/input" + with open(test_file_path, "w") as f: + f.write(python_code) + rewrite_file(test_file_path, hyperparameters) + + with open(test_file_path, "r") as f: + new_python_code = f.read() + assert new_python_code == expected_output_code + + os.remove(test_file_path) From 4fe87383063fa939f05afd2feb25c5f58df19497 Mon Sep 17 00:00:00 2001 From: Gokul Anantha Narayanan <166456257+nargokul@users.noreply.github.com> Date: Tue, 15 Oct 2024 11:12:06 -0700 Subject: [PATCH 09/79] Revert Image Spec (#1541) --- src/sagemaker/modules/image_spec.py | 281 ------------------ .../base_model_trainer.ipynb | 132 ++++---- src/sagemaker/modules/train/model_trainer.py | 17 +- .../unit/sagemaker/modules/test_image_spec.py | 47 --- 4 files changed, 61 insertions(+), 416 deletions(-) delete mode 100644 src/sagemaker/modules/image_spec.py delete mode 100644 tests/unit/sagemaker/modules/test_image_spec.py diff --git a/src/sagemaker/modules/image_spec.py b/src/sagemaker/modules/image_spec.py deleted file mode 100644 index 60b417aaac..0000000000 --- a/src/sagemaker/modules/image_spec.py +++ /dev/null @@ -1,281 +0,0 @@ -# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"). You -# may not use this file except in compliance with the License. A copy of -# the License is located at -# -# http://aws.amazon.com/apache2.0/ -# -# or in the "license" file accompanying this file. This file is -# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF -# ANY KIND, either express or implied. See the License for the specific -# language governing permissions and limitations under the License. -"""ImageSpec class module.""" -from __future__ import absolute_import - -import re -from enum import Enum -from typing import Optional -from packaging.version import Version - -from sagemaker import utils -from sagemaker.image_uris import ( - _validate_version_and_set_if_needed, - _version_for_config, - _config_for_framework_and_scope, - _validate_py_version_and_set_if_needed, - _registry_from_region, - ECR_URI_TEMPLATE, - _get_latest_versions, - _validate_instance_deprecation, - _get_image_tag, - _validate_arg, -) - -DEFAULT_TOLERATE_MODEL = False - - -class Framework(Enum): - """Framework enum class.""" - - HUGGING_FACE = "huggingface" - HUGGING_FACE_NEURON = "huggingface-neuron" - HUGGING_FACE_NEURON_X = "huggingface-neuronx" - HUGGING_FACE_LLM = "huggingface-llm" - HUGGING_FACE_TEI_GPU = "huggingface-tei" - HUGGING_FACE_TEI_CPU = "huggingface-tei-cpu" - HUGGING_FACE_LLM_NEURONX = "huggingface-llm-neuronx" - HUGGING_FACE_TRAINING_COMPILER = "huggingface-training-compiler" - XGBOOST = "xgboost" - XG_BOOST_NEO = "xg-boost-neo" - SKLEARN = "sklearn" - PYTORCH = "pytorch" - PYTORCH_TRAINING_COMPILER = "pytorch-training-compiler" - DATA_WRANGLER = "data-wrangler" - STABILITYAI = "stabilityai" - SAGEMAKER_TRITONSERVER = "sagemaker-tritonserver" - - -class ImageScope(Enum): - """ImageScope enum class.""" - - TRAINING = "training" - INFERENCE = "inference" - INFERENCE_GRAVITON = "inference-graviton" - - -class Processor(Enum): - """Processor enum class.""" - - INF = "inf" - NEURON = "neuron" - GPU = "gpu" - CPU = "cpu" - TRN = "trn" - - -class ImageSpec: - """ImageSpec class to get image URI for a specific framework version. - - Attributes: - framework (Framework): The name of the framework or algorithm. - processor (Processor): The name of the processor (CPU, GPU, etc.). - region (str): The AWS region. - version (str): The framework or algorithm version. This is required if there is - more than one supported version for the given framework or algorithm. - py_version (str): The Python version. This is required if there is - more than one supported Python version for the given framework version. - instance_type (str): The SageMaker instance type. For supported types, see - https://aws.amazon.com/sagemaker/pricing. This is required if - there are different images for different processor types. - accelerator_type (str): Elastic Inference accelerator type. For more, see - https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html. - image_scope (str): The image type, i.e. what it is used for. - Valid values: "training", "inference", "inference_graviton", "eia". - If ``accelerator_type`` is set, ``image_scope`` is ignored. - container_version (str): the version of docker image. - Ideally the value of parameter should be created inside the framework. - For custom use, see the list of supported container versions: - https://github.com/aws/deep-learning-containers/blob/master/available_images.md - (default: None). - distribution (dict): A dictionary with information on how to run distributed training - sdk_version (str): the version of python-sdk that will be used in the image retrieval. - (default: None). - inference_tool (str): the tool that will be used to aid in the inference. - Valid values: "neuron, neuronx, None" - (default: None). - """ - - def __init__( - self, - framework: Framework, - processor: Optional[Processor] = Processor.CPU, - region: Optional[str] = "us-west-2", - version=None, - py_version=None, - instance_type=None, - accelerator_type=None, - image_scope: ImageScope = ImageScope.TRAINING, - container_version=None, - distribution=None, - base_framework_version=None, - sdk_version=None, - inference_tool=None, - ): - self.framework = framework - self.processor = processor - self.version = version - self.image_scope = image_scope - self.instance_type = instance_type - self.py_version = py_version - self.region = region - self.accelerator_type = accelerator_type - self.container_version = container_version - self.distribution = distribution - self.base_framework_version = base_framework_version - self.sdk_version = sdk_version - self.inference_tool = inference_tool - - def update_image_spec(self, **kwargs): - """Update the ImageSpec object with the given arguments.""" - for key, value in kwargs.items(): - if hasattr(self, key): - setattr(self, key, value) - - def retrieve(self) -> str: - """Retrieves the ECR URI for the Docker image matching the given arguments. - - Returns: - str: The ECR URI for the corresponding SageMaker Docker image. - - Raises: - NotImplementedError: If the scope is not supported. - ValueError: If the combination of arguments specified is not supported or - any PipelineVariable object is passed in. - VulnerableJumpStartModelError: If any of the dependencies required by the script have - known security vulnerabilities. - DeprecatedJumpStartModelError: If the version of the model is deprecated. - """ - config = _config_for_framework_and_scope( - self.framework.value, self.image_scope.value, self.accelerator_type - ) - original_version = self.version - try: - version = _validate_version_and_set_if_needed( - self.version, config, self.framework.value - ) - except ValueError: - version = None - if not version: - version = self._fetch_latest_version_from_config(config) - - version_config = config["versions"][_version_for_config(version, config)] - - if "huggingface" in self.framework.value: - if version_config.get("version_aliases"): - full_base_framework_version = version_config["version_aliases"].get( - self.base_framework_version, self.base_framework_version - ) - _validate_arg( - full_base_framework_version, list(version_config.keys()), "base framework" - ) - version_config = version_config.get(full_base_framework_version) - - self.py_version = _validate_py_version_and_set_if_needed( - self.py_version, version_config, self.framework.value - ) - version_config = version_config.get(self.py_version) or version_config - - registry = _registry_from_region(self.region, version_config["registries"]) - endpoint_data = utils._botocore_resolver().construct_endpoint("ecr", self.region) - if self.region == "il-central-1" and not endpoint_data: - endpoint_data = {"hostname": "ecr.{}.amazonaws.com".format(self.region)} - hostname = endpoint_data["hostname"] - - repo = version_config["repository"] - - # if container version is available in .json file, utilize that - if version_config.get("container_version"): - self.container_version = version_config["container_version"][self.processor.value] - - # Append sdk version in case of trainium instances - if repo in ["pytorch-training-neuron"]: - if not self.sdk_version: - sdk_version = _get_latest_versions(version_config["sdk_versions"]) - self.container_version = self.sdk_version + "-" + self.container_version - - if self.framework == Framework.HUGGING_FACE: - pt_or_tf_version = ( - re.compile("^(pytorch|tensorflow)(.*)$").match(self.base_framework_version).group(2) - ) - _version = original_version - - if repo in [ - "huggingface-pytorch-trcomp-training", - "huggingface-tensorflow-trcomp-training", - ]: - _version = version - if repo in [ - "huggingface-pytorch-inference-neuron", - "huggingface-pytorch-inference-neuronx", - ]: - if not sdk_version: - self.sdk_version = _get_latest_versions(version_config["sdk_versions"]) - self.container_version = self.sdk_version + "-" + self.container_version - if config.get("version_aliases").get(original_version): - _version = config.get("version_aliases")[original_version] - if ( - config.get("versions", {}) - .get(_version, {}) - .get("version_aliases", {}) - .get(self.base_framework_version, {}) - ): - _base_framework_version = config.get("versions")[_version]["version_aliases"][ - self.base_framework_version - ] - pt_or_tf_version = ( - re.compile("^(pytorch|tensorflow)(.*)$") - .match(_base_framework_version) - .group(2) - ) - - tag_prefix = f"{pt_or_tf_version}-transformers{_version}" - else: - tag_prefix = version_config.get("tag_prefix", version) - - if repo == f"{self.framework.value}-inference-graviton": - self.container_version = f"{self.container_version}-sagemaker" - _validate_instance_deprecation(self.framework, self.instance_type, version) - - tag = _get_image_tag( - self.container_version, - self.distribution, - self.image_scope.value, - self.framework.value, - self.inference_tool, - self.instance_type, - self.processor.value, - self.py_version, - tag_prefix, - version, - ) - - if tag: - repo += ":{}".format(tag) - - return ECR_URI_TEMPLATE.format(registry=registry, hostname=hostname, repository=repo) - - def _fetch_latest_version_from_config(self, framework_config: dict) -> str: - """Fetches the latest version from the framework config.""" - if self.image_scope.value in framework_config: - if image_scope_config := framework_config[self.image_scope.value]: - if version_aliases := image_scope_config["version_aliases"]: - if latest_version := version_aliases["latest"]: - return latest_version - versions = list(framework_config["versions"].keys()) - top_version = versions[0] - bottom_version = versions[-1] - - if Version(top_version) >= Version(bottom_version): - return top_version - return bottom_version diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index 58c13f7084..567657f6ad 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -2,15 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "import sys, os\n", + "\n", + "from tests.integ.test_processing import image_uri\n", + "\n", "# Get the absolute path of the root directory\n", "root_dir = os.path.abspath(os.path.join(os.getcwd(), '../../..'))\n", "sys.path.insert(0, root_dir)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -25,25 +28,17 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sagemaker.modules.train.model_trainer import ModelTrainer\n", - "from sagemaker.modules.image_spec import ImageSpec, Framework\n", - "\n", - "pytorch_image = ImageSpec(\n", - " framework=Framework.PYTORCH\n", - ")\n", - "\n", - "model_trainer = ModelTrainer(training_image=pytorch_image)\n" - ] + "model_trainer = ModelTrainer(training_image=\"python:3.10.15-slim\")\n" + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", @@ -51,7 +46,9 @@ " command=\"echo 'Hello World' && env\",\n", ")\n", "model_trainer.train(source_code_config=source_code_config)" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -72,9 +69,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", @@ -89,7 +84,9 @@ " \"test\": \"script-mode-basic/data/test\",\n", " }\n", ")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -100,9 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", @@ -118,7 +113,9 @@ " \"test\": \"script-mode-basic/data/test\",\n", " }\n", ")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -129,24 +126,24 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "!pip install \"datasets[s3]\"" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "access_token = os.environ.get(\"HF_TOKEN\", \"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\")\n", "model_id = \"meta-llama/Llama-2-7b-hf\"\n", "\n", "dataset_name = \"tatsu-lab/alpaca\"\n" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -157,9 +154,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from datasets import load_dataset\n", "from transformers import AutoTokenizer \n", @@ -174,13 +169,13 @@ "dataset = load_dataset(dataset_name)\n", "\n", "dataset = dataset.shuffle(seed=42)\n" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "if \"validation\" not in dataset.keys():\n", " dataset[\"validation\"] = load_dataset(\n", @@ -192,7 +187,9 @@ " dataset_name,\n", " split=\"train[1%:]\"\n", " )" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -203,9 +200,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from itertools import chain\n", "from functools import partial\n", @@ -234,7 +229,9 @@ " partial(group_texts, block_size=2048),\n", " batched=True,\n", ")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -245,9 +242,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "#save data locally\n", "\n", @@ -255,7 +250,9 @@ "lm_dataset.save_to_disk(training_input_path)\n", "\n", "print(f\"Saved data to: {training_input_path}\")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -266,12 +263,10 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ + "from sagemaker import image_uris\n", "from sagemaker.modules.train.model_trainer import ModelTrainer\n", - "from sagemaker.modules.image_spec import ImageSpec, Framework, Processor\n", "from sagemaker.modules.configs import ResourceConfig\n", "\n", "env = {}\n", @@ -290,27 +285,26 @@ " volume_size_in_gb=96,\n", ")\n", "\n", - "hugging_face_image = ImageSpec(\n", - " framework=Framework.HUGGING_FACE,\n", - " processor=Processor.GPU,\n", - " version=\"4.28.1\",\n", - " base_framework_version=\"pytorch2.0.0\",\n", - " py_version=\"py310\",\n", - " distribution={\"torch_distributed\": {\"enabled\": True}}\n", - ")\n", - "\n", "model_trainer = ModelTrainer(\n", - " training_image=hugging_face_image,\n", + " training_image=image_uris.retrieve(\n", + " framework=\"huggingface\",\n", + " region=\"us-west-2\",\n", + " version=\"4.28.1\",\n", + " base_framework_version=\"pytorch2.0.0\",\n", + " image_scope=\"training\",\n", + " py_version=\"py310\",\n", + " distribution={\"torch_distributed\": {\"enabled\": True}}\n", + " ),\n", " environment=env,\n", " resource_config=resource_config\n", ")\n" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", @@ -344,7 +338,9 @@ " hyper_parameters=hyper_parameters,\n", " input_data_channels={\"dataset\": training_input_path},\n", ")" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "markdown", @@ -352,16 +348,6 @@ "source": [ "## Problems to Solve\n", "\n", - "### How can we make it easier for user to set a sagemaker distributed image?\n", - "* Would be very nice if user could just provide framework name and we somehow automatically pick latest and resolve some things from ModelTrainer. Many low level technical challanges with this due to legacy image uri configs and conditions that are not predictable from the the `json` (ie, hardcoded prefixes/substrings used for certain specific conditions when building image)\n", - "\n", - "```python\n", - "pytorch_image = ImageSpec(\"pytorch\")\n", - "```\n", - "\n", - "---\n", - "\n", - "\n", "### How can we make it easier for user to build an image?\n", "* There are challenges here because of some of the OS differences in installing some ML frameworks which depend on some lower level dependencies being installed like CUDA - https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Amazon-Linux&target_version=2023&target_type=rpm_network\n", "\n", @@ -382,9 +368,7 @@ }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "from sagemaker.modules.train.model_trainer import ModelTrainer\n", "\n", @@ -393,19 +377,21 @@ "\n", "# This does not work\n", "model_trainer = ModelTrainer(training_image=python_ecr_image)\n" - ] + ], + "outputs": [], + "execution_count": null }, { "cell_type": "code", - "execution_count": null, "metadata": {}, - "outputs": [], "source": [ "source_code_config = SourceCodeConfig(\n", " command=\"echo 'Hello World'\",\n", ")\n", "model_trainer.train(source_code_config=source_code_config)" - ] + ], + "outputs": [], + "execution_count": null } ], "metadata": { diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index cb844f95c9..032974dff7 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -50,10 +50,6 @@ DEFAULT_CONTAINER_ARGUMENTS, ) from sagemaker.modules.templates import TRAIN_SCRIPT_TEMPLATE -from sagemaker.modules.image_spec import ( - ImageSpec, - ImageScope, -) from sagemaker.modules import logger @@ -94,7 +90,7 @@ class ModelTrainer(BaseModel): algorithm_name (Optional[str]): The SageMaker marketplace algorithm name/arn to use for the training job. algorithm_name cannot be specified if training_image is specified. - training_image (Optional[Union[str, ImageSpec]]): + training_image (Optional[str]): The training image URI to use for the training job container. training_image cannot be specified if algorithm_name is specified. training_input_mode (Optional[str]): @@ -122,7 +118,7 @@ class ModelTrainer(BaseModel): input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] = None source_code_config: Optional[SourceCodeConfig] = None algorithm_name: Optional[str] = None - training_image: Optional[Union[str, ImageSpec]] = None + training_image: Optional[str] = None training_input_mode: Optional[str] = "File" training_image_config: Optional[TrainingImageConfig] = None environment: Optional[Dict[str, str]] = None @@ -197,9 +193,6 @@ def __init__(self, **data): if self.algorithm_name: self.base_name = f"{self.algorithm_name}-job" elif self.training_image: - if isinstance(self.training_image, ImageSpec): - self.base_name = f"{self.training_image.framework.value}-job" - else: self.base_name = f"{_get_repo_name_from_image(self.training_image)}-job" logger.warning(f"Base name not provided. Using default name:\n{self.base_name}") @@ -237,12 +230,6 @@ def __init__(self, **data): ) if self.training_image: - if isinstance(self.training_image, ImageSpec): - self.training_image.update_image_spec( - image_scope=ImageScope.TRAINING, - instance_type=self.resource_config.instance_type, - ) - self.training_image = self.training_image.retrieve() logger.info(f"Training image URI: {self.training_image}") def train( diff --git a/tests/unit/sagemaker/modules/test_image_spec.py b/tests/unit/sagemaker/modules/test_image_spec.py deleted file mode 100644 index a6bff4cb31..0000000000 --- a/tests/unit/sagemaker/modules/test_image_spec.py +++ /dev/null @@ -1,47 +0,0 @@ -from __future__ import absolute_import -import unittest - -from sagemaker.modules.image_spec import ImageSpec, Framework, Processor - - -class TestImageSpec(unittest.TestCase): - - def test_image_spec_update(self): - image_spec = ImageSpec(framework=Framework.HUGGING_FACE) - assert image_spec.version is None - image_spec.update_image_spec(version="v3") - assert image_spec.version == "v3" - - def test_image_spec_retrive(self): - # Asserting substrings because full string uri can change with newer versions - image_spec = ImageSpec(framework=Framework.XGBOOST) - xgboost_uri = image_spec.retrieve() - assert "dkr.ecr.us-west-2.amazonaws.com" in xgboost_uri - assert "sagemaker-xgboost" in xgboost_uri - - image_spec = ImageSpec( - framework=Framework.HUGGING_FACE, - processor=Processor.GPU, - base_framework_version="pytorch2.1.0", - ) - hugging_face_uri = image_spec.retrieve() - assert "dkr.ecr.us-west-2.amazonaws.com" in hugging_face_uri - assert "huggingface-pytorch-training" in hugging_face_uri - - image_spec = ImageSpec(framework=Framework.PYTORCH) - pytorch_uri = image_spec.retrieve() - assert "dkr.ecr.us-west-2.amazonaws.com" in pytorch_uri - assert "pytorch-training" in pytorch_uri - - image_spec = ImageSpec(framework=Framework.SKLEARN) - sklearn_uri = image_spec.retrieve() - assert "dkr.ecr.us-west-2.amazonaws.com" in sklearn_uri - assert "sagemaker-scikit-learn" in sklearn_uri - - def test_image_spec_retrive_with_version(self): - image_spec = ImageSpec(framework=Framework.XGBOOST, version="0.90-1") - xgboost_uri = image_spec.retrieve() - assert ( - xgboost_uri - == "246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:0.90-1-cpu-py3" - ) From 72e42661e2bf40f30c33530986e494797967b100 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Tue, 15 Oct 2024 14:40:15 -0700 Subject: [PATCH 10/79] Cleanup ModelTrainer (#1542) --- src/sagemaker/modules/scripts/environment.py | 1 - .../modules/scripts/intelligent_params.py | 28 ++- .../base_model_trainer.ipynb | 225 ++++++++++-------- .../basic-script-mode/custom_script.py | 64 ++--- src/sagemaker/modules/train/local_snapshot.py | 16 +- src/sagemaker/modules/train/model_trainer.py | 38 +-- .../scripts/test_intelligent_params.py | 19 +- .../modules/train/test_model_trainer.py | 51 +--- 8 files changed, 206 insertions(+), 236 deletions(-) diff --git a/src/sagemaker/modules/scripts/environment.py b/src/sagemaker/modules/scripts/environment.py index db54f001f3..4b80dcae72 100644 --- a/src/sagemaker/modules/scripts/environment.py +++ b/src/sagemaker/modules/scripts/environment.py @@ -11,7 +11,6 @@ # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """This module is used to define the environment variables for the training job container.""" - from __future__ import absolute_import from typing import Dict, Any diff --git a/src/sagemaker/modules/scripts/intelligent_params.py b/src/sagemaker/modules/scripts/intelligent_params.py index 71a94162a3..71624c3925 100644 --- a/src/sagemaker/modules/scripts/intelligent_params.py +++ b/src/sagemaker/modules/scripts/intelligent_params.py @@ -1,3 +1,18 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module is used to set intelligent parameters for all python files under the given path.""" +from __future__ import absolute_import + import argparse import json import os @@ -7,8 +22,8 @@ def set_intelligent_params(path: str) -> None: - """ - Set intelligent parameters for all python files under the given path. + """Set intelligent parameters for all python files under the given path. + For python code with comment sm_hyper_param or sm_hp_{variable_name}, the value will be found in /opt/ml/input/config/hyperparameters.json, and this function will rewrite lines with these comments. @@ -25,8 +40,7 @@ def set_intelligent_params(path: str) -> None: def rewrite_file(file_path: str, hyperparameters: dict) -> None: - """ - Rewrite a single python file with intelligent parameters. + """Rewrite a single python file with intelligent parameters. Args: file_path (str): The file path to rewrite @@ -41,8 +55,7 @@ def rewrite_file(file_path: str, hyperparameters: dict) -> None: def rewrite_line(line: str, hyperparameters: dict) -> None: - """ - Rewrite a single line of python code with intelligent parameters. + """Rewrite a single line of python code with intelligent parameters. Args: line (str): The python code to rewrite @@ -70,8 +83,7 @@ def rewrite_line(line: str, hyperparameters: dict) -> None: def get_parameter_value(variable: str, comment: str, hyperparameters: dict) -> None: - """ - Get the parameter value by the variable name and comment. + """Get the parameter value by the variable name and comment. Args: variable (str): The variable name diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index 567657f6ad..9b3e7676fd 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -2,18 +2,17 @@ "cells": [ { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "import sys, os\n", - "\n", - "from tests.integ.test_processing import image_uri\n", + "from sagemaker import image_uris\n", "\n", "# Get the absolute path of the root directory\n", "root_dir = os.path.abspath(os.path.join(os.getcwd(), '../../..'))\n", "sys.path.insert(0, root_dir)" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -28,27 +27,39 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, - "source": [ - "from sagemaker.modules.train.model_trainer import ModelTrainer\n", - "model_trainer = ModelTrainer(training_image=\"python:3.10.15-slim\")\n" - ], "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": {}, "source": [ + "from sagemaker.modules.train.model_trainer import ModelTrainer\n", "from sagemaker.modules.configs import SourceCodeConfig\n", + "from sagemaker.modules.constants import DEFAULT_INSTANCE_TYPE\n", + "\n", + "pytorch_image = image_uris.retrieve(\n", + " framework=\"pytorch\",\n", + " region=\"us-west-2\",\n", + " version=\"2.0.0\",\n", + " image_scope=\"training\",\n", + " instance_type=DEFAULT_INSTANCE_TYPE\n", + ")\n", "\n", "source_code_config = SourceCodeConfig(\n", " command=\"echo 'Hello World' && env\",\n", ")\n", - "model_trainer.train(source_code_config=source_code_config)" - ], + "model_trainer = ModelTrainer(\n", + " training_image=pytorch_image,\n", + " source_code_config=source_code_config,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "model_trainer.train()" + ] }, { "cell_type": "markdown", @@ -69,24 +80,31 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", "source_code_config = SourceCodeConfig(\n", - " source_dir=\"script-mode-basic\",\n", + " source_dir=\"basic-script-mode\",\n", " command=\"python custom_script.py\",\n", ")\n", - "model_trainer.train(\n", + "\n", + "model_trainer = ModelTrainer(\n", + " training_image=pytorch_image,\n", " source_code_config=source_code_config,\n", - " input_data_channels={\n", - " \"train\": \"script-mode-basic/data/train\",\n", - " \"test\": \"script-mode-basic/data/test\",\n", - " }\n", - ")" - ], + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "model_trainer.train()" + ] }, { "cell_type": "markdown", @@ -97,25 +115,32 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", "source_code_config = SourceCodeConfig(\n", - " source_dir=\"script-mode-basic\",\n", + " source_dir=\"basic-script-mode\",\n", " requirements=\"requirements.txt\",\n", " entry_script=\"custom_script.py\",\n", ")\n", - "model_trainer.train(\n", + "\n", + "model_trainer = ModelTrainer(\n", + " training_image=pytorch_image,\n", " source_code_config=source_code_config,\n", - " input_data_channels={\n", - " \"train\": \"script-mode-basic/data/train\",\n", - " \"test\": \"script-mode-basic/data/test\",\n", - " }\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "model_trainer.train()" + ] }, { "cell_type": "markdown", @@ -126,24 +151,24 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "!pip install \"datasets[s3]\"" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "access_token = os.environ.get(\"HF_TOKEN\", \"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\")\n", "model_id = \"meta-llama/Llama-2-7b-hf\"\n", "\n", "dataset_name = \"tatsu-lab/alpaca\"\n" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -154,7 +179,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "from datasets import load_dataset\n", "from transformers import AutoTokenizer \n", @@ -169,13 +196,13 @@ "dataset = load_dataset(dataset_name)\n", "\n", "dataset = dataset.shuffle(seed=42)\n" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "if \"validation\" not in dataset.keys():\n", " dataset[\"validation\"] = load_dataset(\n", @@ -187,9 +214,7 @@ " dataset_name,\n", " split=\"train[1%:]\"\n", " )" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -200,7 +225,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "from itertools import chain\n", "from functools import partial\n", @@ -229,9 +256,7 @@ " partial(group_texts, block_size=2048),\n", " batched=True,\n", ")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -242,7 +267,9 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "#save data locally\n", "\n", @@ -250,9 +277,7 @@ "lm_dataset.save_to_disk(training_input_path)\n", "\n", "print(f\"Saved data to: {training_input_path}\")" - ], - "outputs": [], - "execution_count": null + ] }, { "cell_type": "markdown", @@ -263,11 +288,12 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "from sagemaker import image_uris\n", "from sagemaker.modules.train.model_trainer import ModelTrainer\n", - "from sagemaker.modules.configs import ResourceConfig\n", + "from sagemaker.modules.configs import ResourceConfig, SourceCodeConfig\n", "\n", "env = {}\n", "env['FI_PROVIDER'] = 'efa'\n", @@ -278,36 +304,23 @@ "env['FI_EFA_USE_DEVICE_RDMA'] = '1'\n", "env['RDMAV_FORK_SAFE'] = '1'\n", "\n", + "instance_type = \"ml.p4d.24xlarge\"\n", "\n", "resource_config = ResourceConfig(\n", " instance_count=2,\n", - " instance_type=\"ml.p4d.24xlarge\" ,\n", + " instance_type=instance_type,\n", " volume_size_in_gb=96,\n", ")\n", "\n", - "model_trainer = ModelTrainer(\n", - " training_image=image_uris.retrieve(\n", - " framework=\"huggingface\",\n", - " region=\"us-west-2\",\n", - " version=\"4.28.1\",\n", - " base_framework_version=\"pytorch2.0.0\",\n", - " image_scope=\"training\",\n", - " py_version=\"py310\",\n", - " distribution={\"torch_distributed\": {\"enabled\": True}}\n", - " ),\n", - " environment=env,\n", - " resource_config=resource_config\n", - ")\n" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "metadata": {}, - "source": [ - "from sagemaker.modules.configs import SourceCodeConfig\n", - "\n", + "hugging_face_image = image_uris.retrieve(\n", + " framework=\"huggingface\",\n", + " base_framework_version=\"pytorch2.0.0\",\n", + " region=\"us-west-2\",\n", + " version=\"4.28.1\",\n", + " instance_type=instance_type,\n", + " image_scope=\"training\",\n", + " py_version=\"py310\",\n", + ")\n", "\n", "hyper_parameters={\n", " 'dataset_path': '/opt/ml/input/data/dataset',\n", @@ -325,22 +338,34 @@ "source_code_config = SourceCodeConfig(\n", " source_dir=\"distributed-training/scripts\",\n", " requirements=\"requirements.txt\",\n", - " command=\"torchrun --nnodes 2 \\\n", + " command=\"torchrun --nnodes $SM_HOST_COUNT \\\n", " --nproc_per_node $SM_NUM_GPUS \\\n", - " --master_addr algo-1 \\\n", - " --master_port 7777 \\\n", - " --node_rank $SM_CURRENT_HOST_INDEX \\\n", + " --master_addr $SM_MASTER_ADDR \\\n", + " --master_port $SM_MASTER_PORT \\\n", + " --node_rank $SM_CURRENT_HOST_RANK \\\n", " run_clm_no_trainer.py\",\n", ")\n", "\n", - "model_trainer.train(\n", - " source_code_config=source_code_config,\n", + "\n", + "model_trainer = ModelTrainer(\n", + " training_image=hugging_face_image,\n", + " resource_config=resource_config,\n", + " environment=env,\n", " hyper_parameters=hyper_parameters,\n", - " input_data_channels={\"dataset\": training_input_path},\n", - ")" - ], + " source_code_config=source_code_config,\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], - "execution_count": null + "source": [ + "model_trainer.train(\n", + " input_data_channels={\"dataset\": training_input_path}\n", + ")" + ] }, { "cell_type": "markdown", @@ -368,30 +393,34 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "from sagemaker.modules.train.model_trainer import ModelTrainer\n", "\n", "python_ecr_image = \"public.ecr.aws/docker/library/python:3.10.15-slim\"\n", "python_docker_image = \"python:3.10.15-slim\"\n", "\n", + "source_code_config = SourceCodeConfig(\n", + " command=\"echo 'Hello World'\",\n", + ")\n", + "\n", "# This does not work\n", - "model_trainer = ModelTrainer(training_image=python_ecr_image)\n" - ], - "outputs": [], - "execution_count": null + "model_trainer = ModelTrainer(\n", + " training_image=python_ecr_image,\n", + " source_code_config=source_code_config,\n", + ")\n" + ] }, { "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "source_code_config = SourceCodeConfig(\n", - " command=\"echo 'Hello World'\",\n", - ")\n", "model_trainer.train(source_code_config=source_code_config)" - ], - "outputs": [], - "execution_count": null + ] } ], "metadata": { diff --git a/src/sagemaker/modules/testing_notebooks/basic-script-mode/custom_script.py b/src/sagemaker/modules/testing_notebooks/basic-script-mode/custom_script.py index 8b8fdb9ec4..acacf6c19e 100644 --- a/src/sagemaker/modules/testing_notebooks/basic-script-mode/custom_script.py +++ b/src/sagemaker/modules/testing_notebooks/basic-script-mode/custom_script.py @@ -11,37 +11,10 @@ from torch.utils.data import DataLoader, TensorDataset from pytorch_model_def import get_model - logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) logger.addHandler(logging.StreamHandler(sys.stdout)) - - -def parse_args(): - """ - Parse arguments passed from the SageMaker API - to the container - """ - - parser = argparse.ArgumentParser() - - # Hyperparameters sent by the client are passed as command-line arguments to the script - parser.add_argument("--epochs", type=int, default=os.environ.get("SM_HP_EPOCHS", 1)) - parser.add_argument("--batch_size", type=int, default=os.environ.get("SM_HP_BATCH_SIZE", 64)) - parser.add_argument( - "--learning_rate", type=float, default=os.environ.get("SM_HP_LEARNING_RATE", 0.1) - ) - - # Data directories - parser.add_argument( - "--train", type=str, default=os.environ.get("SM_CHANNEL_TRAIN", "data/train") - ) - parser.add_argument("--test", type=str, default=os.environ.get("SM_CHANNEL_TEST", "data/test")) - - # Model directory: we will use the default set by SageMaker, /opt/ml/model/ - parser.add_argument("--model_dir", type=str, default=os.environ.get("SM_MODEL_DIR")) - - return parser.parse_known_args() +current_dir = os.path.dirname(os.path.abspath(__file__)) def get_train_data(train_dir): @@ -107,25 +80,33 @@ def train(): """ Train the PyTorch model """ - - x_train, y_train = get_train_data(args.train) - x_test, y_test = get_test_data(args.test) + # Directories: train, test and model + train_dir = os.path.join(current_dir, "data/train") + test_dir = os.path.join(current_dir, "data/test") + model_dir = os.environ.get("SM_MODEL_DIR", os.path.join(current_dir, "data/model")) + + # Load the training and testing data + x_train, y_train = get_train_data(train_dir) + x_test, y_test = get_test_data(test_dir) train_ds = TensorDataset(x_train, y_train) - batch_size = args.batch_size - epochs = args.epochs - learning_rate = args.learning_rate + # Training parameters - used to configure the training loop + batch_size = 64 + epochs = 1 + learning_rate = 0.1 logger.info( "batch_size = {}, epochs = {}, learning rate = {}".format(batch_size, epochs, learning_rate) ) train_dl = DataLoader(train_ds, batch_size, shuffle=True) + # Define the model, loss function and optimizer model = get_model() model = model.to(device) criterion = nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) + # Train the model for epoch in range(epochs): for x_train_batch, y_train_batch in train_dl: y = model(x_train_batch.float()) @@ -136,31 +117,30 @@ def train(): epoch += 1 logger.info(f"epoch: {epoch} -> loss: {loss}") - # evalutate on test set + # Test the model with torch.no_grad(): y = model(x_test.float()).flatten() mse = ((y - y_test) ** 2).sum() / y_test.shape[0] print("\nTest MSE:", mse.numpy()) - torch.save(model.state_dict(), args.model_dir + "/model.pth") - # PyTorch requires that the inference script must - # be in the .tar.gz model file and Step Functions SDK doesn't do this. - inference_code_path = args.model_dir + "/code/" + # Save the model + os.makedirs(model_dir, exist_ok=True) + torch.save(model.state_dict(), model_dir + "/model.pth") + inference_code_path = model_dir + "/code/" if not os.path.exists(inference_code_path): os.mkdir(inference_code_path) logger.info("Created a folder at {}!".format(inference_code_path)) - code_dir = os.environ.get("SM_CHANNEL_CODE") + code_dir = os.environ.get("SM_CHANNEL_CODE", current_dir) shutil.copy(os.path.join(code_dir, "custom_script.py"), inference_code_path) shutil.copy(os.path.join(code_dir, "pytorch_model_def.py"), inference_code_path) logger.info("Saving models files to {}".format(inference_code_path)) if __name__ == "__main__": - print("Running in the training container ...\n") + print("Running the training job ...\n") - args, _ = parse_args() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") train() diff --git a/src/sagemaker/modules/train/local_snapshot.py b/src/sagemaker/modules/train/local_snapshot.py index b697286a88..57d25dff98 100644 --- a/src/sagemaker/modules/train/local_snapshot.py +++ b/src/sagemaker/modules/train/local_snapshot.py @@ -11,6 +11,8 @@ # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """Utility function to capture local environment""" +from __future__ import absolute_import + import logging import subprocess import sys @@ -77,8 +79,8 @@ def capture_local_environment( ecr_repo_name: Optional[str] = None, boto_session: Optional[boto3.Session] = None, ): - """ - Capture all dependency packages installed in the local environment and build a docker image. + """Capture all dependency packages installed in the local environment and build a docker image. + When using this utility method, the docker daemon must be active in the environment. Please note that this is an experimental feature. This utility function is not be able to detect the package compatability between platforms. It is also not able to detect dependency @@ -209,8 +211,7 @@ def capture_local_environment( def _merge_environment_ymls(env_name: str, env_file1: str, env_file2: str, output_file: str): - """ - Merge two environment.yml files and save to a new environment.yml file. + """Merge two environment.yml files and save to a new environment.yml file. Args: env_name (str): The name of the virtual environment to be activated in the image. @@ -257,9 +258,7 @@ def _merge_environment_ymls(env_name: str, env_file1: str, env_file2: str, outpu def _merge_environment_yml_with_requirement_txt( env_name: str, env_file: str, req_txt: str, output_file: str ): - """ - Merge an environment.yml file with a requirements.txt file and save to a new - environment.yml file. + """Merge an environment.yml file with a requirements.txt file. Args: env_name (str): The name of the virtual environment to be activated in the image. @@ -302,8 +301,7 @@ def _merge_environment_yml_with_requirement_txt( def _push_image_to_ecr(image_name: str, ecr_repo_name: str, boto_session: Optional[boto3.Session]): - """ - Push the docker image to AWS ECR. + """Push the docker image to AWS ECR. Args: image_name (str): The name of the docker image. diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 032974dff7..56dc7d96e9 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -15,7 +15,7 @@ import os -from typing import Optional, List, Union, Dict +from typing import Optional, List, Union, Dict, Any from pydantic import BaseModel, ConfigDict from sagemaker_core.resources import TrainingJob @@ -93,6 +93,8 @@ class ModelTrainer(BaseModel): training_image (Optional[str]): The training image URI to use for the training job container. training_image cannot be specified if algorithm_name is specified. + For documentaion on sagemaker distributed images see: + https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths.html training_input_mode (Optional[str]): The input mode for the training job. Valid values are "Pipe", "File", "FastFile". Defaults to "File". @@ -101,7 +103,7 @@ class ModelTrainer(BaseModel): Docker registry for a training job. environment (Optional[Dict[str, str]]): The environment variables for the training job. - hyper_parameters (Optional[Dict[str, str]]): + hyper_parameters (Optional[Dict[str, Any]]): The hyperparameters for the training job. vpc_config: (Optional[VpcConfig]): The VPC configuration. @@ -122,7 +124,7 @@ class ModelTrainer(BaseModel): training_input_mode: Optional[str] = "File" training_image_config: Optional[TrainingImageConfig] = None environment: Optional[Dict[str, str]] = None - hyper_parameters: Optional[Dict[str, str]] = None + hyper_parameters: Optional[Dict[str, Any]] = None vpc_config: Optional[VpcConfig] = None def _validate_training_image_and_algorithm_name( @@ -173,10 +175,8 @@ def _validate_source_code_config(self, source_code_config: Optional[SourceCodeCo + "Must be a valid file within the 'source_dir'.", ) - def __init__(self, **data): - """Set default values for session, role, base_name, and other interdependent fields.""" - # Validate types with pydantic - super().__init__(**data) + def model_post_init(self, __context: Any): + """Post init method to perform custom validation and set default values.""" self._validate_training_image_and_algorithm_name(self.training_image, self.algorithm_name) self._validate_source_code_config(self.source_code_config) @@ -193,7 +193,7 @@ def __init__(self, **data): if self.algorithm_name: self.base_name = f"{self.algorithm_name}-job" elif self.training_image: - self.base_name = f"{_get_repo_name_from_image(self.training_image)}-job" + self.base_name = f"{_get_repo_name_from_image(self.training_image)}-job" logger.warning(f"Base name not provided. Using default name:\n{self.base_name}") if self.resource_config is None: @@ -229,15 +229,13 @@ def __init__(self, **data): f"OutputDataConfig not provided. Using default:\n{self.output_data_config}" ) + # TODO: Autodetect which image to use if source_code_config is provided if self.training_image: logger.info(f"Training image URI: {self.training_image}") def train( self, input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] = None, - source_code_config: Optional[SourceCodeConfig] = None, - hyper_parameters: Optional[Dict[str, str]] = None, - environment: Optional[Dict[str, str]] = None, wait: bool = True, logs: bool = True, ): @@ -249,13 +247,6 @@ def train( Takes a list of Channel objects or a dictionary of channel names to DataSourceType. DataSourceType can be an S3 URI string, local file path string, S3DataSource object, or FileSystemDataSource object. - source_code_config (Optional[SourceCodeConfig]): - The source code configuration. This is used to configure the source code for - running the training job. - hyper_parameters (Optional[Dict[str, str]]): - The hyperparameters for the training job. - environment (Optional[Dict[str,str]]): - The environment variables for the training job. wait (Optional[bool]): Whether to wait for the training job to complete before returning. Defaults to True. @@ -265,13 +256,6 @@ def train( """ if input_data_channels: self.input_data_channels = input_data_channels - if source_code_config: - self._validate_source_code_config(source_code_config) - self.source_code_config = source_code_config - if hyper_parameters: - self.hyper_parameters = hyper_parameters - if environment: - self.environment = environment input_data_config = [] if self.input_data_channels: @@ -324,8 +308,8 @@ def train( # Unfortunately, API requires hyperparameters to be strings string_hyper_parameters = {} - if hyper_parameters: - for hyper_parameter, value in hyper_parameters.items(): + if self.hyper_parameters: + for hyper_parameter, value in self.hyper_parameters.items(): string_hyper_parameters[hyper_parameter] = str(value) training_job = TrainingJob.create( diff --git a/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py b/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py index 95939cefae..a9d3363ee5 100644 --- a/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py +++ b/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py @@ -1,3 +1,18 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Intelligent Parameter Tests.""" +from __future__ import absolute_import + import os import pytest from sagemaker.modules.scripts.intelligent_params import ( @@ -20,7 +35,7 @@ def main(args): n_estimators = 10 # sm_hp_n_estimators state = "Not Started" # sm_hyper_param random_seed = 0.1 # sm_hyper_param - + output_dir = "local/dir/" # sm_model_dir epochs = 5 # sm_hp_epochs input_data = [0, 0, 0] # sm_hp_list_value @@ -46,7 +61,7 @@ def main(args): n_estimators = 3 # set by intelligent parameters state = "In Progress" # set by intelligent parameters random_seed = 0.1 # sm_hyper_param - + output_dir = "/opt/ml/input" # set by intelligent parameters epochs = 4 # set by intelligent parameters input_data = [1, 2, 3] # set by intelligent parameters diff --git a/tests/unit/sagemaker/modules/train/test_model_trainer.py b/tests/unit/sagemaker/modules/train/test_model_trainer.py index 5d4f57c937..9cfd27d70e 100644 --- a/tests/unit/sagemaker/modules/train/test_model_trainer.py +++ b/tests/unit/sagemaker/modules/train/test_model_trainer.py @@ -14,11 +14,11 @@ from __future__ import absolute_import import pytest -from unittest.mock import patch, MagicMock, call +from unittest.mock import patch, MagicMock from sagemaker.session import Session from sagemaker.modules.train.model_trainer import ModelTrainer -from sagemaker.modules.constants import DEFAULT_INSTANCE_TYPE, SM_CODE_LOCAL_PATH +from sagemaker.modules.constants import DEFAULT_INSTANCE_TYPE from sagemaker.modules.configs import ( ResourceConfig, StoppingCondition, @@ -159,53 +159,6 @@ def test_train_with_input_data_channels(mock_get_input_config, mock_training_job mock_training_job.create.assert_called_once() -@pytest.mark.parametrize( - "source_code_config", - [ - None, - SourceCodeConfig( - source_dir="test-data", - entry_script="train.py", - ), - SourceCodeConfig( - command="python train.py", - ), - ], - ids=[ - "no_source_code_config", - "supported_source_code_config", - "unsupported_source_code_config", - ], -) -@patch("sagemaker.modules.train.model_trainer.TrainingJob") -@patch.object(ModelTrainer, "create_input_data_channel") -@patch.object(ModelTrainer, "_validate_source_code_config") -def test_train_with_source_code_config( - mock_validate_source_code_config, - mock_create_input_data_channel, - mock_training_job, - model_trainer, - source_code_config, -): - mock_validate_source_code_config.return_value = None - mock_create_input_data_channel.return_value = None - - model_trainer.train(source_code_config=source_code_config) - - input_channel_calls = [] - if source_code_config: - if source_code_config.source_dir: - input_channel_calls.append(call("code", source_code_config.source_dir)) - mock_validate_source_code_config.assert_called_once_with(source_code_config) - input_channel_calls.append(call("sm_code", SM_CODE_LOCAL_PATH)) - else: - mock_validate_source_code_config.assert_not_called() - mock_create_input_data_channel.assert_not_called() - - mock_create_input_data_channel.assert_has_calls(input_channel_calls) - mock_training_job.create.assert_called_once() - - @pytest.mark.parametrize( "test_case", [ From 89edb6dd2976d60eee7e14c4b02d2d96712037b0 Mon Sep 17 00:00:00 2001 From: pintaoz-aws <167920275+pintaoz-aws@users.noreply.github.com> Date: Fri, 18 Oct 2024 13:43:49 -0700 Subject: [PATCH 11/79] General image builder (#1546) * General image builder * General image builder * Fix codestyle * Fix codestyle * Move location * Add warnings * Add integ tests * Fix integ test * Fix integ test * Fix region error * Add region --- .../local_snapshot.py => image_builder.py} | 246 ++++++++++++++---- tests/integ/sagemaker/modules/__init__.py | 13 + tests/integ/sagemaker/modules/environment.yml | 11 + .../integ/sagemaker/modules/requirements.txt | 3 + .../sagemaker/modules/test_image_builder.py | 38 +++ 5 files changed, 263 insertions(+), 48 deletions(-) rename src/sagemaker/modules/{train/local_snapshot.py => image_builder.py} (59%) create mode 100644 tests/integ/sagemaker/modules/__init__.py create mode 100644 tests/integ/sagemaker/modules/environment.yml create mode 100644 tests/integ/sagemaker/modules/requirements.txt create mode 100644 tests/integ/sagemaker/modules/test_image_builder.py diff --git a/src/sagemaker/modules/train/local_snapshot.py b/src/sagemaker/modules/image_builder.py similarity index 59% rename from src/sagemaker/modules/train/local_snapshot.py rename to src/sagemaker/modules/image_builder.py index 57d25dff98..e27bc7fba0 100644 --- a/src/sagemaker/modules/train/local_snapshot.py +++ b/src/sagemaker/modules/image_builder.py @@ -10,10 +10,12 @@ # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. -"""Utility function to capture local environment""" +"""Utility functions to build docker image""" from __future__ import absolute_import import logging +import os +import shutil import subprocess import sys from typing import Optional @@ -24,12 +26,12 @@ logger = logging.getLogger(__name__) -REQUIREMENT_TXT_PATH = "/tmp/requirements.txt" -ENVIRONMENT_YML_PATH = "/tmp/environment.yml" +REQUIREMENT_TXT_PATH = "/tmp/{image_name}/requirements.txt" +ENVIRONMENT_YML_PATH = "/tmp/{image_name}/environment.yml" DOCKERFILE_PATH = "/tmp/Dockerfile" CONDA_DOCKERFILE_TEMPLATE = """ -FROM {base_image_name} +FROM {base_image} ADD environment.yml . # Install prerequisites for conda @@ -55,31 +57,126 @@ """ PIP_DOCKERFILE_TEMPLATE = """ -FROM {base_image_name} +FROM {base_image} + +# Install the latest Python3 if the base image doesn't have python pre-installed +RUN python --version || {{ \ + apt-get update; \ + apt-get install -y python3-full python3-pip; \ + }} + ADD requirements.txt . # Create a virtual environment -RUN python -m venv {env_name} +RUN python3 -m venv {env_name} # Activate the virtual environment -RUN . {env_name}/bin/activate +ENV PATH="{env_name}/bin:$PATH" RUN pip install --no-cache-dir -r requirements.txt """ +BASE_IMAGE_TEMPLATE = """ +FROM {base_image} +""" + + +def build_image( + image_name: str = "sm-custom-image", + env_name: str = "sm_custom_env", + deploy_to_ecr: bool = False, + base_image: Optional[str] = "ubuntu:latest", + dependency_file: Optional[str] = None, + ecr_repo_name: Optional[str] = None, + boto_session: Optional[boto3.Session] = None, + region: Optional[str] = None, +) -> Optional[str]: + """WARNING: This function is expremental and not intended for production use. + + Build a docker image with the given base image and dependencies. + + When using this utility method, the docker daemon must be active in the environment. + + Args: + image_name (str): The name of the docker image. + env_name (str): The name of the virtual environment to be activated in the image, + defaults to "sm_custom_env". + deploy_to_ecr (bool): Whether to deploy the docker image to AWS ECR, defaults to False. + If set to True, the AWS credentials must be configured in the environment. + base_image (Optional[str]): The base Docker image, can be an AWS ECR image URI, defaults + to ubuntu:latest. + dependency_file (Optional[str]): Either the path to a dependencies file (conda + environment.yml OR pip requirements.txt file). + ecr_repo_name (Optional[str]): The AWS ECR repo to push the docker image. If not specified, + it will use image_name as the ECR repo name. This parameter is only valid when + deploy_to_ecr is True. + boto_session (Optional[boto3.Session]): The boto3 session with AWS account info. If not + provided, a new boto session will be created. + region (Optional[str]): The AWS region. + + Returns: + Optional[str]: If deploy_to_ecr set to True, return the AWS ECR uri of the image. + + Exceptions: + docker.errors.DockerException: Error while fetching server API version: + The docker engine is not running in your environment. + docker.errors.BuildError: The docker failed to build the image. The most likely reason is: + 1) Some packages are not supported in the base image. + botocore.exceptions.ClientError: AWS credentials are not configured. + """ + if ".dkr.ecr." in base_image: + # If the base image is on AWS ECR, need to authenticate first + _docker_ecr_login(boto_session, region) + + path = f"/tmp/{image_name}" + if os.path.exists(path): + shutil.rmtree(path) + os.mkdir(path) + environment_yml_path = ENVIRONMENT_YML_PATH.format(image_name=image_name) + requirement_txt_path = REQUIREMENT_TXT_PATH.format(image_name=image_name) + + if dependency_file: + if dependency_file.endswith(".yml"): + shutil.copy(dependency_file, environment_yml_path) + dockerfile_contents = CONDA_DOCKERFILE_TEMPLATE.format( + base_image=base_image, + env_name=env_name, + ) + elif dependency_file.endswith(".txt"): + shutil.copy(dependency_file, requirement_txt_path) + dockerfile_contents = PIP_DOCKERFILE_TEMPLATE.format( + base_image=base_image, + env_name=env_name, + ) + else: + raise ValueError( + "The dependency file must be a conda " + "environment.yml file or a pip requirements.txt file." + ) + else: + dockerfile_contents = BASE_IMAGE_TEMPLATE.format(base_image=base_image) + + _build_docker_image(image_name, dockerfile_contents) + if deploy_to_ecr: + return _push_image_to_ecr(image_name, ecr_repo_name, boto_session, region) + return None + def capture_local_environment( image_name: str = "sm-local-capture", env_name: str = "saved_local_env", package_manager: str = "pip", deploy_to_ecr: bool = False, - base_image_name: Optional[str] = None, + base_image: Optional[str] = None, job_conda_env: Optional[str] = None, additional_dependencies: Optional[str] = None, ecr_repo_name: Optional[str] = None, boto_session: Optional[boto3.Session] = None, -): - """Capture all dependency packages installed in the local environment and build a docker image. + region: Optional[str] = None, +) -> Optional[str]: + """WARNING: This function is expremental and not intended for production use. + + Capture all dependency packages installed in the local environment and build a docker image. When using this utility method, the docker daemon must be active in the environment. Please note that this is an experimental feature. This utility function is not be able to @@ -93,8 +190,8 @@ def capture_local_environment( package_manager (str): The package manager, must be one of "conda" or "pip". deploy_to_ecr (bool): Whether to deploy the docker image to AWS ECR, defaults to False. If set to True, the AWS credentials must be configured in the environment. - base_image_name (Optional[str]): If provided will be used as the base image, else the - utility will evaluate from local environment in following manner: + base_image (Optional[str]): If provided will be used as the base image, can be an AWS ECR + image URI, else the utility will evaluate from local environment in following manner: 1. If package manager is conda, it will use ubuntu:latest. 2. If package manager is pip, it is resolved to base python image with the same python version as the environment running the local code. @@ -104,12 +201,16 @@ def capture_local_environment( additional_dependencies (Optional[str]): Either the path to a dependencies file (conda environment.yml OR pip requirements.txt file). Regardless of this setting utility will automatically generate the dependencies file corresponding to the current active - environment’s snapshot. In addition to this, additional dependencies is configurable. + environment's snapshot. In addition to this, additional dependencies is configurable. ecr_repo_name (Optional[str]): The AWS ECR repo to push the docker image. If not specified, it will use image_name as the ECR repo name. This parameter is only valid when deploy_to_ecr is True. boto_session (Optional[boto3.Session]): The boto3 session with AWS account info. If not provided, a new boto session will be created. + region (Optional[str]): The AWS region. + + Returns: + Optional[str]: If deploy_to_ecr set to True, return the AWS ECR uri of the image. Exceptions: docker.errors.DockerException: Error while fetching server API version: @@ -119,17 +220,23 @@ def capture_local_environment( between your local environment and additional dependencies. botocore.exceptions.ClientError: AWS credentials are not configured. """ + path = f"/tmp/{image_name}" + if os.path.exists(path): + shutil.rmtree(path) + os.mkdir(path) + environment_yml_path = ENVIRONMENT_YML_PATH.format(image_name=image_name) + requirement_txt_path = REQUIREMENT_TXT_PATH.format(image_name=image_name) if package_manager == "conda": if job_conda_env: subprocess.run( - f"conda env export -n {job_conda_env} > {ENVIRONMENT_YML_PATH} --no-builds", + f"conda env export -n {job_conda_env} > {environment_yml_path} --no-builds", shell=True, check=True, ) else: subprocess.run( - f"conda env export > {ENVIRONMENT_YML_PATH} --no-builds", shell=True, check=True + f"conda env export > {environment_yml_path} --no-builds", shell=True, check=True ) if additional_dependencies: @@ -143,26 +250,29 @@ def capture_local_environment( if additional_dependencies.endswith(".yml"): _merge_environment_ymls( env_name, - ENVIRONMENT_YML_PATH, + environment_yml_path, additional_dependencies, - ENVIRONMENT_YML_PATH, + environment_yml_path, ) elif additional_dependencies.endswith(".txt"): _merge_environment_yml_with_requirement_txt( env_name, - ENVIRONMENT_YML_PATH, + environment_yml_path, additional_dependencies, - ENVIRONMENT_YML_PATH, + environment_yml_path, ) - if not base_image_name: - base_image_name = "ubuntu:latest" + if not base_image: + base_image = "ubuntu:latest" + elif ".dkr.ecr." in base_image: + # If the base image is on AWS ECR, need to authenticate first + _docker_ecr_login(boto_session, region) dockerfile_contents = CONDA_DOCKERFILE_TEMPLATE.format( - base_image_name=base_image_name, + base_image=base_image, env_name=env_name, ) elif package_manager == "pip": - subprocess.run(f"pip list --format=freeze > {REQUIREMENT_TXT_PATH}", shell=True, check=True) + subprocess.run(f"pip list --format=freeze > {requirement_txt_path}", shell=True, check=True) if additional_dependencies: if not additional_dependencies.endswith(".txt"): @@ -171,15 +281,15 @@ def capture_local_environment( ) with open(additional_dependencies, "r") as f: additional_requirements = f.read() - with open(REQUIREMENT_TXT_PATH, "a") as f: + with open(requirement_txt_path, "a") as f: f.write(additional_requirements) - logger.info("Merged requirements file saved to %s", REQUIREMENT_TXT_PATH) + logger.info("Merged requirements file saved to %s", requirement_txt_path) - if not base_image_name: + if not base_image: version = sys.version_info - base_image_name = f"python:{version.major}.{version.minor}.{version.micro}" + base_image = f"python:{version.major}.{version.minor}.{version.micro}" dockerfile_contents = PIP_DOCKERFILE_TEMPLATE.format( - base_image_name=base_image_name, + base_image=base_image, env_name=env_name, ) @@ -189,25 +299,11 @@ def capture_local_environment( "Use conda or pip as the package manager." ) - # Create the Dockerfile - with open(DOCKERFILE_PATH, "w") as f: - f.write(dockerfile_contents) - - client = docker.from_env() - _, logs = client.images.build( - path="/tmp", - dockerfile=DOCKERFILE_PATH, - rm=True, - tag=image_name, - ) - for log in logs: - logger.info(log.get("stream", "").strip()) - logger.info("Docker image %s built successfully", image_name) + _build_docker_image(image_name, dockerfile_contents) if deploy_to_ecr: - if boto_session is None: - boto_session = boto3.Session() - _push_image_to_ecr(image_name, ecr_repo_name, boto_session) + return _push_image_to_ecr(image_name, ecr_repo_name, boto_session, region) + return None def _merge_environment_ymls(env_name: str, env_file1: str, env_file2: str, output_file: str): @@ -300,16 +396,68 @@ def _merge_environment_yml_with_requirement_txt( logger.info("Merged environment file saved to '%s'", output_file) -def _push_image_to_ecr(image_name: str, ecr_repo_name: str, boto_session: Optional[boto3.Session]): +def _build_docker_image(image_name: str, dockerfile_contents: str): + """Build the Docker image locally. + + Args: + image_name (str): The name of the docker image. + dockerfile_contents (str): The content of Dockerfile. + """ + # Create the Dockerfile + with open(DOCKERFILE_PATH, "w") as f: + f.write(dockerfile_contents) + + client = docker.from_env() + _, logs = client.images.build( + path=f"/tmp/{image_name}", + dockerfile=DOCKERFILE_PATH, + rm=True, + tag=image_name, + ) + for log in logs: + logger.info(log.get("stream", "").strip()) + logger.info("Docker image %s built successfully", image_name) + + +def _docker_ecr_login(boto_session: Optional[boto3.Session], region: Optional[str]): + """Authenticate Docker with AWS ECR credentials + + Args: + boto_session (Optional[boto3.Session]): The boto3 session with AWS account info. If not + provided, a new boto session will be created. + region (Optional[str]): The AWS region. + """ + if boto_session is None: + boto_session = boto3.Session(region_name=region) + region = boto_session.region_name or "us-west-2" + aws_account_id = boto_session.client("sts", region_name=region).get_caller_identity()["Account"] + docker_login_cmd = ( + f"aws ecr get-login-password --region {region} " + f"| docker login --username AWS --password-stdin {aws_account_id}.dkr.ecr.{region}.amazonaws.com" + ) + subprocess.run(docker_login_cmd, shell=True, check=True) + + +def _push_image_to_ecr( + image_name: str, + ecr_repo_name: str, + boto_session: Optional[boto3.Session], + region: Optional[str], +): """Push the docker image to AWS ECR. Args: image_name (str): The name of the docker image. ecr_repo_name (str): The AWS ECR repo to push the docker image. + boto_session (Optional[boto3.Session]): The boto3 session with AWS account info. If not + provided, a new boto session will be created. + region (Optional[str]): The AWS region. """ - region = boto_session.region_name + if boto_session is None: + boto_session = boto3.Session(region_name=region) + region = boto_session.region_name or "us-west-2" aws_account_id = boto_session.client("sts", region_name=region).get_caller_identity()["Account"] - ecr_client = boto3.client("ecr") + ecr_client = boto_session.client("ecr", region_name=region) # Authenticate Docker with ECR registry_url = f"{aws_account_id}.dkr.ecr.{region}.amazonaws.com" @@ -336,3 +484,5 @@ def _push_image_to_ecr(image_name: str, ecr_repo_name: str, boto_session: Option subprocess.run(docker_push_cmd, shell=True, check=True) logger.info("Image %s pushed to %s", image_name, ecr_image_uri) + + return ecr_image_uri diff --git a/tests/integ/sagemaker/modules/__init__.py b/tests/integ/sagemaker/modules/__init__.py new file mode 100644 index 0000000000..9d8bffee3f --- /dev/null +++ b/tests/integ/sagemaker/modules/__init__.py @@ -0,0 +1,13 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Placeholder docstring""" diff --git a/tests/integ/sagemaker/modules/environment.yml b/tests/integ/sagemaker/modules/environment.yml new file mode 100644 index 0000000000..649853d6cc --- /dev/null +++ b/tests/integ/sagemaker/modules/environment.yml @@ -0,0 +1,11 @@ +channels: + - defaults +dependencies: + - pip=24.2 + - python=3.12.4 + - setuptools=75.1.0 + - sqlite=3.45.3 + - pip: + - boto3==1.35.31 + - botocore==1.35.31 + - requests==2.32.3 diff --git a/tests/integ/sagemaker/modules/requirements.txt b/tests/integ/sagemaker/modules/requirements.txt new file mode 100644 index 0000000000..3142f3de01 --- /dev/null +++ b/tests/integ/sagemaker/modules/requirements.txt @@ -0,0 +1,3 @@ +numpy +sagemaker +scipy diff --git a/tests/integ/sagemaker/modules/test_image_builder.py b/tests/integ/sagemaker/modules/test_image_builder.py new file mode 100644 index 0000000000..7e4b5de8fa --- /dev/null +++ b/tests/integ/sagemaker/modules/test_image_builder.py @@ -0,0 +1,38 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module contains code to test image builder""" +from __future__ import absolute_import + +import os + +from sagemaker.modules.image_builder import build_image + + +def test_build_public_image_locally(): + build_image(image_name="python_310", base_image="python:3.10") + + +def test_build_with_dependency_file(): + dependency_file_path = os.getcwd() + "/tests/integ/sagemaker/modules/requirements.txt" + build_image(image_name="ubuntu_with_dependencies", dependency_file=dependency_file_path) + + +def test_build_image_and_push_to_ecr(): + dependency_file_path = os.getcwd() + "/tests/integ/sagemaker/modules/environment.yml" + build_image( + image_name="ecr_test_image", + dependency_file=dependency_file_path, + base_image="debian", + deploy_to_ecr=True, + ecr_repo_name="image_builder_integ_test", + ) From b40a499a32906aa6916b2ffd7bcaa442c1ae8c1a Mon Sep 17 00:00:00 2001 From: Gokul Anantha Narayanan <166456257+nargokul@users.noreply.github.com> Date: Mon, 21 Oct 2024 14:45:00 -0700 Subject: [PATCH 12/79] Latest Container Image (#1545) * Latest Container Image * Test Fixes * Parameterized tests and some logic updates * Test fixes * Move to Image URI * Fixes for unit test * Fixes for unit test * Fix codestyle error checks --- src/sagemaker/image_uris.py | 256 ++++++++++++++---- .../container_drivers/distributed_runner.json | 1 + .../train/container_drivers/sm_train.sh | 64 +++++ .../train/container_drivers/sourcecode.json | 1 + .../image_uris/test_latest_container_image.py | 110 ++++++++ 5 files changed, 373 insertions(+), 59 deletions(-) create mode 100644 src/sagemaker/modules/train/container_drivers/distributed_runner.json create mode 100644 src/sagemaker/modules/train/container_drivers/sm_train.sh create mode 100644 src/sagemaker/modules/train/container_drivers/sourcecode.json create mode 100644 tests/unit/sagemaker/image_uris/test_latest_container_image.py diff --git a/src/sagemaker/image_uris.py b/src/sagemaker/image_uris.py index dd7012b2f2..710fdc8a3a 100644 --- a/src/sagemaker/image_uris.py +++ b/src/sagemaker/image_uris.py @@ -17,7 +17,7 @@ import logging import os import re -from typing import Optional +from typing import Optional, Tuple from packaging.version import Version from sagemaker import utils @@ -52,28 +52,28 @@ @override_pipeline_parameter_var def retrieve( - framework, - region, - version=None, - py_version=None, - instance_type=None, - accelerator_type=None, - image_scope=None, - container_version=None, - distribution=None, - base_framework_version=None, - training_compiler_config=None, - model_id=None, - model_version=None, - hub_arn=None, - tolerate_vulnerable_model=False, - tolerate_deprecated_model=False, - sdk_version=None, - inference_tool=None, - serverless_inference_config=None, - sagemaker_session=DEFAULT_JUMPSTART_SAGEMAKER_SESSION, - config_name=None, - model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS, + framework, + region, + version=None, + py_version=None, + instance_type=None, + accelerator_type=None, + image_scope=None, + container_version=None, + distribution=None, + base_framework_version=None, + training_compiler_config=None, + model_id=None, + model_version=None, + hub_arn=None, + tolerate_vulnerable_model=False, + tolerate_deprecated_model=False, + sdk_version=None, + inference_tool=None, + serverless_inference_config=None, + sagemaker_session=DEFAULT_JUMPSTART_SAGEMAKER_SESSION, + config_name=None, + model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS, ) -> str: """Retrieves the ECR URI for the Docker image matching the given arguments. @@ -250,10 +250,10 @@ def retrieve( if config.get("version_aliases").get(original_version): _version = config.get("version_aliases")[original_version] if ( - config.get("versions", {}) - .get(_version, {}) - .get("version_aliases", {}) - .get(base_framework_version, {}) + config.get("versions", {}) + .get(_version, {}) + .get("version_aliases", {}) + .get(base_framework_version, {}) ): _base_framework_version = config.get("versions")[_version]["version_aliases"][ base_framework_version @@ -290,16 +290,16 @@ def retrieve( def _get_image_tag( - container_version, - distribution, - final_image_scope, - framework, - inference_tool, - instance_type, - processor, - py_version, - tag_prefix, - version, + container_version, + distribution, + final_image_scope, + framework, + inference_tool, + instance_type, + processor, + py_version, + tag_prefix, + version, ): """Return image tag based on framework, container, and compute configuration(s).""" instance_type_family = utils.get_instance_type_family(instance_type) @@ -311,8 +311,8 @@ def _get_image_tag( "instance type", ) if ( - instance_type_family in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY - or final_image_scope == INFERENCE_GRAVITON + instance_type_family in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY + or final_image_scope == INFERENCE_GRAVITON ): version_to_arm64_tag_mapping = { "xgboost": { @@ -330,7 +330,7 @@ def _get_image_tag( tag = _format_tag(tag_prefix, processor, py_version, container_version, inference_tool) if instance_type is not None and _should_auto_select_container_version( - instance_type, distribution + instance_type, distribution ): container_versions = { "tensorflow-2.3-gpu-py37": "cu110-ubuntu18.04-v3", @@ -398,7 +398,7 @@ def _validate_instance_deprecation(framework, instance_type, version): """Check if instance type is deprecated for a certain framework with a certain version""" if utils.get_instance_type_family(instance_type) == "p2": if (framework == "pytorch" and Version(version) >= Version("1.13")) or ( - framework == "tensorflow" and Version(version) >= Version("2.12") + framework == "tensorflow" and Version(version) >= Version("2.12") ): raise ValueError( "P2 instances have been deprecated for sagemaker jobs starting PyTorch 1.13 and TensorFlow 2.12" @@ -411,17 +411,17 @@ def _validate_for_suppported_frameworks_and_instance_type(framework, instance_ty """Validate if framework is supported for the instance_type""" # Validate for Trainium allowed frameworks if ( - instance_type is not None - and "trn" in instance_type - and framework not in TRAINIUM_ALLOWED_FRAMEWORKS + instance_type is not None + and "trn" in instance_type + and framework not in TRAINIUM_ALLOWED_FRAMEWORKS ): _validate_framework(framework, TRAINIUM_ALLOWED_FRAMEWORKS, "framework", "Trainium") # Validate for Graviton allowed frameowrks if ( - instance_type is not None - and utils.get_instance_type_family(instance_type) in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY - and framework not in GRAVITON_ALLOWED_FRAMEWORKS + instance_type is not None + and utils.get_instance_type_family(instance_type) in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY + and framework not in GRAVITON_ALLOWED_FRAMEWORKS ): _validate_framework(framework, GRAVITON_ALLOWED_FRAMEWORKS, "framework", "Graviton") @@ -436,8 +436,8 @@ def config_for_framework(framework): def _get_final_image_scope(framework, instance_type, image_scope): """Return final image scope based on provided framework and instance type.""" if ( - framework in GRAVITON_ALLOWED_FRAMEWORKS - and utils.get_instance_type_family(instance_type) in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY + framework in GRAVITON_ALLOWED_FRAMEWORKS + and utils.get_instance_type_family(instance_type) in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY ): return INFERENCE_GRAVITON if image_scope is None and framework in (XGBOOST_FRAMEWORK, SKLEARN_FRAMEWORK): @@ -635,16 +635,16 @@ def _format_tag(tag_prefix, processor, py_version, container_version, inference_ @override_pipeline_parameter_var def get_training_image_uri( - region, - framework, - framework_version=None, - py_version=None, - image_uri=None, - distribution=None, - compiler_config=None, - tensorflow_version=None, - pytorch_version=None, - instance_type=None, + region, + framework, + framework_version=None, + py_version=None, + image_uri=None, + distribution=None, + compiler_config=None, + tensorflow_version=None, + pytorch_version=None, + instance_type=None, ) -> str: """Retrieves the image URI for training. @@ -746,3 +746,141 @@ def get_base_python_image_uri(region, py_version="310") -> str: repo_and_tag = repo + ":" + version return ECR_URI_TEMPLATE.format(registry=registry, hostname=hostname, repository=repo_and_tag) + + +def get_latest_container_image(framework: str, + image_scope: Optional[str] = None, + instance_type: Optional[str] = None, + py_version: Optional[str] = None, + region: str = "us-west-2", + version: Optional[str] = None, + accelerator_type=None, + container_version=None, + distribution=None, + base_framework_version=None, + training_compiler_config=None, + model_id=None, + model_version=None, + hub_arn=None, + sdk_version=None, + inference_tool=None, + serverless_inference_config=None, + config_name=None, + ) -> Tuple[str, str]: + """Retrieves the latest container image URI + Args: + framework (str): The name of the framework or algorithm. + image_scope (str): The image type, i.e. what it is used for. + Valid values: "training", "inference", "inference_graviton", "eia". + If ``accelerator_type`` is set, ``image_scope`` is ignored. + region (str): The AWS region. + version (str): The framework or algorithm version. This is required if there is + more than one supported version for the given framework or algorithm. + py_version (str): The Python version. This is required if there is + more than one supported Python version for the given framework version. + instance_type (str): The SageMaker instance type. For supported types, see + https://aws.amazon.com/sagemaker/pricing. This is required if + there are different images for different processor types. + accelerator_type (str): Elastic Inference accelerator type. For more, see + https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html. + container_version (str): the version of docker image. + Ideally the value of parameter should be created inside the framework. + For custom use, see the list of supported container versions: + https://github.com/aws/deep-learning-containers/blob/master/available_images.md + (default: None). + distribution (dict): A dictionary with information on how to run distributed training + training_compiler_config (:class:`~sagemaker.training_compiler.TrainingCompilerConfig`): + A configuration class for the SageMaker Training Compiler + (default: None). + model_id (str): The JumpStart model ID for which to retrieve the image URI + (default: None). + model_version (str): The version of the JumpStart model for which to retrieve the + image URI (default: None). + hub_arn (str): The arn of the SageMaker Hub for which to retrieve + model details from. (Default: None). + sdk_version (str): the version of python-sdk that will be used in the image retrieval. + (default: None). + inference_tool (str): the tool that will be used to aid in the inference. + Valid values: "neuron, neuronx, None" + (default: None). + serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig): + Specifies configuration related to serverless endpoint. Instance type is + not provided in serverless inference. So this is used to determine processor type. + config_name (Optional[str]): Name of the JumpStart Model config to apply. (Default: None). + """ + try: + framework_config = config_for_framework(framework) + except FileNotFoundError: + raise ValueError("Invalid framework {}".format(framework)) + + if not framework_config: + raise ValueError("Invalid framework {}".format(framework)) + + if not version: + version = _fetch_latest_version_from_config(framework_config, image_scope) + image_uri = retrieve(framework=framework, + region=region, + version=version, + instance_type=instance_type, + py_version=py_version, + accelerator_type=accelerator_type, + image_scope=image_scope, + container_version=container_version, + distribution=distribution, + base_framework_version=base_framework_version, + training_compiler_config=training_compiler_config, + model_id=model_id, + model_version=model_version, + hub_arn=hub_arn, + sdk_version=sdk_version, + inference_tool=inference_tool, + serverless_inference_config=serverless_inference_config, + config_name=config_name + ) + return image_uri, version + + +def _fetch_latest_version_from_config(framework_config: dict, + image_scope: Optional[str] = None) -> Optional[str]: + """ Helper function to fetch the latest version as a string from a framework's config + Args: + framework_config (dict): A framework config dict. + image_scope (str): Scope of the image, eg: training, inference + Returns: + Version string if latest version found else None + """ + if image_scope in framework_config: + if image_scope_config := framework_config[image_scope]: + if "version_aliases" in image_scope_config: + if "latest" in image_scope_config["version_aliases"]: + return image_scope_config["version_aliases"]["latest"] + top_version = None + bottom_version = None + + if "versions" in framework_config: + versions = list(framework_config["versions"].keys()) + top_version = versions[0] + bottom_version = versions[-1] + if top_version == "latest" or bottom_version == "latest": + return None + elif (image_scope is not None and image_scope in framework_config + and "versions" in framework_config[image_scope]): + versions = list(framework_config[image_scope]["versions"].keys()) + top_version = versions[0] + bottom_version = versions[-1] + elif "processing" in framework_config and "versions" in framework_config["processing"]: + versions = list(framework_config["processing"]["versions"].keys()) + top_version = versions[0] + bottom_version = versions[-1] + + if top_version and bottom_version: + if top_version.endswith(".x") or bottom_version.endswith(".x"): + top_number = int(top_version[:-2]) + bottom_number = int(bottom_version[:-2]) + max_version = max(top_number, bottom_number) + return f"{max_version}.x" + if Version(top_version) >= Version(bottom_version): + return top_version + return bottom_version + + return None diff --git a/src/sagemaker/modules/train/container_drivers/distributed_runner.json b/src/sagemaker/modules/train/container_drivers/distributed_runner.json new file mode 100644 index 0000000000..a90706c134 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/distributed_runner.json @@ -0,0 +1 @@ +{"mpi_additional_options": ["-x", "MASTER_ADDR=algo-1", "-x", "MASTER_PORT=7777"], "_type": "mpi"} \ No newline at end of file diff --git a/src/sagemaker/modules/train/container_drivers/sm_train.sh b/src/sagemaker/modules/train/container_drivers/sm_train.sh new file mode 100644 index 0000000000..cc3915c887 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/sm_train.sh @@ -0,0 +1,64 @@ + +#!/bin/bash +set -e +echo "Starting training script" + +handle_error() { + EXIT_STATUS=$? + echo "An error occurred with exit code $EXIT_STATUS" + if [ ! -s /opt/ml/output/failure ]; then + echo "Training Execution failed. For more details, see CloudWatch logs at 'aws/sagemaker/TrainingJobs'. +TrainingJob - $TRAINING_JOB_NAME" >> /opt/ml/output/failure + fi + exit $EXIT_STATUS +} + +check_python() { + if command -v python3 &>/dev/null; then + SM_PYTHON_CMD="python3" + SM_PIP_CMD="pip3" + echo "Found python3" + elif command -v python &>/dev/null; then + SM_PYTHON_CMD="python" + SM_PIP_CMD="pip" + echo "Found python" + else + echo "Python may not be installed" + return 1 + fi +} + +trap 'handle_error' ERR + +check_python + +$SM_PYTHON_CMD --version + +echo "/opt/ml/input/config/resourceconfig.json:" +cat /opt/ml/input/config/resourceconfig.json +echo + +echo "/opt/ml/input/config/inputdataconfig.json:" +cat /opt/ml/input/config/inputdataconfig.json +echo + +echo "/opt/ml/input/data/sm_drivers/sourcecode.json" +cat /opt/ml/input/data/sm_drivers/sourcecode.json +echo + +echo "/opt/ml/input/data/sm_drivers/distributed_runner.json" +cat /opt/ml/input/data/sm_drivers/distributed_runner.json +echo + +echo "Setting up environment variables" +$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/scripts/environment.py +source /opt/ml/input/data/sm_drivers/scripts/sm_training.env + +cd /opt/ml/input/data/sm_code +$SM_PIP_CMD install -r requirements.txt + +echo "Running MPI driver" +$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/mpi_driver.py + + +echo "Training Container Execution Completed" diff --git a/src/sagemaker/modules/train/container_drivers/sourcecode.json b/src/sagemaker/modules/train/container_drivers/sourcecode.json new file mode 100644 index 0000000000..bee6559762 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/sourcecode.json @@ -0,0 +1 @@ +{"source_dir": "distributed-training/scripts", "requirements": "requirements.txt", "entry_script": "run_clm_no_trainer.py"} \ No newline at end of file diff --git a/tests/unit/sagemaker/image_uris/test_latest_container_image.py b/tests/unit/sagemaker/image_uris/test_latest_container_image.py new file mode 100644 index 0000000000..0e4c337160 --- /dev/null +++ b/tests/unit/sagemaker/image_uris/test_latest_container_image.py @@ -0,0 +1,110 @@ +import json +import unittest +from unittest.mock import patch + +import pytest + +from src.sagemaker.image_uris import get_latest_container_image + + +class TestImageUtils(unittest.TestCase): + + @patch('src.sagemaker.image_uris.config_for_framework') + def test_get_latest_container_image_invalid_framework(self, + mock_config_for_framework): + mock_config_for_framework.side_effect = FileNotFoundError + + with self.assertRaises(ValueError) as e: + get_latest_container_image("xgboost", "inference") + assert "No framework config for framework" in str(e.exception) + + @patch('src.sagemaker.image_uris.config_for_framework') + def test_get_latest_container_image_no_framework(self, + mock_config_for_framework): + mock_config_for_framework.return_value = {} + + with self.assertRaises(ValueError) as e: + get_latest_container_image("xgboost", "inference") + assert "No framework config for framework" in str(e.exception) + + +@pytest.mark.parametrize("framework", ['object-detection', + 'instance_gpu_info', + 'object2vec', + 'pytorch', + 'djl-lmi', + 'mxnet', + 'debugger', + 'data-wrangler', + 'spark', + 'blazingtext', + 'pytorch-neuron', + 'forecasting-deepar', + 'huggingface-neuron', + 'ntm', + 'neo-mxnet', + 'image-classification', + 'xgboost', + 'autogluon', + 'sparkml-serving', + 'clarify', + 'inferentia-pytorch', + 'neo-tensorflow', + 'huggingface-tei-cpu', + 'huggingface', + 'sagemaker-tritonserver', + 'pytorch-smp', + 'knn', + 'linear-learner', + 'model-monitor', + 'ray-tensorflow', + 'djl-neuronx', + 'huggingface-llm-neuronx', + 'image-classification-neo', + 'lda', + 'stabilityai', + 'ray-pytorch', + 'chainer', + 'coach-mxnet', + 'pca', + 'sagemaker-geospatial', + 'djl-tensorrtllm', + 'huggingface-training-compiler', + 'pytorch-training-compiler', + 'vw', + 'huggingface-neuronx', + 'ipinsights', + 'detailed-profiler', + 'inferentia-tensorflow', + 'semantic-segmentation', + 'inferentia-mxnet', + 'xgboost-neo', + 'neo-pytorch', + 'djl-deepspeed', + 'djl-fastertransformer', + 'sklearn', + 'tensorflow', + 'randomcutforest', + 'huggingface-llm', + 'factorization-machines', + 'huggingface-tei', + 'coach-tensorflow', + 'seq2seq', + 'kmeans', + 'sagemaker-base-python']) +@patch('src.sagemaker.image_uris.config_for_framework') +@patch('src.sagemaker.image_uris.retrieve') +def test_get_latest_container_image_parameterized(mock_image_retrieve, + mock_config_for_framework, + framework): + file_path = f"src/sagemaker/image_uri_config/{framework}.json" + with open(file_path, 'r') as json_file: + config_for_framework = json.load(json_file) + + mock_config_for_framework.return_value = config_for_framework + mock_image_retrieve.return_value = "latest-image" + image, version = get_latest_container_image(framework=framework, + image_scope="inference", + instance_type="ml.c4.xlarge", + region="us-east-1") + assert image == "latest-image" From c3f432c7e48385dc2661234fdf1b527ac1605fa9 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Wed, 23 Oct 2024 22:39:46 -0700 Subject: [PATCH 13/79] Cleanup ModelTrainer code (#1552) --- src/sagemaker/image_uris.py | 210 +++++++++--------- src/sagemaker/modules/scripts/environment.py | 37 ++- .../modules/scripts/intelligent_params.py | 9 +- .../base_model_trainer.ipynb | 25 +-- .../scripts/requirements.txt | 4 +- .../scripts/run_clm_no_trainer.py | 33 ++- .../image_uris/test_latest_container_image.py | 179 ++++++++------- 7 files changed, 261 insertions(+), 236 deletions(-) diff --git a/src/sagemaker/image_uris.py b/src/sagemaker/image_uris.py index 710fdc8a3a..4a863765a5 100644 --- a/src/sagemaker/image_uris.py +++ b/src/sagemaker/image_uris.py @@ -52,28 +52,28 @@ @override_pipeline_parameter_var def retrieve( - framework, - region, - version=None, - py_version=None, - instance_type=None, - accelerator_type=None, - image_scope=None, - container_version=None, - distribution=None, - base_framework_version=None, - training_compiler_config=None, - model_id=None, - model_version=None, - hub_arn=None, - tolerate_vulnerable_model=False, - tolerate_deprecated_model=False, - sdk_version=None, - inference_tool=None, - serverless_inference_config=None, - sagemaker_session=DEFAULT_JUMPSTART_SAGEMAKER_SESSION, - config_name=None, - model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS, + framework, + region, + version=None, + py_version=None, + instance_type=None, + accelerator_type=None, + image_scope=None, + container_version=None, + distribution=None, + base_framework_version=None, + training_compiler_config=None, + model_id=None, + model_version=None, + hub_arn=None, + tolerate_vulnerable_model=False, + tolerate_deprecated_model=False, + sdk_version=None, + inference_tool=None, + serverless_inference_config=None, + sagemaker_session=DEFAULT_JUMPSTART_SAGEMAKER_SESSION, + config_name=None, + model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS, ) -> str: """Retrieves the ECR URI for the Docker image matching the given arguments. @@ -250,10 +250,10 @@ def retrieve( if config.get("version_aliases").get(original_version): _version = config.get("version_aliases")[original_version] if ( - config.get("versions", {}) - .get(_version, {}) - .get("version_aliases", {}) - .get(base_framework_version, {}) + config.get("versions", {}) + .get(_version, {}) + .get("version_aliases", {}) + .get(base_framework_version, {}) ): _base_framework_version = config.get("versions")[_version]["version_aliases"][ base_framework_version @@ -290,16 +290,16 @@ def retrieve( def _get_image_tag( - container_version, - distribution, - final_image_scope, - framework, - inference_tool, - instance_type, - processor, - py_version, - tag_prefix, - version, + container_version, + distribution, + final_image_scope, + framework, + inference_tool, + instance_type, + processor, + py_version, + tag_prefix, + version, ): """Return image tag based on framework, container, and compute configuration(s).""" instance_type_family = utils.get_instance_type_family(instance_type) @@ -311,8 +311,8 @@ def _get_image_tag( "instance type", ) if ( - instance_type_family in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY - or final_image_scope == INFERENCE_GRAVITON + instance_type_family in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY + or final_image_scope == INFERENCE_GRAVITON ): version_to_arm64_tag_mapping = { "xgboost": { @@ -330,7 +330,7 @@ def _get_image_tag( tag = _format_tag(tag_prefix, processor, py_version, container_version, inference_tool) if instance_type is not None and _should_auto_select_container_version( - instance_type, distribution + instance_type, distribution ): container_versions = { "tensorflow-2.3-gpu-py37": "cu110-ubuntu18.04-v3", @@ -398,7 +398,7 @@ def _validate_instance_deprecation(framework, instance_type, version): """Check if instance type is deprecated for a certain framework with a certain version""" if utils.get_instance_type_family(instance_type) == "p2": if (framework == "pytorch" and Version(version) >= Version("1.13")) or ( - framework == "tensorflow" and Version(version) >= Version("2.12") + framework == "tensorflow" and Version(version) >= Version("2.12") ): raise ValueError( "P2 instances have been deprecated for sagemaker jobs starting PyTorch 1.13 and TensorFlow 2.12" @@ -411,17 +411,17 @@ def _validate_for_suppported_frameworks_and_instance_type(framework, instance_ty """Validate if framework is supported for the instance_type""" # Validate for Trainium allowed frameworks if ( - instance_type is not None - and "trn" in instance_type - and framework not in TRAINIUM_ALLOWED_FRAMEWORKS + instance_type is not None + and "trn" in instance_type + and framework not in TRAINIUM_ALLOWED_FRAMEWORKS ): _validate_framework(framework, TRAINIUM_ALLOWED_FRAMEWORKS, "framework", "Trainium") # Validate for Graviton allowed frameowrks if ( - instance_type is not None - and utils.get_instance_type_family(instance_type) in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY - and framework not in GRAVITON_ALLOWED_FRAMEWORKS + instance_type is not None + and utils.get_instance_type_family(instance_type) in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY + and framework not in GRAVITON_ALLOWED_FRAMEWORKS ): _validate_framework(framework, GRAVITON_ALLOWED_FRAMEWORKS, "framework", "Graviton") @@ -436,8 +436,8 @@ def config_for_framework(framework): def _get_final_image_scope(framework, instance_type, image_scope): """Return final image scope based on provided framework and instance type.""" if ( - framework in GRAVITON_ALLOWED_FRAMEWORKS - and utils.get_instance_type_family(instance_type) in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY + framework in GRAVITON_ALLOWED_FRAMEWORKS + and utils.get_instance_type_family(instance_type) in GRAVITON_ALLOWED_TARGET_INSTANCE_FAMILY ): return INFERENCE_GRAVITON if image_scope is None and framework in (XGBOOST_FRAMEWORK, SKLEARN_FRAMEWORK): @@ -635,16 +635,16 @@ def _format_tag(tag_prefix, processor, py_version, container_version, inference_ @override_pipeline_parameter_var def get_training_image_uri( - region, - framework, - framework_version=None, - py_version=None, - image_uri=None, - distribution=None, - compiler_config=None, - tensorflow_version=None, - pytorch_version=None, - instance_type=None, + region, + framework, + framework_version=None, + py_version=None, + image_uri=None, + distribution=None, + compiler_config=None, + tensorflow_version=None, + pytorch_version=None, + instance_type=None, ) -> str: """Retrieves the image URI for training. @@ -748,26 +748,28 @@ def get_base_python_image_uri(region, py_version="310") -> str: return ECR_URI_TEMPLATE.format(registry=registry, hostname=hostname, repository=repo_and_tag) -def get_latest_container_image(framework: str, - image_scope: Optional[str] = None, - instance_type: Optional[str] = None, - py_version: Optional[str] = None, - region: str = "us-west-2", - version: Optional[str] = None, - accelerator_type=None, - container_version=None, - distribution=None, - base_framework_version=None, - training_compiler_config=None, - model_id=None, - model_version=None, - hub_arn=None, - sdk_version=None, - inference_tool=None, - serverless_inference_config=None, - config_name=None, - ) -> Tuple[str, str]: +def get_latest_container_image( + framework: str, + image_scope: Optional[str] = None, + instance_type: Optional[str] = None, + py_version: Optional[str] = None, + region: str = "us-west-2", + version: Optional[str] = None, + accelerator_type=None, + container_version=None, + distribution=None, + base_framework_version=None, + training_compiler_config=None, + model_id=None, + model_version=None, + hub_arn=None, + sdk_version=None, + inference_tool=None, + serverless_inference_config=None, + config_name=None, +) -> Tuple[str, str]: """Retrieves the latest container image URI + Args: framework (str): The name of the framework or algorithm. image_scope (str): The image type, i.e. what it is used for. @@ -818,31 +820,34 @@ def get_latest_container_image(framework: str, if not version: version = _fetch_latest_version_from_config(framework_config, image_scope) - image_uri = retrieve(framework=framework, - region=region, - version=version, - instance_type=instance_type, - py_version=py_version, - accelerator_type=accelerator_type, - image_scope=image_scope, - container_version=container_version, - distribution=distribution, - base_framework_version=base_framework_version, - training_compiler_config=training_compiler_config, - model_id=model_id, - model_version=model_version, - hub_arn=hub_arn, - sdk_version=sdk_version, - inference_tool=inference_tool, - serverless_inference_config=serverless_inference_config, - config_name=config_name - ) + image_uri = retrieve( + framework=framework, + region=region, + version=version, + instance_type=instance_type, + py_version=py_version, + accelerator_type=accelerator_type, + image_scope=image_scope, + container_version=container_version, + distribution=distribution, + base_framework_version=base_framework_version, + training_compiler_config=training_compiler_config, + model_id=model_id, + model_version=model_version, + hub_arn=hub_arn, + sdk_version=sdk_version, + inference_tool=inference_tool, + serverless_inference_config=serverless_inference_config, + config_name=config_name, + ) return image_uri, version -def _fetch_latest_version_from_config(framework_config: dict, - image_scope: Optional[str] = None) -> Optional[str]: - """ Helper function to fetch the latest version as a string from a framework's config +def _fetch_latest_version_from_config( + framework_config: dict, image_scope: Optional[str] = None +) -> Optional[str]: + """Helper function to fetch the latest version as a string from a framework's config + Args: framework_config (dict): A framework config dict. image_scope (str): Scope of the image, eg: training, inference @@ -863,8 +868,11 @@ def _fetch_latest_version_from_config(framework_config: dict, bottom_version = versions[-1] if top_version == "latest" or bottom_version == "latest": return None - elif (image_scope is not None and image_scope in framework_config - and "versions" in framework_config[image_scope]): + elif ( + image_scope is not None + and image_scope in framework_config + and "versions" in framework_config[image_scope] + ): versions = list(framework_config[image_scope]["versions"].keys()) top_version = versions[0] bottom_version = versions[-1] diff --git a/src/sagemaker/modules/scripts/environment.py b/src/sagemaker/modules/scripts/environment.py index 4b80dcae72..2ccdd92c82 100644 --- a/src/sagemaker/modules/scripts/environment.py +++ b/src/sagemaker/modules/scripts/environment.py @@ -26,7 +26,7 @@ logger = logging.getLogger(__name__) console_handler = logging.StreamHandler(sys.stdout) logger.addHandler(console_handler) -logger.setLevel(SM_LOG_LEVEL) +logger.setLevel(int(SM_LOG_LEVEL)) SM_MODEL_DIR = "/opt/ml/model" @@ -85,7 +85,7 @@ def num_neurons(): neuron_cores = 0 for item in j: neuron_cores += item.get("nc_count", 0) - logger.info(f"Found {neuron_cores} neurons on this instance") + logger.info("Found %s neurons on this instance", neuron_cores) return neuron_cores except OSError: logger.info("No Neurons detected (normal if no neurons installed)") @@ -96,9 +96,8 @@ def num_neurons(): msg = e.output.decode("utf-8").partition("error=")[2] logger.info( "No Neurons detected (normal if no neurons installed). \ - If neuron installed then {}".format( - msg - ) + If neuron installed then %s", + msg, ) except AttributeError: logger.info("No Neurons detected (normal if no neurons installed)") @@ -109,11 +108,10 @@ def num_neurons(): def set_env( - resource_config: Dict[str, Any] = {}, - input_data_config: Dict[str, Any] = {}, - hyperparameters_config: Dict[str, Any] = {}, + resource_config: Dict[str, Any], + input_data_config: Dict[str, Any], + hyperparameters_config: Dict[str, Any], output_file: str = "sm_training.env", - write_to_etc: bool = False, ): """Set environment variables for the training job container. @@ -122,7 +120,6 @@ def set_env( input_data_config (Dict[str, Any]): Input data configuration for the training job. hyperparameters_config (Dict[str, Any]): Hyperparameters configuration for the training job. output_file (str): Output file to write the environment variables. - write_to_etc (bool): Whether to write the environment variables to /etc/environment. """ # Constants env_vars = { @@ -151,10 +148,12 @@ def set_env( # Host Variables current_host = resource_config["current_host"] + current_instance_type = resource_config["current_instance_type"] hosts = resource_config["hosts"] sorted_hosts = sorted(hosts) env_vars["SM_CURRENT_HOST"] = current_host + env_vars["SM_CURRENT_INSTANCE_TYPE"] = current_instance_type env_vars["SM_HOSTS"] = sorted_hosts env_vars["SM_NETWORK_INTERFACE_NAME"] = resource_config["network_interface_name"] env_vars["SM_HOST_COUNT"] = len(sorted_hosts) @@ -174,6 +173,7 @@ def set_env( channel: env_vars[f"SM_CHANNEL_{channel.upper()}"] for channel in channels }, "current_host": env_vars["SM_CURRENT_HOST"], + "current_instance_type": env_vars["SM_CURRENT_INSTANCE_TYPE"], "hosts": env_vars["SM_HOSTS"], "master_addr": env_vars["SM_MASTER_ADDR"], "master_port": env_vars["SM_MASTER_PORT"], @@ -204,17 +204,9 @@ def set_env( else: f.write(f"export {key}='{value}'\n") - # Need to write to /etc/environment for MPI to work - if write_to_etc: - with open("/etc/environment", "a") as f: - for key, value in env_vars.items(): - if isinstance(value, (list, dict)): - f.write(f"{key}='{json.dumps(value)}'\n") - else: - f.write(f"{key}='{value}'\n") - -if __name__ == "__main__": +def main(): + """Main function to set the environment variables for the training job container.""" with open(RESOURCE_CONFIG, "r") as f: resource_config = json.load(f) with open(INPUT_DATA_CONFIG, "r") as f: @@ -227,5 +219,8 @@ def set_env( input_data_config=input_data_config, hyperparameters_config=hyperparameters_config, output_file=ENV_OUTPUT_FILE, - write_to_etc=True, ) + + +if __name__ == "__main__": + main() diff --git a/src/sagemaker/modules/scripts/intelligent_params.py b/src/sagemaker/modules/scripts/intelligent_params.py index 71624c3925..e1105def1a 100644 --- a/src/sagemaker/modules/scripts/intelligent_params.py +++ b/src/sagemaker/modules/scripts/intelligent_params.py @@ -24,15 +24,16 @@ def set_intelligent_params(path: str) -> None: """Set intelligent parameters for all python files under the given path. - For python code with comment sm_hyper_param or sm_hp_{variable_name}, the value will be found in - /opt/ml/input/config/hyperparameters.json, and this function will rewrite lines with these comments. + For python code with comment sm_hyper_param or sm_hp_{variable_name}, + the value will be found in /opt/ml/input/config/hyperparameters.json, + and this function will rewrite lines with these comments. Args: path (str): The folder path to set intellingent parameters """ with open(HYPERPARAMETERS_FILE_PATH, "r") as f: hyperparameters = json.load(f) - for root, dirs, files in os.walk(path): + for root, dirs, files in os.walk(path): # pylint: disable=W0612 for file in files: if file.endswith(".py"): file_path = os.path.join(root, file) @@ -48,7 +49,7 @@ def rewrite_file(file_path: str, hyperparameters: dict) -> None: """ with open(file_path, "r", encoding="utf-8") as f: lines = f.readlines() - for i in range(len(lines)): + for i in range(len(lines)): # pylint: disable=C0200 lines[i] = rewrite_line(lines[i], hyperparameters) with open(file_path, "w", encoding="utf-8") as f: f.writelines(lines) diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index 9b3e7676fd..1e2fbc4ed6 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -7,7 +7,6 @@ "outputs": [], "source": [ "import sys, os\n", - "from sagemaker import image_uris\n", "\n", "# Get the absolute path of the root directory\n", "root_dir = os.path.abspath(os.path.join(os.getcwd(), '../../..'))\n", @@ -33,15 +32,8 @@ "source": [ "from sagemaker.modules.train.model_trainer import ModelTrainer\n", "from sagemaker.modules.configs import SourceCodeConfig\n", - "from sagemaker.modules.constants import DEFAULT_INSTANCE_TYPE\n", - "\n", - "pytorch_image = image_uris.retrieve(\n", - " framework=\"pytorch\",\n", - " region=\"us-west-2\",\n", - " version=\"2.0.0\",\n", - " image_scope=\"training\",\n", - " instance_type=DEFAULT_INSTANCE_TYPE\n", - ")\n", + "\n", + "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", "\n", "source_code_config = SourceCodeConfig(\n", " command=\"echo 'Hello World' && env\",\n", @@ -304,23 +296,14 @@ "env['FI_EFA_USE_DEVICE_RDMA'] = '1'\n", "env['RDMAV_FORK_SAFE'] = '1'\n", "\n", - "instance_type = \"ml.p4d.24xlarge\"\n", "\n", "resource_config = ResourceConfig(\n", " instance_count=2,\n", - " instance_type=instance_type,\n", + " instance_type=\"ml.p4d.24xlarge\",\n", " volume_size_in_gb=96,\n", ")\n", "\n", - "hugging_face_image = image_uris.retrieve(\n", - " framework=\"huggingface\",\n", - " base_framework_version=\"pytorch2.0.0\",\n", - " region=\"us-west-2\",\n", - " version=\"4.28.1\",\n", - " instance_type=instance_type,\n", - " image_scope=\"training\",\n", - " py_version=\"py310\",\n", - ")\n", + "hugging_face_image = \"63104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n", "\n", "hyper_parameters={\n", " 'dataset_path': '/opt/ml/input/data/dataset',\n", diff --git a/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/requirements.txt b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/requirements.txt index 381ad4dfe5..793d160a95 100644 --- a/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/requirements.txt +++ b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/requirements.txt @@ -1,5 +1,7 @@ transformers==4.31 -datasets +datasets==2.16.1 +fsspec==2023.6.0 accelerate>=0.21 +tokenizers>=0.13.3 bitsandbytes peft diff --git a/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py index 48da810353..7a06c2d8b8 100644 --- a/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py +++ b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py @@ -28,10 +28,6 @@ import functools -# Initialize the distributed environment -dist.init_process_group(backend="nccl") - - def parse_arge(): """Parse the arguments.""" parser = argparse.ArgumentParser() @@ -195,7 +191,8 @@ def training_function(args): HfFolder.save_token(args.access_token) - dataset = load_from_disk(args.dataset_path) + print(f"Loading dataset from {args.dataset_path}") + dataset = load_from_disk(f"file://{args.dataset_path}") dist.barrier() @@ -360,11 +357,31 @@ def training_function(args): def main(): args, _ = parse_arge() - args.local_rank = int(os.environ["LOCAL_RANK"]) - args.rank = int(os.environ["RANK"]) - args.world_size = int(os.environ["WORLD_SIZE"]) + + if "LOCAL_RANK" in os.environ: + # Environment variables set by torch.distributed.launch or torchrun + LOCAL_RANK = int(os.environ["LOCAL_RANK"]) + WORLD_SIZE = int(os.environ["WORLD_SIZE"]) + WORLD_RANK = int(os.environ["RANK"]) + elif "OMPI_COMM_WORLD_LOCAL_RANK" in os.environ: + # Environment variables set by mpirun + LOCAL_RANK = int(os.environ["OMPI_COMM_WORLD_LOCAL_RANK"]) + WORLD_SIZE = int(os.environ["OMPI_COMM_WORLD_SIZE"]) + WORLD_RANK = int(os.environ["OMPI_COMM_WORLD_RANK"]) + else: + import sys + + sys.exit("Can't find the evironment variables for local rank") + + args.local_rank = LOCAL_RANK + args.rank = WORLD_RANK + args.world_size = WORLD_SIZE print(f"Running with args: {args}") + # Initialize the distributed environment + dist.init_process_group( + backend="nccl", init_method="env://", rank=args.rank, world_size=args.world_size + ) training_function(args) diff --git a/tests/unit/sagemaker/image_uris/test_latest_container_image.py b/tests/unit/sagemaker/image_uris/test_latest_container_image.py index 0e4c337160..6b52db5c6a 100644 --- a/tests/unit/sagemaker/image_uris/test_latest_container_image.py +++ b/tests/unit/sagemaker/image_uris/test_latest_container_image.py @@ -1,3 +1,17 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +from __future__ import absolute_import + import json import unittest from unittest.mock import patch @@ -9,18 +23,16 @@ class TestImageUtils(unittest.TestCase): - @patch('src.sagemaker.image_uris.config_for_framework') - def test_get_latest_container_image_invalid_framework(self, - mock_config_for_framework): + @patch("src.sagemaker.image_uris.config_for_framework") + def test_get_latest_container_image_invalid_framework(self, mock_config_for_framework): mock_config_for_framework.side_effect = FileNotFoundError with self.assertRaises(ValueError) as e: get_latest_container_image("xgboost", "inference") assert "No framework config for framework" in str(e.exception) - @patch('src.sagemaker.image_uris.config_for_framework') - def test_get_latest_container_image_no_framework(self, - mock_config_for_framework): + @patch("src.sagemaker.image_uris.config_for_framework") + def test_get_latest_container_image_no_framework(self, mock_config_for_framework): mock_config_for_framework.return_value = {} with self.assertRaises(ValueError) as e: @@ -28,83 +40,90 @@ def test_get_latest_container_image_no_framework(self, assert "No framework config for framework" in str(e.exception) -@pytest.mark.parametrize("framework", ['object-detection', - 'instance_gpu_info', - 'object2vec', - 'pytorch', - 'djl-lmi', - 'mxnet', - 'debugger', - 'data-wrangler', - 'spark', - 'blazingtext', - 'pytorch-neuron', - 'forecasting-deepar', - 'huggingface-neuron', - 'ntm', - 'neo-mxnet', - 'image-classification', - 'xgboost', - 'autogluon', - 'sparkml-serving', - 'clarify', - 'inferentia-pytorch', - 'neo-tensorflow', - 'huggingface-tei-cpu', - 'huggingface', - 'sagemaker-tritonserver', - 'pytorch-smp', - 'knn', - 'linear-learner', - 'model-monitor', - 'ray-tensorflow', - 'djl-neuronx', - 'huggingface-llm-neuronx', - 'image-classification-neo', - 'lda', - 'stabilityai', - 'ray-pytorch', - 'chainer', - 'coach-mxnet', - 'pca', - 'sagemaker-geospatial', - 'djl-tensorrtllm', - 'huggingface-training-compiler', - 'pytorch-training-compiler', - 'vw', - 'huggingface-neuronx', - 'ipinsights', - 'detailed-profiler', - 'inferentia-tensorflow', - 'semantic-segmentation', - 'inferentia-mxnet', - 'xgboost-neo', - 'neo-pytorch', - 'djl-deepspeed', - 'djl-fastertransformer', - 'sklearn', - 'tensorflow', - 'randomcutforest', - 'huggingface-llm', - 'factorization-machines', - 'huggingface-tei', - 'coach-tensorflow', - 'seq2seq', - 'kmeans', - 'sagemaker-base-python']) -@patch('src.sagemaker.image_uris.config_for_framework') -@patch('src.sagemaker.image_uris.retrieve') -def test_get_latest_container_image_parameterized(mock_image_retrieve, - mock_config_for_framework, - framework): +@pytest.mark.parametrize( + "framework", + [ + "object-detection", + "instance_gpu_info", + "object2vec", + "pytorch", + "djl-lmi", + "mxnet", + "debugger", + "data-wrangler", + "spark", + "blazingtext", + "pytorch-neuron", + "forecasting-deepar", + "huggingface-neuron", + "ntm", + "neo-mxnet", + "image-classification", + "xgboost", + "autogluon", + "sparkml-serving", + "clarify", + "inferentia-pytorch", + "neo-tensorflow", + "huggingface-tei-cpu", + "huggingface", + "sagemaker-tritonserver", + "pytorch-smp", + "knn", + "linear-learner", + "model-monitor", + "ray-tensorflow", + "djl-neuronx", + "huggingface-llm-neuronx", + "image-classification-neo", + "lda", + "stabilityai", + "ray-pytorch", + "chainer", + "coach-mxnet", + "pca", + "sagemaker-geospatial", + "djl-tensorrtllm", + "huggingface-training-compiler", + "pytorch-training-compiler", + "vw", + "huggingface-neuronx", + "ipinsights", + "detailed-profiler", + "inferentia-tensorflow", + "semantic-segmentation", + "inferentia-mxnet", + "xgboost-neo", + "neo-pytorch", + "djl-deepspeed", + "djl-fastertransformer", + "sklearn", + "tensorflow", + "randomcutforest", + "huggingface-llm", + "factorization-machines", + "huggingface-tei", + "coach-tensorflow", + "seq2seq", + "kmeans", + "sagemaker-base-python", + ], +) +@patch("src.sagemaker.image_uris.config_for_framework") +@patch("src.sagemaker.image_uris.retrieve") +def test_get_latest_container_image_parameterized( + mock_image_retrieve, mock_config_for_framework, framework +): file_path = f"src/sagemaker/image_uri_config/{framework}.json" - with open(file_path, 'r') as json_file: + with open(file_path, "r") as json_file: config_for_framework = json.load(json_file) mock_config_for_framework.return_value = config_for_framework mock_image_retrieve.return_value = "latest-image" - image, version = get_latest_container_image(framework=framework, - image_scope="inference", - instance_type="ml.c4.xlarge", - region="us-east-1") + image, version = get_latest_container_image( + framework=framework, + image_scope="inference", + instance_type="ml.c4.xlarge", + region="us-east-1", + ) assert image == "latest-image" From 2e17bcbd7a1a3003033e2276d44e56c8917e7961 Mon Sep 17 00:00:00 2001 From: Pravali Uppugunduri <46845440+pravali96@users.noreply.github.com> Date: Fri, 1 Nov 2024 16:15:15 -0700 Subject: [PATCH 14/79] feat: add pre-processing and post-processing logic to inference_spec (#1560) * add pre-processing and post-processing logic to inference_spec * fix format * make accept_type and content_type optional * remove accept_type and content_type from pre/post processing * correct typo --- .../model_server/multi_model_server/inference.py | 12 ++++++++++-- .../serve/model_server/torchserve/inference.py | 12 ++++++++++-- src/sagemaker/serve/spec/inference_spec.py | 6 ++++++ 3 files changed, 26 insertions(+), 4 deletions(-) diff --git a/src/sagemaker/serve/model_server/multi_model_server/inference.py b/src/sagemaker/serve/model_server/multi_model_server/inference.py index 1ee7b5e4dc..595b9d9c39 100644 --- a/src/sagemaker/serve/model_server/multi_model_server/inference.py +++ b/src/sagemaker/serve/model_server/multi_model_server/inference.py @@ -44,13 +44,19 @@ def input_fn(input_data, content_type): """Deserializes the bytes that were received from the model server""" try: if hasattr(schema_builder, "custom_input_translator"): - return schema_builder.custom_input_translator.deserialize( + deserialized_data = schema_builder.custom_input_translator.deserialize( io.BytesIO(input_data), content_type ) else: - return schema_builder.input_deserializer.deserialize( + deserialized_data = schema_builder.input_deserializer.deserialize( io.BytesIO(input_data), content_type[0] ) + + # Check if preprocess method is defined and call it + if hasattr(inference_spec, "preprocess"): + return inference_spec.preprocess(deserialized_data) + + return deserialized_data except Exception as e: logger.error("Encountered error: %s in deserialize_response." % e) raise Exception("Encountered error in deserialize_request.") from e @@ -64,6 +70,8 @@ def predict_fn(input_data, predict_callable): def output_fn(predictions, accept_type): """Prediction is serialized to bytes and sent back to the customer""" try: + if hasattr(inference_spec, "postprocess"): + predictions = inference_spec.postprocess(predictions) if hasattr(schema_builder, "custom_output_translator"): return schema_builder.custom_output_translator.serialize(predictions, accept_type) else: diff --git a/src/sagemaker/serve/model_server/torchserve/inference.py b/src/sagemaker/serve/model_server/torchserve/inference.py index 2675f6ea6a..cad94cc817 100644 --- a/src/sagemaker/serve/model_server/torchserve/inference.py +++ b/src/sagemaker/serve/model_server/torchserve/inference.py @@ -66,13 +66,19 @@ def input_fn(input_data, content_type): """Placeholder docstring""" try: if hasattr(schema_builder, "custom_input_translator"): - return schema_builder.custom_input_translator.deserialize( + deserialized_data = schema_builder.custom_input_translator.deserialize( io.BytesIO(input_data), content_type ) else: - return schema_builder.input_deserializer.deserialize( + deserialized_data = schema_builder.input_deserializer.deserialize( io.BytesIO(input_data), content_type[0] ) + + # Check if preprocess method is defined and call it + if hasattr(inference_spec, "preprocess"): + return inference_spec.preprocess(deserialized_data) + + return deserialized_data except Exception as e: raise Exception("Encountered error in deserialize_request.") from e @@ -85,6 +91,8 @@ def predict_fn(input_data, predict_callable): def output_fn(predictions, accept_type): """Placeholder docstring""" try: + if hasattr(inference_spec, "postprocess"): + predictions = inference_spec.postprocess(predictions) if hasattr(schema_builder, "custom_output_translator"): return schema_builder.custom_output_translator.serialize(predictions, accept_type) else: diff --git a/src/sagemaker/serve/spec/inference_spec.py b/src/sagemaker/serve/spec/inference_spec.py index 2598a38d01..0397e84975 100644 --- a/src/sagemaker/serve/spec/inference_spec.py +++ b/src/sagemaker/serve/spec/inference_spec.py @@ -28,6 +28,12 @@ def invoke(self, input_object: object, model: object): model (object): The model object """ + def preprocess(self, input_data: object): + """Custom pre-processing function""" + + def postprocess(self, predictions: object): + """Custom post-processing function""" + def prepare(self, *args, **kwargs): """Custom prepare function""" From 21a11a92e1298afcc624bc9f7e55f593adaa7476 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Mon, 4 Nov 2024 15:16:42 -0800 Subject: [PATCH 15/79] Add Distributed Training Support Model Trainer (#1536) --- .gitignore | 3 +- src/sagemaker/modules/configs.py | 110 ++++++- src/sagemaker/modules/constants.py | 18 +- src/sagemaker/modules/templates.py | 74 +++-- .../base_model_trainer.ipynb | 250 ++++++++++----- .../scripts/run_clm_no_trainer.py | 1 - .../train/container_drivers/__init__.py | 14 + .../train/container_drivers/mpi_driver.py | 100 ++++++ .../train/container_drivers/mpi_utils.py | 294 ++++++++++++++++++ .../train/container_drivers/pytorch_driver.py | 117 +++++++ .../container_drivers/scripts/__init__.py | 14 + .../container_drivers}/scripts/environment.py | 17 +- .../scripts/intelligent_params.py | 0 .../modules/train/container_drivers/utils.py | 124 ++++++++ src/sagemaker/modules/train/model_trainer.py | 191 ++++++++---- .../train/container_drivers/__init__.py | 0 .../scripts/test_enviornment.py | 134 ++++++++ .../scripts/test_intelligent_params.py | 4 +- .../container_drivers/test_mpi_driver.py | 136 ++++++++ .../container_drivers/test_pytorch_driver.py | 151 +++++++++ .../modules/train/test_model_trainer.py | 56 ---- 21 files changed, 1565 insertions(+), 243 deletions(-) create mode 100644 src/sagemaker/modules/train/container_drivers/__init__.py create mode 100644 src/sagemaker/modules/train/container_drivers/mpi_driver.py create mode 100644 src/sagemaker/modules/train/container_drivers/mpi_utils.py create mode 100644 src/sagemaker/modules/train/container_drivers/pytorch_driver.py create mode 100644 src/sagemaker/modules/train/container_drivers/scripts/__init__.py rename src/sagemaker/modules/{ => train/container_drivers}/scripts/environment.py (95%) rename src/sagemaker/modules/{ => train/container_drivers}/scripts/intelligent_params.py (100%) create mode 100644 src/sagemaker/modules/train/container_drivers/utils.py create mode 100644 tests/unit/sagemaker/modules/train/container_drivers/__init__.py create mode 100644 tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py rename tests/unit/sagemaker/modules/{ => train/container_drivers}/scripts/test_intelligent_params.py (97%) create mode 100644 tests/unit/sagemaker/modules/train/container_drivers/test_mpi_driver.py create mode 100644 tests/unit/sagemaker/modules/train/container_drivers/test_pytorch_driver.py diff --git a/.gitignore b/.gitignore index 9b8f732547..39a442bcd6 100644 --- a/.gitignore +++ b/.gitignore @@ -32,7 +32,8 @@ env/ .python-version *.html **/_repack_script_launcher.sh -src/sagemaker/modules/scripts/train.sh +src/sagemaker/modules/train/container_drivers/sm_train.sh +src/sagemaker/modules/train/container_drivers/sourcecodeconfig.json tests/data/**/_repack_model.py tests/data/experiment/sagemaker-dev-1.0.tar.gz src/sagemaker/serve/tmp_workspace \ No newline at end of file diff --git a/src/sagemaker/modules/configs.py b/src/sagemaker/modules/configs.py index c53449a1d3..2b186f816c 100644 --- a/src/sagemaker/modules/configs.py +++ b/src/sagemaker/modules/configs.py @@ -21,8 +21,8 @@ from __future__ import absolute_import -from typing import Optional -from pydantic import BaseModel +from typing import Optional, Dict, Any, List +from pydantic import BaseModel, model_validator from sagemaker_core.shapes import ( ResourceConfig, @@ -36,6 +36,8 @@ VpcConfig, ) +from sagemaker.modules import logger + __all__ = [ "SourceCodeConfig", "ResourceConfig", @@ -50,6 +52,97 @@ ] +class SMDistributedSettings(BaseModel): + """SMDistributedSettings. + + The SMDistributedSettings is used to configure distributed training when + using the smdistributed library. + + Attributes: + enable_dataparallel (Optional[bool]): + Whether to enable data parallelism. + enable_modelparallel (Optional[bool]): + Whether to enable model parallelism. + modelparallel_parameters (Optional[Dict[str, Any]]): + The parameters for model parallelism. + """ + + enable_dataparallel: Optional[bool] = False + enable_modelparallel: Optional[bool] = False + modelparallel_parameters: Optional[Dict[str, Any]] = None + + +class DistributionConfig(BaseModel): + """Base class for distribution configurations.""" + + _distribution_type: str + + +class TorchDistributionConfig(DistributionConfig): + """TorchDistributionConfig. + + The TorchDistributionConfig uses `torchrun` or `torch.distributed.launch` in the backend to + launch distributed training. + + SMDistributed Library Information: + - `TorchDistributionConfig` can be used for SMModelParallel V2. + - For SMDataParallel or SMModelParallel V1, it is recommended to use the + `MPIDistributionConfig.` + + + Attributes: + smdistributed_settings (Optional[SMDistributedSettings]): + The settings for smdistributed library. + process_count_per_node (int): + The number of processes to run on each node in the training job. + Will default to the number of CPUs or GPUs available in the container. + """ + + _distribution_type: str = "torch_distributed" + + smdistributed_settings: Optional[SMDistributedSettings] = None + process_count_per_node: Optional[int] = None + + @model_validator(mode="after") + def _validate_model(cls, model): # pylint: disable=E0213 + """Validate the model.""" + if ( + getattr(model, "smddistributed_settings", None) + and model.smddistributed_settings.enable_dataparallel + ): + logger.warning( + "For smdistributed data parallelism, it is recommended to use " + + "MPIDistributionConfig." + ) + return model + + +class MPIDistributionConfig(DistributionConfig): + """MPIDistributionConfig. + + The MPIDistributionConfig uses `mpirun` in the backend to launch distributed training. + + SMDistributed Library Information: + - `MPIDistributionConfig` can be used for SMDataParallel and SMModelParallel V1. + - For SMModelParallel V2, it is recommended to use the `TorchDistributionConfig`. + + Attributes: + smdistributed_settings (Optional[SMDistributedSettings]): + The settings for smdistributed library. + process_count_per_node (int): + The number of processes to run on each node in the training job. + Will default to the number of CPUs or GPUs available in the container. + mpi_additional_options (Optional[str]): + The custom MPI options to use for the training job. + """ + + _distribution_type: str = "mpi" + + smdistributed_settings: Optional[SMDistributedSettings] = None + process_count_per_node: Optional[int] = None + mpi_additional_options: Optional[List[str]] = None + + class SourceCodeConfig(BaseModel): """SourceCodeConfig. @@ -57,9 +150,6 @@ class SourceCodeConfig(BaseModel): entry script, or commands to be executed in the training job container. Attributes: - command (Optional[str]): - The command(s) to execute in the training job container. Example: "python my_script.py". - If not specified, entry_script must be provided source_dir (Optional[str]): The local directory containing the source code to be used in the training job container. requirements (Optional[str]): @@ -68,9 +158,17 @@ class SourceCodeConfig(BaseModel): entry_script (Optional[str]): The path within `source_dir` to the entry script that will be executed in the training job container. If not specified, command must be provided. + command (Optional[str]): + The command(s) to execute in the training job container. Example: "python my_script.py". + If not specified, entry_script must be provided. + distribution (Optional[Union[ + MPIDistributionConfig, + TorchDistributionConfig, + ]]): + The distribution configuration for the training job. """ - command: Optional[str] = None source_dir: Optional[str] = None requirements: Optional[str] = None entry_script: Optional[str] = None + command: Optional[str] = None diff --git a/src/sagemaker/modules/constants.py b/src/sagemaker/modules/constants.py index 7cdd10e970..a7f9a897ff 100644 --- a/src/sagemaker/modules/constants.py +++ b/src/sagemaker/modules/constants.py @@ -16,15 +16,21 @@ DEFAULT_INSTANCE_TYPE = "ml.m5.xlarge" -SOURCE_CODE_CONTAINER_PATH = "/opt/ml/input/data/code" - +SM_CODE = "sm_code" SM_CODE_CONTAINER_PATH = "/opt/ml/input/data/sm_code" -SM_CODE_LOCAL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "scripts") -TRAIN_SCRIPT = "train.sh" + +SM_DRIVERS = "sm_drivers" +SM_DRIVERS_CONTAINER_PATH = "/opt/ml/input/data/sm_drivers" +SM_DRIVERS_LOCAL_PATH = os.path.join( + os.path.dirname(os.path.abspath(__file__)), "train/container_drivers" +) + +SOURCE_CODE_CONFIG_JSON = "sourcecodeconfig.json" +TRAIN_SCRIPT = "sm_train.sh" DEFAULT_CONTAINER_ENTRYPOINT = ["/bin/bash"] DEFAULT_CONTAINER_ARGUMENTS = [ "-c", - f"chmod +x {SM_CODE_CONTAINER_PATH}/{TRAIN_SCRIPT} " - + f"&& {SM_CODE_CONTAINER_PATH}/{TRAIN_SCRIPT}", + f"chmod +x {SM_DRIVERS_CONTAINER_PATH}/{TRAIN_SCRIPT} " + + f"&& {SM_DRIVERS_CONTAINER_PATH}/{TRAIN_SCRIPT}", ] diff --git a/src/sagemaker/modules/templates.py b/src/sagemaker/modules/templates.py index 3040bef5f1..55ad44d81d 100644 --- a/src/sagemaker/modules/templates.py +++ b/src/sagemaker/modules/templates.py @@ -13,10 +13,58 @@ """Templates module.""" from __future__ import absolute_import +EXECUTE_BASE_COMMANDS = """ +CMD="{base_command}" +echo "Running command: $CMD" +eval $CMD +""" + +EXECUTE_PYTORCH_DRIVER = """ +echo "Running PyTorch training driver" +$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/pytorch_driver.py +""" + +EXECUTE_MPI_DRIVER = """ +echo "Running MPI training driver" +$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/mpi_driver.py +""" + TRAIN_SCRIPT_TEMPLATE = """ #!/bin/bash +set -e echo "Starting training script" +handle_error() {{ + EXIT_STATUS=$? + echo "An error occurred with exit code $EXIT_STATUS" + if [ ! -s /opt/ml/output/failure ]; then + echo "Training Execution failed. For more details, see CloudWatch logs at 'aws/sagemaker/TrainingJobs'. +TrainingJob - $TRAINING_JOB_NAME" >> /opt/ml/output/failure + fi + exit $EXIT_STATUS +}} + +check_python() {{ + if command -v python3 &>/dev/null; then + SM_PYTHON_CMD="python3" + SM_PIP_CMD="pip3" + echo "Found python3" + elif command -v python &>/dev/null; then + SM_PYTHON_CMD="python" + SM_PIP_CMD="pip" + echo "Found python" + else + echo "Python may not be installed" + return 1 + fi +}} + +trap 'handle_error' ERR + +check_python + +$SM_PYTHON_CMD --version + echo "/opt/ml/input/config/resourceconfig.json:" cat /opt/ml/input/config/resourceconfig.json echo @@ -29,27 +77,17 @@ cat /opt/ml/input/config/hyperparameters.json echo +echo "/opt/ml/input/data/sm_drivers/sourcecodeconfig.json" +cat /opt/ml/input/data/sm_drivers/sourcecodeconfig.json +echo + echo "Setting up environment variables" -python /opt/ml/input/data/sm_code/environment.py -source /opt/ml/input/data/sm_code/sm_training.env +$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/scripts/environment.py +source /opt/ml/input/data/sm_drivers/scripts/sm_training.env -python --version {working_dir} {install_requirements} -CMD="{command}" -echo "Running command: $CMD" -eval $CMD -EXIT_STATUS=$? +{execute_driver} -if [ $EXIT_STATUS -ne 0 ]; then - echo "Command failed with exit status $EXIT_STATUS" - if [ ! -s /opt/ml/output/failure ]; then - echo "Command failed with exit code $EXIT_STATUS. -For more details, see CloudWatch logs at 'aws/sagemaker/TrainingJobs'. -TrainingJob - $TRAINING_JOB_NAME" >> /opt/ml/output/failure - fi - exit $EXIT_STATUS -else - echo "Command succeeded" -fi +echo "Training Container Execution Completed" """ diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index 1e2fbc4ed6..eda9b19bca 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -9,7 +9,7 @@ "import sys, os\n", "\n", "# Get the absolute path of the root directory\n", - "root_dir = os.path.abspath(os.path.join(os.getcwd(), '../../..'))\n", + "root_dir = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n", "sys.path.insert(0, root_dir)" ] }, @@ -17,11 +17,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Simple Case Minimally Setup ModelTrainer and Execute Commands\n", - "\n", - "1. No longer have an issue about API Limit\n", - "2. Maintain contract with container (no secret handshake)\n", - "3. Decouple Training Toolkit" + "## Simple Case Minimally Setup ModelTrainer and Execute Commands" ] }, { @@ -41,7 +37,7 @@ "model_trainer = ModelTrainer(\n", " training_image=pytorch_image,\n", " source_code_config=source_code_config,\n", - ")\n" + ")" ] }, { @@ -57,17 +53,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Simple Script Mode Case - 1\n", - "\n", - "1. No more code smell of user having to use /opt/ml/input/data/code/custom_script.py\n", - "```python\n", - "source_code_config = SourceCodeConfig(\n", - " source_dir=\"script-mode-basic\",\n", - " command=\"python /opt/ml/input/data/code/custom_script.py\",\n", - ")\n", - "```\n", - "\n", - "2. No more risk of API Limit being reached" + "Successful Run - https://tiny.amazon.com/3maxeyb/IsenLink" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Script Mode Case - 1" ] }, { @@ -76,8 +69,11 @@ "metadata": {}, "outputs": [], "source": [ + "from sagemaker.modules.train.model_trainer import ModelTrainer\n", "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", + "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", + "\n", "source_code_config = SourceCodeConfig(\n", " source_dir=\"basic-script-mode\",\n", " command=\"python custom_script.py\",\n", @@ -86,16 +82,16 @@ "model_trainer = ModelTrainer(\n", " training_image=pytorch_image,\n", " source_code_config=source_code_config,\n", - ")\n" + ")\n", + "\n", + "model_trainer.train()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "model_trainer.train()" + "Successful Run - https://tiny.amazon.com/6uy7pmpj/IsenLink" ] }, { @@ -134,6 +130,13 @@ "model_trainer.train()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Successful Run - https://tiny.amazon.com/7n4n4ogr/IsenLink" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -159,7 +162,7 @@ "access_token = os.environ.get(\"HF_TOKEN\", \"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\")\n", "model_id = \"meta-llama/Llama-2-7b-hf\"\n", "\n", - "dataset_name = \"tatsu-lab/alpaca\"\n" + "dataset_name = \"tatsu-lab/alpaca\"" ] }, { @@ -176,18 +179,18 @@ "outputs": [], "source": [ "from datasets import load_dataset\n", - "from transformers import AutoTokenizer \n", + "from transformers import AutoTokenizer\n", "\n", - "from huggingface_hub.hf_api import HfFolder;\n", + "from huggingface_hub.hf_api import HfFolder\n", "\n", - "# Load Tokenizer \n", + "# Load Tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained(model_id, access_token=access_token)\n", "\n", "\n", "# Load dataset from huggingface.co\n", "dataset = load_dataset(dataset_name)\n", "\n", - "dataset = dataset.shuffle(seed=42)\n" + "dataset = dataset.shuffle(seed=42)" ] }, { @@ -197,15 +200,9 @@ "outputs": [], "source": [ "if \"validation\" not in dataset.keys():\n", - " dataset[\"validation\"] = load_dataset(\n", - " dataset_name,\n", - " split=\"train[:1%]\"\n", - " )\n", + " dataset[\"validation\"] = load_dataset(dataset_name, split=\"train[:1%]\")\n", "\n", - " dataset[\"train\"] = load_dataset(\n", - " dataset_name,\n", - " split=\"train[1%:]\"\n", - " )" + " dataset[\"train\"] = load_dataset(dataset_name, split=\"train[1%:]\")" ] }, { @@ -224,26 +221,30 @@ "from itertools import chain\n", "from functools import partial\n", "\n", - "def group_texts(examples,block_size = 2048):\n", - " # Concatenate all texts.\n", - " concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}\n", - " total_length = len(concatenated_examples[list(examples.keys())[0]])\n", - " # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n", - " # customize this part to your needs.\n", - " if total_length >= block_size:\n", - " total_length = (total_length // block_size) * block_size\n", - " # Split by chunks of max_len.\n", - " result = {\n", - " k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n", - " for k, t in concatenated_examples.items()\n", - " }\n", - " result[\"labels\"] = result[\"input_ids\"].copy()\n", - " return result\n", + "\n", + "def group_texts(examples, block_size=2048):\n", + " # Concatenate all texts.\n", + " concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}\n", + " total_length = len(concatenated_examples[list(examples.keys())[0]])\n", + " # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can\n", + " # customize this part to your needs.\n", + " if total_length >= block_size:\n", + " total_length = (total_length // block_size) * block_size\n", + " # Split by chunks of max_len.\n", + " result = {\n", + " k: [t[i : i + block_size] for i in range(0, total_length, block_size)]\n", + " for k, t in concatenated_examples.items()\n", + " }\n", + " result[\"labels\"] = result[\"input_ids\"].copy()\n", + " return result\n", + "\n", "\n", "column_names = dataset[\"train\"].column_names\n", "\n", "lm_dataset = dataset.map(\n", - " lambda sample: tokenizer(sample[\"text\"],return_token_type_ids=False), batched=True, remove_columns=list(column_names)\n", + " lambda sample: tokenizer(sample[\"text\"], return_token_type_ids=False),\n", + " batched=True,\n", + " remove_columns=list(column_names),\n", ").map(\n", " partial(group_texts, block_size=2048),\n", " batched=True,\n", @@ -263,9 +264,9 @@ "metadata": {}, "outputs": [], "source": [ - "#save data locally\n", + "# save data locally\n", "\n", - "training_input_path = f'distributed-training/processed/data/'\n", + "training_input_path = f\"distributed-training/processed/data/\"\n", "lm_dataset.save_to_disk(training_input_path)\n", "\n", "print(f\"Saved data to: {training_input_path}\")" @@ -286,16 +287,16 @@ "source": [ "from sagemaker.modules.train.model_trainer import ModelTrainer\n", "from sagemaker.modules.configs import ResourceConfig, SourceCodeConfig\n", + "from sagemaker import image_uris\n", "\n", "env = {}\n", - "env['FI_PROVIDER'] = 'efa'\n", - "env['NCCL_PROTO'] = 'simple'\n", - "env['NCCL_SOCKET_IFNAME'] = \"eth0\"\n", - "env['NCCL_IB_DISABLE'] = \"1\"\n", - "env['NCCL_DEBUG'] = \"WARN\"\n", - "env['FI_EFA_USE_DEVICE_RDMA'] = '1'\n", - "env['RDMAV_FORK_SAFE'] = '1'\n", - "\n", + "env[\"FI_PROVIDER\"] = \"efa\"\n", + "env[\"NCCL_PROTO\"] = \"simple\"\n", + "env[\"NCCL_SOCKET_IFNAME\"] = \"eth0\"\n", + "env[\"NCCL_IB_DISABLE\"] = \"1\"\n", + "env[\"NCCL_DEBUG\"] = \"WARN\"\n", + "env[\"FI_EFA_USE_DEVICE_RDMA\"] = \"1\"\n", + "env[\"RDMAV_FORK_SAFE\"] = \"1\"\n", "\n", "resource_config = ResourceConfig(\n", " instance_count=2,\n", @@ -303,21 +304,28 @@ " volume_size_in_gb=96,\n", ")\n", "\n", - "hugging_face_image = \"63104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n", - "\n", - "hyper_parameters={\n", - " 'dataset_path': '/opt/ml/input/data/dataset',\n", - " 'epochs': 1,\n", - " 'max_steps':100,\n", - " 'fsdp': '\"full_shard auto_wrap\"',\n", - " 'fsdp_transformer_layer_cls_to_wrap': \"LlamaDecoderLayer\",\n", - " 'gradient_checkpointing': True,\n", - " 'optimizer': \"adamw_torch\",\n", - " 'per_device_train_batch_size': 1,\n", - " 'model_id': model_id,\n", - " 'access_token': access_token\n", - "}\n", - "\n", + "hugging_face_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n", + "\n", + "hyper_parameters = {\n", + " \"dataset_path\": \"/opt/ml/input/data/dataset\",\n", + " \"epochs\": 1,\n", + " \"max_steps\": 100,\n", + " \"fsdp\": \"full_shard auto_wrap\",\n", + " \"fsdp_transformer_layer_cls_to_wrap\": \"LlamaDecoderLayer\",\n", + " \"gradient_checkpointing\": True,\n", + " \"optimizer\": \"adamw_torch\",\n", + " \"per_device_train_batch_size\": 1,\n", + " \"model_id\": model_id,\n", + " \"access_token\": access_token,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "source_code_config = SourceCodeConfig(\n", " source_dir=\"distributed-training/scripts\",\n", " requirements=\"requirements.txt\",\n", @@ -329,14 +337,13 @@ " run_clm_no_trainer.py\",\n", ")\n", "\n", - "\n", "model_trainer = ModelTrainer(\n", " training_image=hugging_face_image,\n", " resource_config=resource_config,\n", " environment=env,\n", " hyper_parameters=hyper_parameters,\n", " source_code_config=source_code_config,\n", - ")\n" + ")" ] }, { @@ -345,11 +352,90 @@ "metadata": {}, "outputs": [], "source": [ - "model_trainer.train(\n", - " input_data_channels={\"dataset\": training_input_path}\n", + "model_trainer.train(input_data_channels={\"dataset\": training_input_path})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Successful Run - https://tiny.amazon.com/10wljn1yu/IsenLink" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.configs import TorchDistributionConfig, ResourceConfig, SourceCodeConfig\n", + "from sagemaker.modules.train.model_trainer import ModelTrainer\n", + "from sagemaker import image_uris\n", + "\n", + "env = {}\n", + "env[\"NCCL_IB_DISABLE\"] = \"1\"\n", + "env[\"NCCL_DEBUG\"] = \"WARN\"\n", + "\n", + "resource_config = ResourceConfig(\n", + " instance_count=2,\n", + " instance_type=\"ml.p4d.24xlarge\",\n", + " volume_size_in_gb=96,\n", + ")\n", + "\n", + "hugging_face_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n", + "\n", + "hyper_parameters = {\n", + " \"dataset_path\": \"/opt/ml/input/data/dataset\",\n", + " \"epochs\": 1,\n", + " \"max_steps\": 49,\n", + " \"fsdp\": \"full_shard auto_wrap\",\n", + " \"fsdp_transformer_layer_cls_to_wrap\": \"LlamaDecoderLayer\",\n", + " \"gradient_checkpointing\": True,\n", + " \"optimizer\": \"adamw_torch\",\n", + " \"per_device_train_batch_size\": 1,\n", + " \"model_id\": model_id,\n", + " \"access_token\": access_token,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "source_code_config = SourceCodeConfig(\n", + " source_dir=\"distributed-training/scripts\",\n", + " requirements=\"requirements.txt\",\n", + " entry_script=\"run_clm_no_trainer.py\",\n", + " distribution=TorchDistributionConfig()\n", + ")\n", + "\n", + "model_trainer = ModelTrainer(\n", + " training_image=hugging_face_image,\n", + " resource_config=resource_config,\n", + " environment=env,\n", + " hyper_parameters=hyper_parameters,\n", + " source_code_config=source_code_config,\n", ")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_trainer.train(input_data_channels={\"dataset\": training_input_path})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Successful Run - https://tiny.amazon.com/1if17nhk1/IsenLink" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -393,7 +479,7 @@ "model_trainer = ModelTrainer(\n", " training_image=python_ecr_image,\n", " source_code_config=source_code_config,\n", - ")\n" + ")" ] }, { diff --git a/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py index 7a06c2d8b8..945267cef6 100644 --- a/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py +++ b/src/sagemaker/modules/testing_notebooks/distributed-training/scripts/run_clm_no_trainer.py @@ -190,7 +190,6 @@ def training_function(args): from huggingface_hub.hf_api import HfFolder HfFolder.save_token(args.access_token) - print(f"Loading dataset from {args.dataset_path}") dataset = load_from_disk(f"file://{args.dataset_path}") diff --git a/src/sagemaker/modules/train/container_drivers/__init__.py b/src/sagemaker/modules/train/container_drivers/__init__.py new file mode 100644 index 0000000000..18557a2eb5 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/__init__.py @@ -0,0 +1,14 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Sagemaker modules container_drivers directory.""" +from __future__ import absolute_import diff --git a/src/sagemaker/modules/train/container_drivers/mpi_driver.py b/src/sagemaker/modules/train/container_drivers/mpi_driver.py new file mode 100644 index 0000000000..989c1cf3d3 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/mpi_driver.py @@ -0,0 +1,100 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module is the entry point for the MPI driver script.""" +from __future__ import absolute_import + +import os +import json + +from utils import ( + logger, + read_source_code_config_json, + get_process_count, + execute_commands, + write_failure_file, + USER_CODE_PATH, +) +from mpi_utils import ( + start_sshd_daemon, + bootstrap_master_node, + bootstrap_worker_node, + get_mpirun_command, + write_status_file_to_workers, + write_env_vars_to_file, +) + + +def main(): + """Main function for the MPI driver script. + + The MPI Dirver is responsible for setting up the MPI environment, + generating the correct mpi commands, and launching the MPI job. + + Execution Lifecycle: + 1. Setup General Environment Variables at /etc/environment + 2. Start SSHD Daemon + 3. Bootstrap Worker Nodes + a. Wait to establish connection with Master Node + b. Wait for Master Node to write status file + 4. Bootstrap Master Node + a. Wait to establish connection with Worker Nodes + b. Generate MPI Command + c. Execute MPI Command with user script provided in `entry_script` + d. Write status file to Worker Nodes + 5. Exit + + """ + source_code_config = read_source_code_config_json() + distribution = source_code_config.get("distribution", {}) + sm_distributed_settings = distribution.get("smdistributed_settings", {}) + + sm_current_host = os.environ["SM_CURRENT_HOST"] + sm_hosts = json.loads(os.environ["SM_HOSTS"]) + sm_master_addr = os.environ["SM_MASTER_ADDR"] + + write_env_vars_to_file() + start_sshd_daemon() + + if sm_current_host != sm_master_addr: + bootstrap_worker_node(sm_master_addr) + else: + worker_hosts = [host for host in sm_hosts if host != sm_master_addr] + bootstrap_master_node(worker_hosts) + + host_list = json.loads(os.environ["SM_HOSTS"]) + host_count = int(os.environ["SM_HOST_COUNT"]) + process_count = get_process_count(source_code_config) + + if process_count > 1: + host_list = ["{}:{}".format(host, process_count) for host in host_list] + + mpi_command = get_mpirun_command( + host_count=host_count, + host_list=host_list, + num_processes=process_count, + smdataparallel_enabled=sm_distributed_settings.get("enable_dataparallel", False), + smmodelparallel_enabled=sm_distributed_settings.get("enable_modelparallel", False), + additional_options=distribution.get("mpi_additional_options", []), + entry_script_path=os.path.join(USER_CODE_PATH, source_code_config["entry_script"]), + ) + + logger.info(f"Executing command: {mpi_command}") + exit_code, error_traceback = execute_commands(mpi_command) + if exit_code != 0: + write_failure_file(error_traceback) + + write_status_file_to_workers(worker_hosts) + + +if __name__ == "__main__": + main() diff --git a/src/sagemaker/modules/train/container_drivers/mpi_utils.py b/src/sagemaker/modules/train/container_drivers/mpi_utils.py new file mode 100644 index 0000000000..2c19986751 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/mpi_utils.py @@ -0,0 +1,294 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module provides mpi related utility functions for the container drivers.""" +from __future__ import absolute_import + +import os +import time +import subprocess +import json + +from typing import List + +from utils import logger, SM_EFA_NCCL_INSTANCES, SM_EFA_RDMA_INSTANCES, get_python_executable + +FINISHED_STATUS_FILE = "/tmp/done.algo-1" +DEFAULT_SSH_PORT = 22 + + +def _write_status_file(host: str, status_file: str) -> bool: + """Write the status file to the provided host.""" + try: + logger.info(f"Start writing mpirun finished status to {host}") + subprocess.run( + ["ssh", host, "touch", f"{status_file}"], + capture_output=True, + text=True, + check=True, + ) + logger.info("Finished writing status file") + return True + except subprocess.CalledProcessError: + logger.info(f"Cannot connect to {host}") + return False + + +def write_status_file_to_workers(worker_hosts: List[str], status_file: str = FINISHED_STATUS_FILE): + """Write the status file to all worker nodes.""" + for worker in worker_hosts: + retry = 0 + while not _write_status_file(worker, status_file): + time.sleep(5) + retry += 1 + if retry > 5: + raise TimeoutError(f"Timed out waiting for {worker} to be reachable.") + logger.info(f"Retrying to write status file to {worker}") + + +def _wait_for_status_file(status_file: str): + """Wait for the status file to be created.""" + logger.info(f"Waiting for status file {status_file}") + while not os.path.exists(status_file): + time.sleep(30) + logger.info(f"Found status file {status_file}") + + +def start_sshd_daemon(): + """Start the SSH daemon on the current node.""" + sshd_executable = "/usr/sbin/sshd" + + if not os.path.exists(sshd_executable): + raise RuntimeError("SSH daemon not found.") + + # Start the sshd in daemon mode (-D) + subprocess.Popen([sshd_executable, "-D"]) + logger.info("Started SSH daemon.") + + +def _can_connect(host: str, port: int = DEFAULT_SSH_PORT) -> bool: + """Check if the connection to the provided host and port is possible.""" + try: + import paramiko + + logger.debug("Testing connection to host %s", host) + client = paramiko.SSHClient() + client.load_system_host_keys() + client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) + client.connect(host, port=port) + client.close() + logger.info("Can connect to host %s", host) + return True + except Exception as e: # pylint: disable=W0703 + logger.info("Cannot connect to host %s", host) + logger.debug(f"Connection failed with exception: {e}") + return False + + +def _wait_for_workers(worker_hosts: List[str], port: int = DEFAULT_SSH_PORT, timeout: int = 300): + """Master node waits until it can connect to all worker nodes.""" + start_time = time.time() + if not worker_hosts: + logger.info("No worker nodes to connect to.") + return + + while True: + logger.info("Master is attempting to connect to all workers...") + all_workers_connected = all(_can_connect(worker, port) for worker in worker_hosts) + + if all_workers_connected: + logger.info("Master can connect to all worker nodes.") + break + if time.time() - start_time > timeout: + raise TimeoutError("Timed out waiting for workers to be reachable.") + + time.sleep(5) # Wait for 5 seconds before trying again + + +def _wait_for_master(master_host: str, port: int = DEFAULT_SSH_PORT, timeout: int = 300): + """Worker nodes wait until they can connect to the master node.""" + start_time = time.time() + while True: + logger.info(f"Worker is attempting to connect to the master node {master_host}...") + if _can_connect(master_host, port): + logger.info(f"Worker can connect to master node {master_host}.") + break + if time.time() - start_time > timeout: + raise TimeoutError(f"Timed out waiting for master {master_host} to be reachable.") + + time.sleep(5) # Wait for 5 seconds before trying again + + +def bootstrap_worker_node(master_host: str, status_file: str = FINISHED_STATUS_FILE): + """Bootstrap the worker nodes.""" + logger.info("Bootstrapping worker node...") + _wait_for_master(master_host) + _wait_for_status_file(status_file) + + +def bootstrap_master_node(worker_hosts: List[str]): + """Bootstrap the master node.""" + logger.info("Bootstrapping master node...") + _wait_for_workers(worker_hosts) + + +def validate_smddprun() -> bool: + """Whether smddprun is installed. + + Returns: + bool: True if installed + """ + try: + output = subprocess.run( + ["which", "smddprun"], + capture_output=True, + text=True, + check=True, + ) + return output.stdout != "" + except subprocess.CalledProcessError: + return False + + +def validate_smddpmprun() -> bool: + """Whether smddpmprun is installed. + + Returns: + bool: True if both are installed + """ + try: + output = subprocess.run( + ["which", "smddpmprun"], + capture_output=True, + text=True, + check=True, + ) + return output.stdout != "" + except subprocess.CalledProcessError: + return False + + +def write_env_vars_to_file(): + """Write environment variables to /etc/environment file.""" + with open("/etc/environment", "a") as f: + for name in os.environ: + f.write("{}={}\n".format(name, os.environ.get(name))) + + +def get_mpirun_command( + host_count: int, + host_list: List[str], + num_processes: int, + smdataparallel_enabled: bool, + smmodelparallel_enabled: bool, + additional_options: List[str], + entry_script_path: str, +): + """Fetch mpi command""" + network_interface_name = os.environ.get("SM_NETWORK_INTERFACE_NAME", "eth0") + + mpirun_command = [ + "mpirun", + "--host", + ",".join(host_list), + "-np", + str(num_processes), + "--allow-run-as-root", + "--tag-output", + "--oversubscribe", + "-mca", + "btl_tcp_if_include", + network_interface_name, + "-mca", + "oob_tcp_if_include", + network_interface_name, + "-mca", + "plm_rsh_no_tree_spawn", + "1", + "-mca", + "pml", + "ob1", + "-mca", + "btl", + "^openib", + "-mca", + "orte_abort_on_non_zero_status", + "1", + "-mca", + "btl_vader_single_copy_mechanism", + "none", + "-mca", + "plm_rsh_num_concurrent", + str(host_count), + "-x", + "NCCL_SOCKET_IFNAME=%s" % network_interface_name, + "-x", + "LD_LIBRARY_PATH", + "-x", + "PATH", + ] + + if additional_options: + mpirun_command.extend(additional_options) + + instance_type = os.environ["SM_CURRENT_INSTANCE_TYPE"] + # EFA settings + if instance_type in SM_EFA_NCCL_INSTANCES: + # Use simple protocol to handle the out-of-order data delivery from EFA + mpirun_command.extend(["-x", "NCCL_PROTO=simple"]) + + if instance_type in SM_EFA_RDMA_INSTANCES: + # Use EFA's RDMA functionality for one-sided and two-sided transfer + mpirun_command.extend(["-x", "FI_EFA_USE_DEVICE_RDMA=1"]) + + for credential in [ + "AWS_ACCESS_KEY_ID", + "AWS_SECRET_ACCESS_KEY", + "AWS_SESSION_TOKEN", + ]: + if credential in os.environ: + mpirun_command.extend(["-x", credential]) + + if smdataparallel_enabled: + if host_count == 1: + smdataparallel_flag = "SMDATAPARALLEL_USE_HOMOGENEOUS=1" + mpirun_command.extend(["-x", smdataparallel_flag]) + else: + smdataparallel_flag = "SMDATAPARALLEL_USE_SINGLENODE=1" + smdataparallel_server_port = 7592 + smdataparallel_server_addr = "algo-1" + + mpirun_command.extend(["-x", smdataparallel_flag]) + mpirun_command.extend( + [ + "-x", + f"SMDATAPARALLEL_SERVER_ADDR={smdataparallel_server_addr}", + "-x", + f"SMDATAPARALLEL_SERVER_PORT={smdataparallel_server_port}", + "-x", + f"SAGEMAKER_INSTANCE_TYPE={instance_type}", + ] + ) + + if validate_smddprun(): + mpirun_command.extend(["smddprun"]) + + if smmodelparallel_enabled: + mp_parameters = json.loads(os.environ.get("SM_HP_MP_PARAMETERS", "{}")) + ddp_dist_backend = mp_parameters.get("ddp_dist_backend", "auto") + if ddp_dist_backend == "auto": + if validate_smddpmprun(): + mpirun_command.extend(["smddpmprun", "-i", instance_type, "--allow-bypass"]) + + mpirun_command.extend([get_python_executable()]) + mpirun_command.extend(["-m", "mpi4py", entry_script_path]) + return mpirun_command diff --git a/src/sagemaker/modules/train/container_drivers/pytorch_driver.py b/src/sagemaker/modules/train/container_drivers/pytorch_driver.py new file mode 100644 index 0000000000..b5d72fbe76 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/pytorch_driver.py @@ -0,0 +1,117 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module is the entry point for the PyTorch driver script.""" +from __future__ import absolute_import + +import os + +from typing import List, Tuple + +from utils import ( + logger, + read_source_code_config_json, + get_process_count, + get_python_executable, + SM_EFA_NCCL_INSTANCES, + SM_EFA_RDMA_INSTANCES, + execute_commands, + write_failure_file, + USER_CODE_PATH, +) + + +def pytorch_version() -> Tuple[int, int]: + """Get the PyTorch version as a tuple of integers.""" + import torch + + return tuple(map(int, torch.__version__.split(".")[:2])) + + +def get_base_pytorch_command() -> List[str]: + """Get the base Torch Distributed launcher to execute""" + if pytorch_version() >= (1, 9): + return ["torchrun"] + return [f"{get_python_executable()}", "-m", "torch.distributed.launch"] + + +def setup_env(): + """Setup the environment variables for PyTorch distributed training""" + instance_type = os.environ["SM_CURRENT_INSTANCE_TYPE"] + network_interface_name = os.environ.get("SM_NETWORK_INTERFACE_NAME", "eth0") + if instance_type in SM_EFA_NCCL_INSTANCES: + # Enable EFA use + os.environ["FI_PROVIDER"] = "efa" + if instance_type in SM_EFA_RDMA_INSTANCES: + # Use EFA's RDMA functionality for one-sided and two-sided transfer + os.environ["FI_EFA_USE_DEVICE_RDMA"] = "1" + os.environ["RDMAV_FORK_SAFE"] = "1" + os.environ["NCCL_SOCKET_IFNAME"] = str(network_interface_name) + os.environ["NCCL_PROTO"] = "simple" + + +def create_commands(): + """Create the Torch Distributed command to execute""" + source_code_config = read_source_code_config_json() + + process_count = get_process_count(source_code_config) + host_count = int(os.environ["SM_HOST_COUNT"]) + + torch_cmd = [] + if os.environ.get("RUN_NEURON_PARALLEL_COMPILE") == "1": + torch_cmd.append("neuron_parallel_compile") + + torch_cmd.extend(get_base_pytorch_command()) + torch_cmd.extend( + [ + f"--nnodes={host_count}", + f"--nproc_per_node={process_count}", + ] + ) + + # If more than one node is used, add node rank information + if int(host_count) > 1: + torch_cmd.extend( + [ + f"--master_addr={os.environ['SM_MASTER_ADDR']}", + f"--master_port={os.environ['SM_MASTER_PORT']}", + f"--node_rank={os.environ['SM_CURRENT_HOST_RANK']}", + ] + ) + + torch_cmd.extend([os.path.join(USER_CODE_PATH, source_code_config["entry_script"])]) + return torch_cmd + + +def main(): + """Main function to execute the PyTorch distributed training script. + + This function sets some environment variables and executes the PyTorch + distributed training script. + + Execution Lifecycle: + 1. Setup Environment Variables for PyTorch Distributed Training + 2. Create Torch Distributed Command + 3. Execute Torch Distributed Command with user script provided in `entry_script` + 4. Exit + + """ + setup_env() + torch_cmd = create_commands() + logger.info(f"Executing command: {torch_cmd}") + exit_code, traceback = execute_commands(torch_cmd) + if exit_code != 0: + write_failure_file(traceback) + + +if __name__ == "__main__": + main() diff --git a/src/sagemaker/modules/train/container_drivers/scripts/__init__.py b/src/sagemaker/modules/train/container_drivers/scripts/__init__.py new file mode 100644 index 0000000000..1abbce4067 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/scripts/__init__.py @@ -0,0 +1,14 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Sagemaker modules scripts directory.""" +from __future__ import absolute_import diff --git a/src/sagemaker/modules/scripts/environment.py b/src/sagemaker/modules/train/container_drivers/scripts/environment.py similarity index 95% rename from src/sagemaker/modules/scripts/environment.py rename to src/sagemaker/modules/train/container_drivers/scripts/environment.py index 2ccdd92c82..bb6ff634aa 100644 --- a/src/sagemaker/modules/scripts/environment.py +++ b/src/sagemaker/modules/train/container_drivers/scripts/environment.py @@ -45,10 +45,10 @@ INPUT_DATA_CONFIG = f"{SM_INPUT_CONFIG_DIR}/inputdataconfig.json" HYPERPARAMETERS_CONFIG = f"{SM_INPUT_CONFIG_DIR}/hyperparameters.json" -ENV_OUTPUT_FILE = "sm_training.env" +ENV_OUTPUT_FILE = "/opt/ml/input/data/sm_drivers/scripts/sm_training.env" -def num_cpus(): +def num_cpus() -> int: """Return the number of CPUs available in the current container. Returns: @@ -57,7 +57,7 @@ def num_cpus(): return multiprocessing.cpu_count() -def num_gpus(): +def num_gpus() -> int: """Return the number of GPUs available in the current container. Returns: @@ -72,7 +72,7 @@ def num_gpus(): return 0 -def num_neurons(): +def num_neurons() -> int: """Return the number of neuron cores available in the current container. Returns: @@ -111,7 +111,7 @@ def set_env( resource_config: Dict[str, Any], input_data_config: Dict[str, Any], hyperparameters_config: Dict[str, Any], - output_file: str = "sm_training.env", + output_file: str = ENV_OUTPUT_FILE, ): """Set environment variables for the training job container. @@ -192,12 +192,7 @@ def set_env( "output_data_dir": env_vars["SM_OUTPUT_DATA_DIR"], "resource_config": env_vars["SM_RESOURCE_CONFIG"], } - try: - cur_dir = os.path.dirname(os.path.abspath(__file__)) - except NameError: - # Fallback to current working directory - cur_dir = os.getcwd() - with open(os.path.join(cur_dir, output_file), "w") as f: + with open(output_file, "w") as f: for key, value in env_vars.items(): if isinstance(value, (list, dict)): f.write(f"export {key}='{json.dumps(value)}'\n") diff --git a/src/sagemaker/modules/scripts/intelligent_params.py b/src/sagemaker/modules/train/container_drivers/scripts/intelligent_params.py similarity index 100% rename from src/sagemaker/modules/scripts/intelligent_params.py rename to src/sagemaker/modules/train/container_drivers/scripts/intelligent_params.py diff --git a/src/sagemaker/modules/train/container_drivers/utils.py b/src/sagemaker/modules/train/container_drivers/utils.py new file mode 100644 index 0000000000..ae05df8994 --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/utils.py @@ -0,0 +1,124 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module provides utility functions for the container drivers.""" +from __future__ import absolute_import + +import os +import logging +import sys +import subprocess +import traceback +import json + +from typing import List, Dict, Any, Tuple, IO + +# Initialize logger +SM_LOG_LEVEL = os.environ.get("SM_LOG_LEVEL", 20) +logger = logging.getLogger(__name__) +console_handler = logging.StreamHandler(sys.stdout) +logger.addHandler(console_handler) +logger.setLevel(int(SM_LOG_LEVEL)) + +FAILURE_FILE = "/opt/ml/output/failure" +DEFAULT_FAILURE_MESSAGE = f""" +Training Execution failed. +For more details, see CloudWatch logs at 'aws/sagemaker/TrainingJobs'. +TrainingJob - {os.environ['TRAINING_JOB_NAME']} +""" + +USER_CODE_PATH = "/opt/ml/input/data/code" +SOURCE_CODE_CONFIG_JSON = "/opt/ml/input/data/sm_code/sourcecodeconfig.json" + +SM_EFA_NCCL_INSTANCES = [ + "ml.g4dn.8xlarge", + "ml.g4dn.12xlarge", + "ml.g5.48xlarge", + "ml.p3dn.24xlarge", + "ml.p4d.24xlarge", + "ml.p4de.24xlarge", + "ml.p5.48xlarge", + "ml.trn1.32xlarge", +] + +SM_EFA_RDMA_INSTANCES = [ + "ml.p4d.24xlarge", + "ml.p4de.24xlarge", + "ml.trn1.32xlarge", +] + + +def write_failure_file(message: str = DEFAULT_FAILURE_MESSAGE): + """Write a failure file with the message.""" + if not os.path.exists(FAILURE_FILE): + with open(FAILURE_FILE, "w") as f: + f.write(message) + + +def read_source_code_config_json(source_code_config_file: Dict[str, Any] = SOURCE_CODE_CONFIG_JSON): + """Read the source code config json file.""" + with open(source_code_config_file, "r") as f: + distribution_config = json.load(f) + return distribution_config + + +def get_process_count(source_code_config: Dict[str, Any]) -> int: + """Get the number of processes to run on each node in the training job.""" + if source_code_config.get("distribution", {}).get("process_count_per_node") is not None: + return int(source_code_config["distribution"]["process_count_per_node"]) + if os.environ.get("SM_NUM_GPUS") is not None: + return int(os.environ["SM_NUM_GPUS"]) + if os.environ.get("SM_NUM_NEURONS") is not None: + return int(os.environ["SM_NUM_NEURONS"]) + return 1 # Default to 1 process per node + + +def get_python_executable() -> str: + """Get the python executable path.""" + return sys.executable + + +def log_subprocess_output(pipe: IO[bytes]): + """Log the output from the subprocess.""" + for line in iter(pipe.readline, b""): + logger.info(line.decode("utf-8").strip()) + + +def execute_commands(commands: List[str]) -> Tuple[int, str]: + """Execute the provided commands and return exit code with failure traceback if any.""" + try: + process = subprocess.Popen( + commands, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + ) + with process.stdout: + log_subprocess_output(process.stdout) + exitcode = process.wait() + if exitcode != 0: + raise subprocess.CalledProcessError(exitcode, commands) + return exitcode, "" + except subprocess.CalledProcessError as e: + # Capture the traceback in case of failure + error_traceback = traceback.format_exc() + print(f"Command failed with exit code {e.returncode}. Traceback: {error_traceback}") + return e.returncode, error_traceback + + +def is_worker_node() -> bool: + """Check if the current node is a worker node.""" + return os.environ.get("SM_CURRENT_HOST") != os.environ.get("SM_MASTER_ADDR") + + +def is_master_node() -> bool: + """Check if the current node is the master node.""" + return os.environ.get("SM_CURRENT_HOST") == os.environ.get("SM_MASTER_ADDR") diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 56dc7d96e9..12a288e770 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -14,6 +14,7 @@ from __future__ import absolute_import import os +import json from typing import Optional, List, Union, Dict, Any from pydantic import BaseModel, ConfigDict @@ -22,17 +23,21 @@ from sagemaker_core.shapes import AlgorithmSpecification from sagemaker import get_execution_role, Session +from sagemaker.fw_utils import validate_mp_config from sagemaker.modules.configs import ( ResourceConfig, StoppingCondition, OutputDataConfig, SourceCodeConfig, + MPIDistributionConfig, + TorchDistributionConfig, TrainingImageConfig, Channel, DataSource, S3DataSource, FileSystemDataSource, VpcConfig, + SMDistributedSettings, ) from sagemaker.modules.utils import ( _get_repo_name_from_image, @@ -43,13 +48,21 @@ from sagemaker.modules.types import DataSourceType from sagemaker.modules.constants import ( DEFAULT_INSTANCE_TYPE, - SOURCE_CODE_CONTAINER_PATH, - SM_CODE_LOCAL_PATH, + SM_CODE, + SM_CODE_CONTAINER_PATH, + SM_DRIVERS, + SM_DRIVERS_LOCAL_PATH, TRAIN_SCRIPT, DEFAULT_CONTAINER_ENTRYPOINT, DEFAULT_CONTAINER_ARGUMENTS, + SOURCE_CODE_CONFIG_JSON, +) +from sagemaker.modules.templates import ( + TRAIN_SCRIPT_TEMPLATE, + EXECUTE_BASE_COMMANDS, + EXECUTE_MPI_DRIVER, + EXECUTE_PYTORCH_DRIVER, ) -from sagemaker.modules.templates import TRAIN_SCRIPT_TEMPLATE from sagemaker.modules import logger @@ -87,6 +100,12 @@ class ModelTrainer(BaseModel): source_code_config (Optional[SourceCodeConfig]): The source code configuration. This is used to configure the source code for running the training job. + distribution_config (Optional[Union[ + MPIDistributionConfig, TorchDistributionConfig + ]]): + The distribution settings for the training job. This is used to configure + a distributed training job. If specifed, a `source_code_config` must also + be provided. algorithm_name (Optional[str]): The SageMaker marketplace algorithm name/arn to use for the training job. algorithm_name cannot be specified if training_image is specified. @@ -119,6 +138,7 @@ class ModelTrainer(BaseModel): output_data_config: Optional[OutputDataConfig] = None input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] = None source_code_config: Optional[SourceCodeConfig] = None + distribution_config: Optional[Union[MPIDistributionConfig, TorchDistributionConfig]] = None algorithm_name: Optional[str] = None training_image: Optional[str] = None training_input_mode: Optional[str] = "File" @@ -140,6 +160,36 @@ def _validate_training_image_and_algorithm_name( "Only one of 'training_image' or 'algorithm_name' must be provided.", ) + # TODO: Move to use pydantic model validators + def _validate_sm_distributed_settings( + self, sm_distributed_settings: Optional[SMDistributedSettings] + ): + """Validate the SM distributed settings.""" + if ( + sm_distributed_settings.enable_dataparallel + and sm_distributed_settings.enable_modelparallel + ): + raise ValueError( + "Both 'enable_dataparallel' and 'enable_modelparallel' cannot be True." + ) + if sm_distributed_settings.modelparallel_parameters: + validate_mp_config(sm_distributed_settings.modelparallel_parameters) + + def _validate_distribution_config( + self, + source_code_config: Optional[SourceCodeConfig], + distribution_config: Optional[Union[MPIDistributionConfig, TorchDistributionConfig]], + ): + """Validate the distribution configuration.""" + if distribution_config and not source_code_config.entry_script: + raise ValueError( + "Must provide 'entry_script' if 'distribution' " + + "is provided in 'source_code_config'.", + ) + if distribution_config and distribution_config.smdistributed_settings: + self._validate_sm_distributed_settings(distribution_config.smdistributed_settings) + + # TODO: Move to use pydantic model validators def _validate_source_code_config(self, source_code_config: Optional[SourceCodeConfig]): """Validate the source code configuration.""" if source_code_config: @@ -177,9 +227,9 @@ def _validate_source_code_config(self, source_code_config: Optional[SourceCodeCo def model_post_init(self, __context: Any): """Post init method to perform custom validation and set default values.""" - self._validate_training_image_and_algorithm_name(self.training_image, self.algorithm_name) self._validate_source_code_config(self.source_code_config) + self._validate_distribution_config(self.source_code_config, self.distribution_config) if self.session is None: self.session = Session() @@ -261,33 +311,45 @@ def train( if self.input_data_channels: input_data_config = self._get_input_data_config(self.input_data_channels) + # Unfortunately, API requires hyperparameters to be strings + string_hyper_parameters = {} + if self.hyper_parameters: + for hyper_parameter, value in self.hyper_parameters.items(): + if isinstance(value, (dict, list)): + string_hyper_parameters[hyper_parameter] = json.dumps(value) + else: + string_hyper_parameters[hyper_parameter] = str(value) + container_entrypoint = None container_arguments = None if self.source_code_config: # If source code is provided, create a channel for the source code # The source code will be mounted at /opt/ml/input/data/code in the container - # and set as the working directory - working_dir = "" if self.source_code_config.source_dir: source_code_channel = self.create_input_data_channel( - "code", self.source_code_config.source_dir + SM_CODE, self.source_code_config.source_dir ) input_data_config.append(source_code_channel) - working_dir = SOURCE_CODE_CONTAINER_PATH - # Get the commands to execute in the training job container - # and prepare the train.sh script - commands = self._get_script_mode_command(self.source_code_config) self._prepare_train_script( - command=commands, - requirements=self.source_code_config.requirements, - working_dir=working_dir, + source_code_config=self.source_code_config, ) + if self.distribution_config: + smd_modelparallel_parameters = getattr( + self.distribution_config.smdistributed_settings, + "modelparallel_parameters", + None, + ) + if smd_modelparallel_parameters: + string_hyper_parameters["mp_parameters"] = json.dumps( + smd_modelparallel_parameters + ) + self._write_source_code_config_json(self.source_code_config) - # Create an input channel for scripts packaged by the sdk - sm_scripts_channel = self.create_input_data_channel("sm_code", SM_CODE_LOCAL_PATH) - input_data_config.append(sm_scripts_channel) + # Create an input channel for drivers packaged by the sdk + sm_drivers_channel = self.create_input_data_channel(SM_DRIVERS, SM_DRIVERS_LOCAL_PATH) + input_data_config.append(sm_drivers_channel) # If source_code_config is provided, we will always use # the default container entrypoint and arguments @@ -306,12 +368,6 @@ def train( container_arguments=container_arguments, ) - # Unfortunately, API requires hyperparameters to be strings - string_hyper_parameters = {} - if self.hyper_parameters: - for hyper_parameter, value in self.hyper_parameters.items(): - string_hyper_parameters[hyper_parameter] = str(value) - training_job = TrainingJob.create( session=self.session.boto_session, role_arn=self.role, @@ -406,64 +462,79 @@ def _get_input_data_config( for channel_name, data_source in input_data_channels.items() ] - def _get_script_mode_command(self, source_code_config: SourceCodeConfig) -> str: - """Get the command to execute in the training job container for script mode. + def _write_source_code_config_json(self, source_code_config: SourceCodeConfig): + """Write the source code configuration to a JSON file.""" + file_path = os.path.join(SM_DRIVERS_LOCAL_PATH, SOURCE_CODE_CONFIG_JSON) + with open(file_path, "w") as f: + f.write(source_code_config.model_dump_json()) + + def _prepare_train_script( + self, + source_code_config: SourceCodeConfig, + distribution_config: Optional[Union[MPIDistributionConfig, TorchDistributionConfig]] = None, + ): + """Prepare the training script to be executed in the training job container. Args: source_code_config (SourceCodeConfig): The source code configuration. """ + + base_command = "" if source_code_config.command: if source_code_config.entry_script: logger.warning( "Both 'command' and 'entry_script' are provided in the SourceCodeConfig. " + "Defaulting to 'command'." ) - commands = source_code_config.command.split() - return " ".join(commands) + base_command = source_code_config.command.split() + base_command = " ".join(base_command) - assert ( + if ( source_code_config.entry_script - ), "Either 'command' or 'entry_script' must be provided." - - if source_code_config.entry_script.endswith(".py"): - return f"python {source_code_config.entry_script}" - if source_code_config.entry_script.endswith(".sh"): - return ( - f"chmod +x {source_code_config.entry_script} && " - f"bash {source_code_config.entry_script}" - ) - - raise ValueError( - f"Unsupported entry script: {source_code_config.entry_script}." - + "Only .py and .sh scripts are supported." - ) - - def _prepare_train_script( - self, command: str, requirements: Optional[str], working_dir: Optional[str] - ): - """Prepare the training script to be executed in the training job container. + and not source_code_config.command + and not source_code_config.distribution + ): + if source_code_config.entry_script.endswith(".py"): + base_command = f"$SM_PYTHON_CMD {source_code_config.entry_script}" + elif source_code_config.entry_script.endswith(".sh"): + base_command = ( + f"chmod +x {source_code_config.entry_script} && " + f"bash {source_code_config.entry_script}" + ) + else: + raise ValueError( + f"Unsupported entry script: {source_code_config.entry_script}." + + "Only .py and .sh scripts are supported." + ) - Args: - command (str): The command to execute in the training job container. - requirements (str): The path to the requirements file within the source code directory. - working_dir (str): The working directory for the training job container - """ install_requirements = "" - if requirements: + if source_code_config.requirements: install_requirements = "echo 'Installing requirements'\n" - install_requirements = f"pip install -r {requirements}" - - if working_dir: - working_dir = f"cd {working_dir}" + install_requirements = f"$SM_PIP_CMD install -r {source_code_config.requirements}" + + working_dir = "" + if source_code_config.source_dir: + working_dir = f"cd {SM_CODE_CONTAINER_PATH}" + + if base_command: + execute_driver = EXECUTE_BASE_COMMANDS.format(base_command=base_command) + elif distribution_config: + distribution_type = distribution_config._distribution_type + if distribution_type == "mpi": + execute_driver = EXECUTE_MPI_DRIVER + elif distribution_type == "torch_distributed": + execute_driver = EXECUTE_PYTORCH_DRIVER + else: + raise ValueError(f"Unsupported distribution type: {distribution_type}.") train_script = TRAIN_SCRIPT_TEMPLATE.format( working_dir=working_dir, install_requirements=install_requirements, - command=command, + execute_driver=execute_driver, ) - os.makedirs(SM_CODE_LOCAL_PATH, exist_ok=True) - with open(os.path.join(SM_CODE_LOCAL_PATH, TRAIN_SCRIPT), "w") as f: + os.makedirs(SM_DRIVERS_LOCAL_PATH, exist_ok=True) + with open(os.path.join(SM_DRIVERS_LOCAL_PATH, TRAIN_SCRIPT), "w") as f: f.write(train_script) def get_training_image_uri(self) -> str: diff --git a/tests/unit/sagemaker/modules/train/container_drivers/__init__.py b/tests/unit/sagemaker/modules/train/container_drivers/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py b/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py new file mode 100644 index 0000000000..8dc6e330df --- /dev/null +++ b/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py @@ -0,0 +1,134 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Enviornment Variable Script Unit Tests.""" +from __future__ import absolute_import + +import os + +from unittest.mock import patch + +from sagemaker.modules.train.container_drivers.scripts.environment import set_env + +RESOURCE_CONFIG = dict( + current_host="algo-1", + hosts=["algo-1", "algo-2", "algo-3"], + current_group_name="train1", + current_instance_type="ml.p3.16xlarge", + instance_groups=[ + dict( + instance_group_name="train1", + instance_type="ml.p3.16xlarge", + hosts=["algo-1", "algo-2"], + ), + dict( + instance_group_name="train2", + instance_type="ml.p3.8xlarge", + hosts=["algo-3"], + ), + ], + network_interface_name="eth0", +) + +INPUT_DATA_CONFIG = { + "train": { + "ContentType": "trainingContentType", + "TrainingInputMode": "File", + "S3DistributionType": "FullyReplicated", + "RecordWrapperType": "None", + }, + "validation": { + "TrainingInputMode": "File", + "S3DistributionType": "FullyReplicated", + "RecordWrapperType": "None", + }, +} + +USER_HYPERPARAMETERS = { + "batch_size": 32, + "learning_rate": 0.001, + "hosts": ["algo-1", "algo-2"], + "mp_parameters": { + "microbatches": 2, + "partitions": 2, + "pipeline": "interleaved", + "optimize": "memory", + "horovod": True, + }, +} + +OUTPUT_FILE = os.path.join(os.path.dirname(__file__), "sm_training.env") + +# flake8: noqa +EXPECTED_ENV = """ +export SM_MODEL_DIR='/opt/ml/model' +export SM_INPUT_DIR='/opt/ml/input' +export SM_INPUT_DATA_DIR='/opt/ml/input/data' +export SM_INPUT_CONFIG_DIR='/opt/ml/input/config' +export SM_OUTPUT_DIR='/opt/ml/output' +export SM_OUTPUT_FAILURE='/opt/ml/output/failure' +export SM_OUTPUT_DATA_DIR='/opt/ml/output/data' +export SM_LOG_LEVEL='20' +export SM_MASTER_ADDR='algo-1' +export SM_MASTER_PORT='7777' +export SM_CHANNEL_TRAIN='/opt/ml/input/data/train' +export SM_CHANNEL_VALIDATION='/opt/ml/input/data/validation' +export SM_CHANNELS='["train", "validation"]' +export SM_HPS='{"batch_size": 32, "learning_rate": 0.001, "hosts": ["algo-1", "algo-2"], "mp_parameters": {"microbatches": 2, "partitions": 2, "pipeline": "interleaved", "optimize": "memory", "horovod": true}}' +export SM_HP_BATCH_SIZE='32' +export SM_HP_LEARNING_RATE='0.001' +export SM_HP_HOSTS='["algo-1", "algo-2"]' +export SM_HP_MP_PARAMETERS='{"microbatches": 2, "partitions": 2, "pipeline": "interleaved", "optimize": "memory", "horovod": true}' +export SM_CURRENT_HOST='algo-1' +export SM_CURRENT_INSTANCE_TYPE='ml.p3.16xlarge' +export SM_HOSTS='["algo-1", "algo-2", "algo-3"]' +export SM_NETWORK_INTERFACE_NAME='eth0' +export SM_HOST_COUNT='3' +export SM_CURRENT_HOST_RANK='0' +export SM_NUM_CPUS='8' +export SM_NUM_GPUS='0' +export SM_NUM_NEURONS='0' +export SM_RESOURCE_CONFIG='{"current_host": "algo-1", "hosts": ["algo-1", "algo-2", "algo-3"], "current_group_name": "train1", "current_instance_type": "ml.p3.16xlarge", "instance_groups": [{"instance_group_name": "train1", "instance_type": "ml.p3.16xlarge", "hosts": ["algo-1", "algo-2"]}, {"instance_group_name": "train2", "instance_type": "ml.p3.8xlarge", "hosts": ["algo-3"]}], "network_interface_name": "eth0"}' +export SM_INPUT_DATA_CONFIG='{"train": {"ContentType": "trainingContentType", "TrainingInputMode": "File", "S3DistributionType": "FullyReplicated", "RecordWrapperType": "None"}, "validation": {"TrainingInputMode": "File", "S3DistributionType": "FullyReplicated", "RecordWrapperType": "None"}}' +export SM_TRAINING_ENV='{"channel_input_dirs": {"train": "/opt/ml/input/data/train", "validation": "/opt/ml/input/data/validation"}, "current_host": "algo-1", "current_instance_type": "ml.p3.16xlarge", "hosts": ["algo-1", "algo-2", "algo-3"], "master_addr": "algo-1", "master_port": 7777, "hyperparameters": {"batch_size": 32, "learning_rate": 0.001, "hosts": ["algo-1", "algo-2"], "mp_parameters": {"microbatches": 2, "partitions": 2, "pipeline": "interleaved", "optimize": "memory", "horovod": true}}, "input_data_config": {"train": {"ContentType": "trainingContentType", "TrainingInputMode": "File", "S3DistributionType": "FullyReplicated", "RecordWrapperType": "None"}, "validation": {"TrainingInputMode": "File", "S3DistributionType": "FullyReplicated", "RecordWrapperType": "None"}}, "input_config_dir": "/opt/ml/input/config", "input_data_dir": "/opt/ml/input/data", "input_dir": "/opt/ml/input", "job_name": "test-job", "log_level": 20, "model_dir": "/opt/ml/model", "network_interface_name": "eth0", "num_cpus": 8, "num_gpus": 0, "num_neurons": 0, "output_data_dir": "/opt/ml/output/data", "resource_config": {"current_host": "algo-1", "hosts": ["algo-1", "algo-2", "algo-3"], "current_group_name": "train1", "current_instance_type": "ml.p3.16xlarge", "instance_groups": [{"instance_group_name": "train1", "instance_type": "ml.p3.16xlarge", "hosts": ["algo-1", "algo-2"]}, {"instance_group_name": "train2", "instance_type": "ml.p3.8xlarge", "hosts": ["algo-3"]}], "network_interface_name": "eth0"}}' +""" + + +@patch("sagemaker.modules.train.container_drivers.scripts.environment.num_cpus", return_value=8) +@patch("sagemaker.modules.train.container_drivers.scripts.environment.num_gpus", return_value=0) +@patch("sagemaker.modules.train.container_drivers.scripts.environment.num_neurons", return_value=0) +def test_set_env(mock_num_cpus, mock_num_gpus, mock_num_neurons): + with patch.dict(os.environ, {"TRAINING_JOB_NAME": "test-job"}): + set_env( + resource_config=RESOURCE_CONFIG, + input_data_config=INPUT_DATA_CONFIG, + hyperparameters_config=USER_HYPERPARAMETERS, + output_file=OUTPUT_FILE, + ) + + mock_num_cpus.assert_called_once() + mock_num_gpus.assert_called_once() + mock_num_neurons.assert_called_once() + + with open(OUTPUT_FILE, "r") as f: + env_file = f.read().strip() + expected_env = _remove_extra_lines(EXPECTED_ENV) + env_file = _remove_extra_lines(env_file) + + assert env_file == expected_env + os.remove(OUTPUT_FILE) + assert not os.path.exists(OUTPUT_FILE) + + +def _remove_extra_lines(string): + """Removes extra blank lines from a string.""" + return "\n".join([line for line in string.splitlines() if line.strip()]) diff --git a/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py b/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_intelligent_params.py similarity index 97% rename from tests/unit/sagemaker/modules/scripts/test_intelligent_params.py rename to tests/unit/sagemaker/modules/train/container_drivers/scripts/test_intelligent_params.py index a9d3363ee5..13cc481fa5 100644 --- a/tests/unit/sagemaker/modules/scripts/test_intelligent_params.py +++ b/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_intelligent_params.py @@ -10,12 +10,12 @@ # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. -"""Intelligent Parameter Tests.""" +"""Intelligent Parameter Unit Tests.""" from __future__ import absolute_import import os import pytest -from sagemaker.modules.scripts.intelligent_params import ( +from sagemaker.modules.train.container_drivers.scripts.intelligent_params import ( rewrite_file, rewrite_line, ) diff --git a/tests/unit/sagemaker/modules/train/container_drivers/test_mpi_driver.py b/tests/unit/sagemaker/modules/train/container_drivers/test_mpi_driver.py new file mode 100644 index 0000000000..46e6af3108 --- /dev/null +++ b/tests/unit/sagemaker/modules/train/container_drivers/test_mpi_driver.py @@ -0,0 +1,136 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""MPI Driver Unit Tests.""" +from __future__ import absolute_import + +import os +import sys + +from unittest.mock import patch, MagicMock + +sys.modules["utils"] = MagicMock() +sys.modules["mpi_utils"] = MagicMock() + +from sagemaker.modules.train.container_drivers import mpi_driver # noqa: E402 + + +DUMMY_MPI_COMMAND = [ + "mpirun", + "--host", + "algo-1,algo-2", + "-np", + "2", + "python", + "-m", + "mpi4py", + "-m", + "script.py", +] + +DUMMY_SOURCE_CODE_CONFIG = { + "entry_script": "script.py", + "distribution": { + "process_count_per_node": 2, + "sm_distributed_settings": { + "enable_dataparallel": True, + }, + "mpi_additional_options": [ + "-x", + "AWS_REGION", + ], + }, +} + + +@patch.dict( + os.environ, + { + "SM_CURRENT_HOST": "algo-2", + "SM_HOSTS": '["algo-1", "algo-2"]', + "SM_MASTER_ADDR": "algo-1", + "SM_HOST_COUNT": "2", + }, +) +@patch("sagemaker.modules.train.container_drivers.mpi_driver.read_source_code_config_json") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.write_env_vars_to_file") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.start_sshd_daemon") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.bootstrap_master_node") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.bootstrap_worker_node") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.get_mpirun_command") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.execute_commands") +def test_mpi_driver_worker( + mock_execute_commands, + mock_get_mpirun_command, + mock_bootstrap_worker_node, + mock_bootstrap_master_node, + mock_start_sshd_daemon, + mock_write_env_vars_to_file, + mock_read_source_code_config_json, +): + mock_read_source_code_config_json.return_value = DUMMY_SOURCE_CODE_CONFIG + + mpi_driver.main() + + mock_write_env_vars_to_file.assert_called_once() + mock_start_sshd_daemon.assert_called_once() + mock_bootstrap_worker_node.assert_called_once() + + mock_bootstrap_master_node.assert_not_called() + mock_get_mpirun_command.assert_not_called() + mock_execute_commands.assert_not_called() + + +@patch.dict( + os.environ, + { + "SM_CURRENT_HOST": "algo-1", + "SM_HOSTS": '["algo-1", "algo-2"]', + "SM_MASTER_ADDR": "algo-1", + "SM_HOST_COUNT": "2", + }, +) +@patch("sagemaker.modules.train.container_drivers.mpi_driver.read_source_code_config_json") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.write_env_vars_to_file") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.start_sshd_daemon") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.bootstrap_master_node") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.bootstrap_worker_node") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.get_process_count") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.get_mpirun_command") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.execute_commands") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.write_status_file_to_workers") +def test_mpi_driver_master( + mock_write_status_file_to_workers, + mock_execute_commands, + mock_get_mpirun_command, + mock_get_process_count, + mock_bootstrap_worker_node, + mock_bootstrap_master_node, + mock_start_sshd_daemon, + mock_write_env_vars_to_file, + mock_read_source_code_config_json, +): + mock_read_source_code_config_json.return_value = DUMMY_SOURCE_CODE_CONFIG + mock_get_mpirun_command.return_value = DUMMY_MPI_COMMAND + mock_get_process_count.return_value = 2 + mock_execute_commands.return_value = (0, "") + + mpi_driver.main() + + mock_write_env_vars_to_file.assert_called_once() + mock_start_sshd_daemon.assert_called_once() + mock_bootstrap_master_node.assert_called_once() + mock_get_mpirun_command.assert_called_once() + mock_execute_commands.assert_called_once_with(DUMMY_MPI_COMMAND) + mock_write_status_file_to_workers.assert_called_once() + + mock_bootstrap_worker_node.assert_not_called() diff --git a/tests/unit/sagemaker/modules/train/container_drivers/test_pytorch_driver.py b/tests/unit/sagemaker/modules/train/container_drivers/test_pytorch_driver.py new file mode 100644 index 0000000000..4d6a8beeb7 --- /dev/null +++ b/tests/unit/sagemaker/modules/train/container_drivers/test_pytorch_driver.py @@ -0,0 +1,151 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Pytorch Driver Unit Tests.""" +from __future__ import absolute_import + +import os +import sys + +from unittest.mock import patch, MagicMock + +sys.modules["utils"] = MagicMock() + +from sagemaker.modules.train.container_drivers import pytorch_driver # noqa: E402 + +DUMMY_SOURCE_CODE_CONFIG = { + "entry_script": "script.py", + "distribution": { + "process_count_per_node": 2, + "sm_distributed_settings": { + "enable_dataparallel": True, + }, + "mpi_additional_options": [ + "-x", + "AWS_REGION", + ], + }, +} + + +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.get_python_executable", + return_value="python3", +) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.pytorch_version", return_value=(2, 0) +) +def test_get_base_pytorch_command_torchrun(mock_pytorch_version, mock_get_python_executable): + assert pytorch_driver.get_base_pytorch_command() == ["torchrun"] + + +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.get_python_executable", + return_value="python3", +) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.pytorch_version", return_value=(1, 8) +) +def test_get_base_pytorch_command_torch_distributed_launch( + mock_pytorch_version, mock_get_python_executable +): + assert pytorch_driver.get_base_pytorch_command() == ( + ["python3", "-m", "torch.distributed.launch"] + ) + + +@patch.dict( + os.environ, + { + "SM_CURRENT_INSTANCE_TYPE": "ml.p4d.24xlarge", + "SM_NETWORK_INTERFACE_NAME": "eth0", + "SM_HOST_COUNT": "1", + }, +) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.USER_CODE_PATH", + "/opt/ml/input/data/code", +) +@patch("sagemaker.modules.train.container_drivers.pytorch_driver.get_process_count", return_value=2) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.pytorch_version", return_value=(2, 0) +) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.get_base_pytorch_command", + return_value=["torchrun"], +) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.read_source_code_config_json", + return_value=DUMMY_SOURCE_CODE_CONFIG, +) +def test_create_commands_single_node( + mock_read_source_code_config_json, + mock_get_base_pytorch_command, + mock_pytorch_version, + mock_get_process_count, +): + expected_command = [ + "torchrun", + "--nnodes=1", + "--nproc_per_node=2", + "/opt/ml/input/data/code/script.py", + ] + + command = pytorch_driver.create_commands() + assert command == expected_command + + +@patch.dict( + os.environ, + { + "SM_CURRENT_INSTANCE_TYPE": "ml.p4d.24xlarge", + "SM_NETWORK_INTERFACE_NAME": "eth0", + "SM_HOST_COUNT": "2", + "SM_MASTER_ADDR": "algo-1", + "SM_MASTER_PORT": "7777", + "SM_CURRENT_HOST_RANK": "0", + }, +) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.USER_CODE_PATH", + "/opt/ml/input/data/code", +) +@patch("sagemaker.modules.train.container_drivers.pytorch_driver.get_process_count", return_value=2) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.pytorch_version", return_value=(2, 0) +) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.get_base_pytorch_command", + return_value=["torchrun"], +) +@patch( + "sagemaker.modules.train.container_drivers.pytorch_driver.read_source_code_config_json", + return_value=DUMMY_SOURCE_CODE_CONFIG, +) +def test_create_commands_multi_node( + mock_read_source_code_config_json, + mock_get_base_pytorch_command, + mock_pytorch_version, + mock_get_process_count, +): + expected_command = [ + "torchrun", + "--nnodes=2", + "--nproc_per_node=2", + "--master_addr=algo-1", + "--master_port=7777", + "--node_rank=0", + "/opt/ml/input/data/code/script.py", + ] + + command = pytorch_driver.create_commands() + assert command == expected_command diff --git a/tests/unit/sagemaker/modules/train/test_model_trainer.py b/tests/unit/sagemaker/modules/train/test_model_trainer.py index 9cfd27d70e..57ee78794c 100644 --- a/tests/unit/sagemaker/modules/train/test_model_trainer.py +++ b/tests/unit/sagemaker/modules/train/test_model_trainer.py @@ -227,59 +227,3 @@ def test_create_input_data_channel(mock_upload_data, model_trainer, test_case): assert channel.data_source.file_system_data_source == test_case["data_source"] else: assert channel.data_source.s3_data_source.s3_uri == expected_s3_uri - - -@pytest.mark.parametrize( - "test_case", - [ - { - "source_code_config": SourceCodeConfig( - source_dir="test-data", - entry_script="train.py", - ), - "expected_command": "python train.py", - "should_throw": False, - }, - { - "source_code_config": SourceCodeConfig( - command="python train.py", - ), - "expected_command": "python train.py", - "should_throw": False, - }, - { - "source_code_config": SourceCodeConfig( - source_dir="test-data", - entry_script="other.py", - command="python train.py", - ), - "expected_command": "python train.py", - "should_throw": False, - }, - { - "source_code_config": SourceCodeConfig(source_dir="test-data", entry_script="train.sh"), - "expected_command": "chmod +x train.sh && bash train.sh", - "should_throw": False, - }, - { - "source_code_config": SourceCodeConfig( - source_dir="test-data", - entry_script="train.ts", - ), - "should_throw": True, - }, - { - "source_code_config": SourceCodeConfig( - source_dir="test-data", - ), - "should_throw": True, - }, - ], -) -def test_get_script_mode_command(model_trainer, test_case): - if test_case["should_throw"]: - with pytest.raises((ValueError, AssertionError)): - model_trainer._get_script_mode_command(test_case["source_code_config"]) - else: - command = model_trainer._get_script_mode_command(test_case["source_code_config"]) - assert command == test_case["expected_command"] From a406f6422caf3e7e4b783218061ec29d906ec938 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Mon, 4 Nov 2024 17:37:05 -0800 Subject: [PATCH 16/79] Add path to set Additional Settings in ModelTrainer (#1555) --- src/sagemaker/modules/configs.py | 179 +++++++- .../base_model_trainer.ipynb | 118 ++---- src/sagemaker/modules/train/__init__.py | 2 + src/sagemaker/modules/train/model_trainer.py | 399 ++++++++++++++---- src/sagemaker/modules/utils.py | 12 +- .../modules/train/test_model_trainer.py | 197 ++++++++- 6 files changed, 699 insertions(+), 208 deletions(-) diff --git a/src/sagemaker/modules/configs.py b/src/sagemaker/modules/configs.py index 2b186f816c..eee064c0cf 100644 --- a/src/sagemaker/modules/configs.py +++ b/src/sagemaker/modules/configs.py @@ -12,43 +12,84 @@ # language governing permissions and limitations under the License. """This module provides the configuration classes used in `sagemaker.modules`. -Some of these classes are re-exported from `sagemaker-core.shapes`. For convinence, +Some of these classes are re-exported from `sagemaker_core.shapes`. For convinence, users can import these classes directly from `sagemaker.modules.configs`. -For more documentation on `sagemaker-core.shapes`, see: +For more documentation on `sagemaker_core.shapes`, see: - https://sagemaker-core.readthedocs.io/en/stable/#sagemaker-core-shapes """ from __future__ import absolute_import -from typing import Optional, Dict, Any, List +from typing import Optional, Union, Dict, Any, List from pydantic import BaseModel, model_validator +import sagemaker_core.shapes as shapes + +# TODO: Can we add custom logic to some of these to set better defaults? from sagemaker_core.shapes import ( - ResourceConfig, StoppingCondition, + RetryStrategy, OutputDataConfig, Channel, + ShuffleConfig, DataSource, S3DataSource, FileSystemDataSource, TrainingImageConfig, - VpcConfig, + TrainingRepositoryAuthConfig, + Tag, + MetricDefinition, + DebugHookConfig, + CollectionConfiguration, + DebugRuleConfiguration, + ExperimentConfig, + InfraCheckConfig, + ProfilerConfig, + ProfilerRuleConfiguration, + RemoteDebugConfig, + SessionChainingConfig, + InstanceGroup, + TensorBoardOutputConfig, + CheckpointConfig, ) from sagemaker.modules import logger +from sagemaker.modules.utils import convert_unassigned_to_none __all__ = [ "SourceCodeConfig", - "ResourceConfig", + "TorchDistributionConfig", + "MPIDistributionConfig", + "SMDistributedSettings", + "DistributionConfig", "StoppingCondition", + "RetryStrategy", "OutputDataConfig", "Channel", + "ShuffleConfig", "DataSource", "S3DataSource", "FileSystemDataSource", "TrainingImageConfig", - "VpcConfig", + "TrainingRepositoryAuthConfig", + "Tag", + "MetricDefinition", + "DebugHookConfig", + "CollectionConfiguration", + "DebugRuleConfiguration", + "ExperimentConfig", + "InfraCheckConfig", + "ProfilerConfig", + "ProfilerRuleConfiguration", + "RemoteDebugConfig", + "SessionChainingConfig", + "InstanceGroup", + "TensorBoardOutputConfig", + "CheckpointConfig", + "ComputeConfig", + "NetworkingConfig", + "InputData", ] @@ -161,14 +202,128 @@ class SourceCodeConfig(BaseModel): command (Optional[str]): The command(s) to execute in the training job container. Example: "python my_script.py". If not specified, entry_script must be provided. - distribution (Optional[Union[ - MPIDistributionConfig, - TorchDistributionConfig, - ]]): - The distribution configuration for the training job. """ source_dir: Optional[str] = None requirements: Optional[str] = None entry_script: Optional[str] = None command: Optional[str] = None + + +class ComputeConfig(shapes.ResourceConfig): + """ComputeConfig. + + The ComputeConfig is a subclass of `sagemaker_core.shapes.ResourceConfig` + and allows the user to specify the compute resources for the training job. + + Attributes: + instance_type (Optional[str]): + The ML compute instance type. For information about available instance types, + see https://aws.amazon.com/sagemaker/pricing/. Default: ml.m5.xlarge + instance_count (Optional[int]): The number of ML compute instances to use. For distributed + training, provide a value greater than 1. Default: 1 + volume_size_in_gb (Optional[int]): + The size of the ML storage volume that you want to provision. ML storage volumes store + model artifacts and incremental states. Training algorithms might also use the ML + storage volume for scratch space. Default: 30 + volume_kms_key_id (Optional[str]): + The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage + volume attached to the ML compute instance(s) that run the training job. + keep_alive_period_in_seconds (Optional[int]): + The duration of time in seconds to retain configured resources in a warm pool for + subsequent training jobs. + instance_groups (Optional[List[InstanceGroup]]): + A list of instance groups for heterogeneous clusters to be used in the training job. + enable_managed_spot_training (Optional[bool]): + To train models using managed spot training, choose True. Managed spot training + provides a fully managed and scalable infrastructure for training machine learning + models. this option is useful when training jobs can be interrupted and when there + is flexibility when the training job is run. + """ + + volume_size_in_gb: Optional[int] = 30 + enable_managed_spot_training: Optional[bool] = None + + @model_validator(mode="after") + def _model_validator(self) -> "ComputeConfig": + """Convert Unassigned values to None.""" + return convert_unassigned_to_none(self) + + def _to_resource_config(self) -> shapes.ResourceConfig: + """Convert to a sagemaker_core.shapes.ResourceConfig object.""" + compute_config_dict = self.model_dump() + resource_config_fields = set(shapes.ResourceConfig.__annotations__.keys()) + filtered_dict = { + k: v for k, v in compute_config_dict.items() if k in resource_config_fields + } + return shapes.ResourceConfig(**filtered_dict) + + +class NetworkingConfig(shapes.VpcConfig): + """NetworkingConfig. + + The NetworkingConifg is a subclass of `sagemaker_core.shapes.VpcConfig ` and + allows the user to specify the networking configuration for the training job. + + Attributes: + security_group_ids (Optional[List[str]]): + The VPC security group IDs, in the form sg-xxxxxxxx. Specify the + security groups for the VPC that is specified in the Subnets field. + subnets (Optional[List[str]]): + The ID of the subnets in the VPC to which you want to connect your + training job or model. + enable_network_isolation (Optional[bool]): + Isolates the training container. No inbound or outbound network calls can be made, + except for calls between peers within a training cluster for distributed training. + If you enable network isolation for training jobs that are configured to use a VPC, + SageMaker downloads and uploads customer data and model artifacts through the + specified VPC, but the training container does not have network access. + enable_inter_container_traffic_encryption (Optional[bool]): + To encrypt all communications between ML compute instances in distributed training + choose True. Encryption provides greater security for distributed training, but + training might take longer. How long it takes depends on the amount of + communication between compute instances, especially if you use a deep learning + algorithm in distributed training. + """ + + enable_network_isolation: Optional[bool] = None + enable_inter_container_traffic_encryption: Optional[bool] = None + + @model_validator(mode="after") + def _model_validator(self) -> "NetworkingConfig": + """Convert Unassigned values to None.""" + return convert_unassigned_to_none(self) + + def _to_vpc_config(self) -> shapes.VpcConfig: + """Convert to a sagemaker_core.shapes.VpcConfig object.""" + compute_config_dict = self.model_dump() + resource_config_fields = set(shapes.VpcConfig.__annotations__.keys()) + filtered_dict = { + k: v for k, v in compute_config_dict.items() if k in resource_config_fields + } + return shapes.VpcConfig(**filtered_dict) + + +class InputData(BaseModel): + """InputData. + + This config allows the user to specify an input data source for the training job. + + Will be found at `/opt/ml/input/data/` within the training container. + For convience, can be referenced inside the training container like: + + ```python + import os + input_data_dir = os.environ['SM_CHANNEL_'] + ``` + + Attributes: + channel_name (str): + The name of the input data source channel. + data_source (Union[str, S3DataSource, FileSystemDataSource]): + The data source for the channel. Can be an S3 URI string, local file path string, + S3DataSource object, or FileSystemDataSource object. + """ + + channel_name: str = None + data_source: Union[str, FileSystemDataSource, S3DataSource] = None diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index eda9b19bca..213322aa75 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -26,7 +26,7 @@ "metadata": {}, "outputs": [], "source": [ - "from sagemaker.modules.train.model_trainer import ModelTrainer\n", + "from sagemaker.modules.train import ModelTrainer\n", "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", @@ -69,11 +69,13 @@ "metadata": {}, "outputs": [], "source": [ - "from sagemaker.modules.train.model_trainer import ModelTrainer\n", + "from sagemaker.modules.train import ModelTrainer\n", "from sagemaker.modules.configs import SourceCodeConfig\n", "\n", "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", "\n", + "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", + "\n", "source_code_config = SourceCodeConfig(\n", " source_dir=\"basic-script-mode\",\n", " command=\"python custom_script.py\",\n", @@ -285,9 +287,8 @@ "metadata": {}, "outputs": [], "source": [ - "from sagemaker.modules.train.model_trainer import ModelTrainer\n", - "from sagemaker.modules.configs import ResourceConfig, SourceCodeConfig\n", - "from sagemaker import image_uris\n", + "from sagemaker.modules.train import ModelTrainer\n", + "from sagemaker.modules.configs import ComputeConfig, SourceCodeConfig, InputData\n", "\n", "env = {}\n", "env[\"FI_PROVIDER\"] = \"efa\"\n", @@ -298,7 +299,7 @@ "env[\"FI_EFA_USE_DEVICE_RDMA\"] = \"1\"\n", "env[\"RDMAV_FORK_SAFE\"] = \"1\"\n", "\n", - "resource_config = ResourceConfig(\n", + "compute_config = ComputeConfig(\n", " instance_count=2,\n", " instance_type=\"ml.p4d.24xlarge\",\n", " volume_size_in_gb=96,\n", @@ -306,7 +307,7 @@ "\n", "hugging_face_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n", "\n", - "hyper_parameters = {\n", + "hyperparameters = {\n", " \"dataset_path\": \"/opt/ml/input/data/dataset\",\n", " \"epochs\": 1,\n", " \"max_steps\": 100,\n", @@ -329,19 +330,19 @@ "source_code_config = SourceCodeConfig(\n", " source_dir=\"distributed-training/scripts\",\n", " requirements=\"requirements.txt\",\n", - " command=\"torchrun --nnodes $SM_HOST_COUNT \\\n", - " --nproc_per_node $SM_NUM_GPUS \\\n", - " --master_addr $SM_MASTER_ADDR \\\n", - " --master_port $SM_MASTER_PORT \\\n", + " command=\"torchrun --nnodes 2 \\\n", + " --nproc_per_node 8 \\\n", + " --master_addr algo-1 \\\n", + " --master_port 7777 \\\n", " --node_rank $SM_CURRENT_HOST_RANK \\\n", " run_clm_no_trainer.py\",\n", ")\n", "\n", "model_trainer = ModelTrainer(\n", " training_image=hugging_face_image,\n", - " resource_config=resource_config,\n", + " compute_config=compute_config,\n", " environment=env,\n", - " hyper_parameters=hyper_parameters,\n", + " hyperparameters=hyperparameters,\n", " source_code_config=source_code_config,\n", ")" ] @@ -352,7 +353,11 @@ "metadata": {}, "outputs": [], "source": [ - "model_trainer.train(input_data_channels={\"dataset\": training_input_path})" + "test_data = InputData(\n", + " channel_name=\"dataset\",\n", + " data_source=training_input_path,\n", + ")\n", + "model_trainer.train(input_data_config=[test_data])" ] }, { @@ -368,15 +373,12 @@ "metadata": {}, "outputs": [], "source": [ - "from sagemaker.modules.configs import TorchDistributionConfig, ResourceConfig, SourceCodeConfig\n", - "from sagemaker.modules.train.model_trainer import ModelTrainer\n", - "from sagemaker import image_uris\n", - "\n", - "env = {}\n", - "env[\"NCCL_IB_DISABLE\"] = \"1\"\n", - "env[\"NCCL_DEBUG\"] = \"WARN\"\n", + "from sagemaker.modules.train import ModelTrainer\n", + "from sagemaker.modules.configs import (\n", + " ComputeConfig, SourceCodeConfig, TorchDistributionConfig, InputData\n", + ")\n", "\n", - "resource_config = ResourceConfig(\n", + "compute_config = ComputeConfig(\n", " instance_count=2,\n", " instance_type=\"ml.p4d.24xlarge\",\n", " volume_size_in_gb=96,\n", @@ -384,10 +386,10 @@ "\n", "hugging_face_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n", "\n", - "hyper_parameters = {\n", + "hyperparameters = {\n", " \"dataset_path\": \"/opt/ml/input/data/dataset\",\n", " \"epochs\": 1,\n", - " \"max_steps\": 49,\n", + " \"max_steps\": 100,\n", " \"fsdp\": \"full_shard auto_wrap\",\n", " \"fsdp_transformer_layer_cls_to_wrap\": \"LlamaDecoderLayer\",\n", " \"gradient_checkpointing\": True,\n", @@ -408,15 +410,14 @@ " source_dir=\"distributed-training/scripts\",\n", " requirements=\"requirements.txt\",\n", " entry_script=\"run_clm_no_trainer.py\",\n", - " distribution=TorchDistributionConfig()\n", ")\n", "\n", "model_trainer = ModelTrainer(\n", " training_image=hugging_face_image,\n", - " resource_config=resource_config,\n", - " environment=env,\n", - " hyper_parameters=hyper_parameters,\n", + " compute_config=compute_config,\n", + " hyperparameters=hyperparameters,\n", " source_code_config=source_code_config,\n", + " distribution_config=TorchDistributionConfig(),\n", ")" ] }, @@ -426,60 +427,11 @@ "metadata": {}, "outputs": [], "source": [ - "model_trainer.train(input_data_channels={\"dataset\": training_input_path})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Successful Run - https://tiny.amazon.com/1if17nhk1/IsenLink" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Problems to Solve\n", - "\n", - "### How can we make it easier for user to build an image?\n", - "* There are challenges here because of some of the OS differences in installing some ML frameworks which depend on some lower level dependencies being installed like CUDA - https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Amazon-Linux&target_version=2023&target_type=rpm_network\n", - "\n", - "```python\n", - "custom_image = custom_image_builder(\n", - " image_name=\"my_custom_image\",\n", - " requirements=requirements.txt,\n", - " base_image=\"python:3.10.15-slim\",\n", - " docker_file=\"Dockerfile\",\n", - " enviornment={\"SOME_VAR\": 4},\n", - " upload_ecr=True,\n", - " local_snapshot=True,\n", - ")\n", - "\n", - "model_trainer = ModelTrainer(training_image=custom_image)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from sagemaker.modules.train.model_trainer import ModelTrainer\n", - "\n", - "python_ecr_image = \"public.ecr.aws/docker/library/python:3.10.15-slim\"\n", - "python_docker_image = \"python:3.10.15-slim\"\n", - "\n", - "source_code_config = SourceCodeConfig(\n", - " command=\"echo 'Hello World'\",\n", + "test_data = InputData(\n", + " channel_name=\"dataset\",\n", + " data_source=training_input_path,\n", ")\n", - "\n", - "# This does not work\n", - "model_trainer = ModelTrainer(\n", - " training_image=python_ecr_image,\n", - " source_code_config=source_code_config,\n", - ")" + "model_trainer.train(input_data_config=[test_data])" ] }, { @@ -487,9 +439,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "model_trainer.train(source_code_config=source_code_config)" - ] + "source": [] } ], "metadata": { diff --git a/src/sagemaker/modules/train/__init__.py b/src/sagemaker/modules/train/__init__.py index c5b5d01ed4..51fa17fe04 100644 --- a/src/sagemaker/modules/train/__init__.py +++ b/src/sagemaker/modules/train/__init__.py @@ -12,3 +12,5 @@ # language governing permissions and limitations under the License. """Sagemaker modules train directory.""" from __future__ import absolute_import + +from sagemaker.modules.train.model_trainer import ModelTrainer # noqa: F401 diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 12a288e770..e9fd5c438f 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -17,27 +17,41 @@ import json from typing import Optional, List, Union, Dict, Any -from pydantic import BaseModel, ConfigDict +from pydantic import BaseModel, ConfigDict, PrivateAttr, validate_call from sagemaker_core.resources import TrainingJob from sagemaker_core.shapes import AlgorithmSpecification -from sagemaker import get_execution_role, Session from sagemaker.fw_utils import validate_mp_config +from sagemaker import get_execution_role, Session from sagemaker.modules.configs import ( - ResourceConfig, + ComputeConfig, StoppingCondition, + RetryStrategy, OutputDataConfig, SourceCodeConfig, MPIDistributionConfig, TorchDistributionConfig, + SMDistributedSettings, TrainingImageConfig, Channel, DataSource, S3DataSource, FileSystemDataSource, - VpcConfig, - SMDistributedSettings, + NetworkingConfig, + Tag, + MetricDefinition, + DebugHookConfig, + DebugRuleConfiguration, + ExperimentConfig, + InfraCheckConfig, + ProfilerConfig, + ProfilerRuleConfiguration, + RemoteDebugConfig, + SessionChainingConfig, + TensorBoardOutputConfig, + CheckpointConfig, + InputData, ) from sagemaker.modules.utils import ( _get_repo_name_from_image, @@ -69,6 +83,22 @@ class ModelTrainer(BaseModel): """Class that trains a model using AWS SageMaker. + Example: + ```python + from sagemaker.modules.train import ModelTrainer + from sagemaker.modules.configs import SourceCodeConfig, ComputeConfig, InputDataSource + + source_code_config = SourceCodeConfig(source_dir="source", entry_script="train.py") + training_image = "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-training-image" + model_trainer = ModelTrainer( + training_image=training_image, + source_code_config=source_code_config, + ) + + train_data = InputData(channel_name="train", data_source="s3://bucket/train") + model_trainer.train(input_data_config=[train_data]) + ``` + Attributes: session (Optiona(Session)): The SageMaker session. @@ -76,27 +106,10 @@ class ModelTrainer(BaseModel): role (Optional(str)): The IAM role ARN for the training job. If not specified, the default SageMaker execution role will be used. - base_name (Optional[str]): + base_job_name (Optional[str]): The base name for the training job. If not specified, a default name will be generated using the algorithm name or training image. - resource_config (Optional[ResourceConfig]): - The resource configuration. This is used to specify the compute resources for - the training job. - If not specified, will default to 1 instance of ml.m5.xlarge. - stopping_condition (Optional[StoppingCondition]): - The stopping condition. This is used to specify the different stopping - conditions for the training job. - If not specified, will default to 1 hour max run time. - output_data_config (Optional[OutputDataConfig]): - The output data configuration. This is used to specify the output data location - for the training job. - If not specified, will default to s3:////output/. - input_data_channels (Optional[Union[List[Channel], Dict[str, DataSourceType]]]): - The input data channels for the training job. - Takes a list of Channel objects or a dictionary of channel names to DataSourceType. - DataSourceType can be an S3 URI string, local file path string, - S3DataSource object, or FileSystemDataSource object. source_code_config (Optional[SourceCodeConfig]): The source code configuration. This is used to configure the source code for running the training job. @@ -106,46 +119,90 @@ class ModelTrainer(BaseModel): The distribution settings for the training job. This is used to configure a distributed training job. If specifed, a `source_code_config` must also be provided. + compute_config (Optional[ComputeConfig]): + The compute configuration. This is used to specify the compute resources for + the training job. If not specified, will default to 1 instance of ml.m5.xlarge. + networking_config (Optional[NetworkingConfig]): + The networking configuration. This is used to specify the networking settings + for the training job. + stopping_condition (Optional[StoppingCondition]): + The stopping condition. This is used to specify the different stopping + conditions for the training job. + If not specified, will default to 1 hour max run time. algorithm_name (Optional[str]): The SageMaker marketplace algorithm name/arn to use for the training job. algorithm_name cannot be specified if training_image is specified. training_image (Optional[str]): The training image URI to use for the training job container. training_image cannot be specified if algorithm_name is specified. - For documentaion on sagemaker distributed images see: - https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths.html + To find available sagemaker distributed images, + see: https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths + training_image_config (Optional[TrainingImageConfig]): + Training image Config. This is the configuration to use an image from a private + Docker registry for a traininob. + output_data_config (Optional[OutputDataConfig]): + The output data configuration. This is used to specify the output data location + for the training job. + If not specified, will default to `s3:////output/`. + input_data_config (Optional[List[Union[Channel, InputData]]]): + The input data config for the training job. + Takes a list of Channel or InputData objects. An InputDataSource can be an S3 URI + string, local file path string, S3DataSource object, or FileSystemDataSource object. + checkpoint_config (Optional[CheckpointConfig]): + Contains information about the output location for managed spot training checkpoint + data. training_input_mode (Optional[str]): The input mode for the training job. Valid values are "Pipe", "File", "FastFile". Defaults to "File". - training_image_config (Optional[TrainingImageConfig]: - Training image Config. This is the configuration to use an image from a private - Docker registry for a training job. environment (Optional[Dict[str, str]]): The environment variables for the training job. - hyper_parameters (Optional[Dict[str, Any]]): + hyperparameters (Optional[Dict[str, Any]]): The hyperparameters for the training job. - vpc_config: (Optional[VpcConfig]): - The VPC configuration. + tags (Optional[List[Tag]]): + An array of key-value pairs. You can use tags to categorize your AWS resources + in different ways, for example, by purpose, owner, or environment. """ model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid") session: Optional[Session] = None role: Optional[str] = None - base_name: Optional[str] = None - resource_config: Optional[ResourceConfig] = None - stopping_condition: Optional[StoppingCondition] = None - output_data_config: Optional[OutputDataConfig] = None - input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] = None + base_job_name: Optional[str] = None source_code_config: Optional[SourceCodeConfig] = None distribution_config: Optional[Union[MPIDistributionConfig, TorchDistributionConfig]] = None - algorithm_name: Optional[str] = None + compute_config: Optional[ComputeConfig] = None + networking_config: Optional[NetworkingConfig] = None + stopping_condition: Optional[StoppingCondition] = None training_image: Optional[str] = None - training_input_mode: Optional[str] = "File" training_image_config: Optional[TrainingImageConfig] = None + algorithm_name: Optional[str] = None + output_data_config: Optional[OutputDataConfig] = None + input_data_config: Optional[List[Union[Channel, InputData]]] = None + checkpoint_config: Optional[CheckpointConfig] = None + training_input_mode: Optional[str] = "File" environment: Optional[Dict[str, str]] = None - hyper_parameters: Optional[Dict[str, Any]] = None - vpc_config: Optional[VpcConfig] = None + hyperparameters: Optional[Dict[str, Any]] = None + tags: Optional[List[Tag]] = None + + # Metrics settings + _enable_sage_maker_metrics_time_series: Optional[bool] = PrivateAttr(default=False) + _metric_definitions: Optional[List[MetricDefinition]] = PrivateAttr(default=None) + + # Debugger settings + _debug_hook_config: Optional[DebugHookConfig] = PrivateAttr(default=None) + _debug_rule_configurations: Optional[List[DebugRuleConfiguration]] = PrivateAttr(default=None) + _remote_debug_config: Optional[RemoteDebugConfig] = PrivateAttr(default=None) + _profiler_config: Optional[ProfilerConfig] = PrivateAttr(default=None) + _profiler_rule_configurations: Optional[List[ProfilerRuleConfiguration]] = PrivateAttr( + default=None + ) + _tensor_board_output_config: Optional[TensorBoardOutputConfig] = PrivateAttr(default=None) + + # Additional settings + _retry_strategy: Optional[RetryStrategy] = PrivateAttr(default=None) + _experiment_config: Optional[ExperimentConfig] = PrivateAttr(default=None) + _infra_check_config: Optional[InfraCheckConfig] = PrivateAttr(default=None) + _session_chaining_config: Optional[SessionChainingConfig] = PrivateAttr(default=None) def _validate_training_image_and_algorithm_name( self, training_image: Optional[str], algorithm_name: Optional[str] @@ -239,23 +296,20 @@ def model_post_init(self, __context: Any): self.role = get_execution_role() logger.warning(f"Role not provided. Using default role:\n{self.role}") - if self.base_name is None: + if self.base_job_name is None: if self.algorithm_name: - self.base_name = f"{self.algorithm_name}-job" + self.base_job_name = f"{self.algorithm_name}-job" elif self.training_image: - self.base_name = f"{_get_repo_name_from_image(self.training_image)}-job" - logger.warning(f"Base name not provided. Using default name:\n{self.base_name}") + self.base_job_name = f"{_get_repo_name_from_image(self.training_image)}-job" + logger.warning(f"Base name not provided. Using default name:\n{self.base_job_name}") - if self.resource_config is None: - self.resource_config = ResourceConfig( - volume_size_in_gb=30, - instance_count=1, + if self.compute_config is None: + self.compute_config = ComputeConfig( instance_type=DEFAULT_INSTANCE_TYPE, - volume_kms_key_id=None, - keep_alive_period_in_seconds=None, - instance_groups=None, + instance_count=1, + volume_size_in_gb=30, ) - logger.warning(f"ResourceConfig not provided. Using default:\n{self.resource_config}") + logger.warning(f"ComputeConfig not provided. Using default:\n{self.compute_config}") if self.stopping_condition is None: self.stopping_condition = StoppingCondition( @@ -269,10 +323,10 @@ def model_post_init(self, __context: Any): if self.output_data_config is None: session = self.session - base_name = self.base_name + base_job_name = self.base_job_name self.output_data_config = OutputDataConfig( - s3_output_path=f"s3://{session.default_bucket()}/{base_name}/output", - compression_type="NONE", + s3_output_path=f"s3://{session.default_bucket()}/{base_job_name}", + compression_type="GZIP", kms_key_id=None, ) logger.warning( @@ -283,17 +337,18 @@ def model_post_init(self, __context: Any): if self.training_image: logger.info(f"Training image URI: {self.training_image}") + @validate_call def train( self, - input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] = None, + input_data_config: Optional[List[Union[Channel, InputData]]] = None, wait: bool = True, logs: bool = True, ): """Train a model using AWS SageMaker. Args: - input_data_channels (Optional[Union[List[Channel], Dict[str, DataSourceType]]]): - The input data channels for the training job. + input_data_config (Optional[Union[List[Channel], Dict[str, DataSourceType]]]): + The input data config for the training job. Takes a list of Channel objects or a dictionary of channel names to DataSourceType. DataSourceType can be an S3 URI string, local file path string, S3DataSource object, or FileSystemDataSource object. @@ -304,21 +359,17 @@ def train( Whether to display the training container logs while training. Defaults to True. """ - if input_data_channels: - self.input_data_channels = input_data_channels + if input_data_config: + self.input_data_config = input_data_config input_data_config = [] - if self.input_data_channels: - input_data_config = self._get_input_data_config(self.input_data_channels) + if self.input_data_config: + input_data_config = self._get_input_data_config(self.input_data_config) - # Unfortunately, API requires hyperparameters to be strings string_hyper_parameters = {} - if self.hyper_parameters: - for hyper_parameter, value in self.hyper_parameters.items(): - if isinstance(value, (dict, list)): - string_hyper_parameters[hyper_parameter] = json.dumps(value) - else: - string_hyper_parameters[hyper_parameter] = str(value) + if self.hyperparameters: + for hyper_parameter, value in self.hyperparameters.items(): + string_hyper_parameters[hyper_parameter] = str(value) container_entrypoint = None container_arguments = None @@ -366,20 +417,48 @@ def train( training_image_config=self.training_image_config, container_entrypoint=container_entrypoint, container_arguments=container_arguments, + metric_definitions=self._metric_definitions, + enable_sage_maker_metrics_time_series=self._enable_sage_maker_metrics_time_series, ) + resource_config = self.compute_config._to_resource_config() + vpc_config = self.networking_config._to_vpc_config() if self.networking_config else None + training_job = TrainingJob.create( + training_job_name=_get_unique_name(self.base_job_name), + algorithm_specification=algorithm_specification, + hyper_parameters=string_hyper_parameters, + input_data_config=input_data_config, + resource_config=resource_config, + vpc_config=vpc_config, + # Public Instance Attributes session=self.session.boto_session, role_arn=self.role, - training_job_name=_get_unique_name(self.base_name), - algorithm_specification=algorithm_specification, - resource_config=self.resource_config, + tags=self.tags, stopping_condition=self.stopping_condition, - input_data_config=input_data_config, output_data_config=self.output_data_config, + checkpoint_config=self.checkpoint_config, environment=self.environment, - hyper_parameters=string_hyper_parameters, - vpc_config=self.vpc_config, + enable_managed_spot_training=self.compute_config.enable_managed_spot_training, + enable_inter_container_traffic_encryption=( + self.networking_config.enable_inter_container_traffic_encryption + if self.networking_config + else None + ), + enable_network_isolation=( + self.networking_config.enable_network_isolation if self.networking_config else None + ), + # Private Instance Attributes + debug_hook_config=self._debug_hook_config, + debug_rule_configurations=self._debug_rule_configurations, + remote_debug_config=self._remote_debug_config, + profiler_config=self._profiler_config, + profiler_rule_configurations=self._profiler_rule_configurations, + tensor_board_output_config=self._tensor_board_output_config, + retry_strategy=self._retry_strategy, + experiment_config=self._experiment_config, + infra_check_config=self._infra_check_config, + session_chaining_config=self._session_chaining_config, ) if wait: @@ -414,7 +493,7 @@ def create_input_data_channel(self, channel_name: str, data_source: DataSourceTy s3_uri = self.session.upload_data( path=data_source, bucket=self.session.default_bucket(), - key_prefix=f"{self.base_name}/input/{channel_name}", + key_prefix=f"{self.base_job_name}/input/{channel_name}", ) channel = Channel( channel_name=channel_name, @@ -441,26 +520,34 @@ def create_input_data_channel(self, channel_name: str, data_source: DataSourceTy return channel def _get_input_data_config( - self, input_data_channels: Optional[Union[List[Channel], Dict[str, DataSourceType]]] + self, input_data_channels: Optional[List[Union[Channel, InputData]]] ) -> List[Channel]: """Get the input data configuration for the training job. Args: - input_data_channels (Optional[Union[List[Channel], Dict[str, DataSourceType]]]): - The input data channels for the training job. - Takes a list of Channel objects or a dictionary of channel names to DataSourceType. - DataSourceType can be an S3 URI string, local file path string, - S3DataSource object, or FileSystemDataSource object. + input_data_channels (Optional[List[Union[Channel, InputData]]]): + The input data config for the training job. + Takes a list of Channel or InputData objects. An InputDataSource can be an S3 URI + string, local file path string, S3DataSource object, or FileSystemDataSource object. """ if input_data_channels is None: return [] - if isinstance(input_data_channels, list): - return input_data_channels - return [ - self.create_input_data_channel(channel_name, data_source) - for channel_name, data_source in input_data_channels.items() - ] + channels = [] + for input_data in input_data_channels: + if isinstance(input_data, Channel): + channels.append(input) + elif isinstance(input_data, InputData): + channel = self.create_input_data_channel( + input_data.channel_name, input_data.data_source + ) + channels.append(channel) + else: + raise ValueError( + f"Invalid input data channel: {input_data}. " + + "Must be a Channel or InputDataSource." + ) + return channels def _write_source_code_config_json(self, source_code_config: SourceCodeConfig): """Write the source code configuration to a JSON file.""" @@ -492,7 +579,7 @@ def _prepare_train_script( if ( source_code_config.entry_script and not source_code_config.command - and not source_code_config.distribution + and not distribution_config ): if source_code_config.entry_script.endswith(".py"): base_command = f"$SM_PYTHON_CMD {source_code_config.entry_script}" @@ -537,6 +624,134 @@ def _prepare_train_script( with open(os.path.join(SM_DRIVERS_LOCAL_PATH, TRAIN_SCRIPT), "w") as f: f.write(train_script) - def get_training_image_uri(self) -> str: - """Get the training image URI for the training job.""" - return self.training_image + def with_metric_settings( + self, + enable_sage_maker_metrics_time_series: bool = True, + metric_definitions: Optional[List[MetricDefinition]] = None, + ) -> "ModelTrainer": + """Set the metrics configuration for the training job. + + Example: + ```python + model_trainer = ModelTrainer(...).with_metric_settings( + enable_sage_maker_metrics_time_series=True, + metric_definitions=[ + MetricDefinition( + name="loss", + regex="Loss: (.*?),", + ), + MetricDefinition( + name="accuracy", + regex="Accuracy: (.*?),", + ), + ] + ) + ``` + + Args: + enable_sage_maker_metrics_time_series (Optional[bool]): + Whether to enable SageMaker metrics time series. Defaults to True. + metric_definitions (Optional[List[MetricDefinition]]): + A list of metric definition objects. Each object specifies + the metric name and regular expressions used to parse algorithm logs. + SageMaker publishes each metric to Amazon CloudWatch. + """ + self._enable_sage_maker_metrics_time_series = enable_sage_maker_metrics_time_series + self._metric_definitions = metric_definitions + return self + + def with_debugger_settings( + self, + debug_hook_config: Optional[DebugHookConfig] = None, + debug_rule_configurations: Optional[List[DebugRuleConfiguration]] = None, + remote_debug_config: Optional[RemoteDebugConfig] = None, + profiler_config: Optional[ProfilerConfig] = None, + profiler_rule_configurations: Optional[List[ProfilerRuleConfiguration]] = None, + tensor_board_output_config: Optional[TensorBoardOutputConfig] = None, + ) -> "ModelTrainer": + """Set the configuration for settings related to Amazon SageMaker Debugger. + + Example: + ```python + model_trainer = ModelTrainer(...).with_debugger_settings( + debug_hook_config=DebugHookConfig( + s3_output_path="s3://bucket/path", + collection_configurations=[ + CollectionConfiguration( + collection_name="some_collection", + collection_parameters={ + "include_regex": ".*", + } + ) + ] + ) + ) + ``` + + Args: + debug_hook_config (Optional[DebugHookConfig]): + Configuration information for the Amazon SageMaker Debugger hook parameters, + metric and tensor collections, and storage paths. + To learn more see: https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-createtrainingjob-api.html + debug_rule_configurations (Optional[List[DebugRuleConfiguration]]): + Configuration information for Amazon SageMaker Debugger rules for debugging + output ensors. + remote_debug_config (Optional[RemoteDebugConfig]): + Configuration for remote debugging. To learn more see: + https://docs.aws.amazon.com/sagemaker/latest/dg/train-remote-debugging.html + profiler_config (ProfilerConfig): + Configuration information for Amazon SageMaker Debugger system monitoring, + framework profiling, and storage paths. + profiler_rule_configurations (List[ProfilerRuleConfiguration]): + Configuration information for Amazon SageMaker Debugger rules for profiling + system and framework metrics. + tensor_board_output_config (Optional[TensorBoardOutputConfig]): + Configuration of storage locations for the Amazon SageMaker Debugger TensorBoard + output data. + """ + self._debug_hook_config = debug_hook_config + self._debug_rule_configurations = debug_rule_configurations + self._remote_debug_config = remote_debug_config + self._profiler_config = profiler_config + self._profiler_rule_configurations = profiler_rule_configurations + self._tensor_board_output_config = tensor_board_output_config + return self + + def with_additional_settings( + self, + retry_strategy: Optional[RetryStrategy] = None, + experiment_config: Optional[ExperimentConfig] = None, + infra_check_config: Optional[InfraCheckConfig] = None, + session_chaining_config: Optional[SessionChainingConfig] = None, + ) -> "ModelTrainer": + """Set any additional settings for the training job. + + Example: + ```python + model_trainer = ModelTrainer(...).with_additional_settings( + experiment_config=ExperimentConfig( + experiment_name="my-experiment", + trial_name="my-trial", + ) + ) + ``` + + Args: + retry_strategy (Optional[RetryStrategy]): + The number of times to retry the job when the job fails due to an + `InternalServerError`. + experiment_config (Optional[ExperimentConfig]): + Configuration information for the Amazon SageMaker Experiment. + Associates a SageMaker job as a trial component with an experiment and trial + infra_check_config (Optional[InfraCheckConfig]): + Contains information about the infrastructure health check configuration for the + training job. + session_chaining_config (Optional[SessionChainingConfig]): + Contains information about attribute-based access control (ABAC) for the training + job. + """ + self._retry_strategy = retry_strategy + self._experiment_config = experiment_config + self._infra_check_config = infra_check_config + self._session_chaining_config = session_chaining_config + return self diff --git a/src/sagemaker/modules/utils.py b/src/sagemaker/modules/utils.py index a1e391f45c..b401d3e414 100644 --- a/src/sagemaker/modules/utils.py +++ b/src/sagemaker/modules/utils.py @@ -16,7 +16,9 @@ import re import os from datetime import datetime -from typing import Literal +from typing import Literal, Any + +from sagemaker_core.shapes import Unassigned def _is_valid_s3_uri(path: str, path_type: Literal["File", "Directory", "Any"] = "Any") -> bool: @@ -107,3 +109,11 @@ def _get_repo_name_from_image(image: str) -> str: str: The repository name """ return image.split("/")[-1].split(":")[0] + + +def convert_unassigned_to_none(instance) -> Any: + """Convert Unassigned values to None for any instance.""" + for name, value in instance.__dict__.items(): + if isinstance(value, Unassigned): + setattr(instance, name, None) + return instance diff --git a/tests/unit/sagemaker/modules/train/test_model_trainer.py b/tests/unit/sagemaker/modules/train/test_model_trainer.py index 57ee78794c..5a5a97b3bb 100644 --- a/tests/unit/sagemaker/modules/train/test_model_trainer.py +++ b/tests/unit/sagemaker/modules/train/test_model_trainer.py @@ -20,12 +20,24 @@ from sagemaker.modules.train.model_trainer import ModelTrainer from sagemaker.modules.constants import DEFAULT_INSTANCE_TYPE from sagemaker.modules.configs import ( - ResourceConfig, + ComputeConfig, StoppingCondition, + RetryStrategy, OutputDataConfig, SourceCodeConfig, S3DataSource, FileSystemDataSource, + MetricDefinition, + DebugHookConfig, + DebugRuleConfiguration, + RemoteDebugConfig, + ProfilerConfig, + ProfilerRuleConfiguration, + TensorBoardOutputConfig, + ExperimentConfig, + InfraCheckConfig, + SessionChainingConfig, + InputData, ) from tests.unit import DATA_DIR @@ -33,17 +45,10 @@ DEFAULT_IMAGE = "000000000000.dkr.ecr.us-west-2.amazonaws.com/dummy-image:latest" DEFAULT_BUCKET = "sagemaker-us-west-2-000000000000" DEFAULT_ROLE = "arn:aws:iam::000000000000:role/test-role" -DEFAULT_RESOURCE_CONFIG = ResourceConfig( - instance_count=1, - instance_type=DEFAULT_INSTANCE_TYPE, - volume_size_in_gb=30, - volume_kms_key_id=None, - keep_alive_period_in_seconds=None, - instance_groups=None, -) +DEFAULT_COMPUTE_CONFIG = ComputeConfig(instance_type=DEFAULT_INSTANCE_TYPE, instance_count=1) DEFAULT_OUTPUT_DATA_CONFIG = OutputDataConfig( - s3_output_path=f"s3://{DEFAULT_BUCKET}/{DEFAULT_BASE_NAME}/output", - compression_type="NONE", + s3_output_path=f"s3://{DEFAULT_BUCKET}/{DEFAULT_BASE_NAME}", + compression_type="GZIP", kms_key_id=None, ) DEFAULT_STOPPING_CONDITION = StoppingCondition( @@ -66,6 +71,7 @@ def modules_session(): session_instance = session_mock.return_value session_instance.default_bucket.return_value = DEFAULT_BUCKET session_instance.get_caller_identity_arn.return_value = DEFAULT_ROLE + session_instance.boto_session = MagicMock(spec="boto3.session.Session") yield session_instance @@ -74,9 +80,9 @@ def model_trainer(): trainer = ModelTrainer( training_image=DEFAULT_IMAGE, role=DEFAULT_ROLE, - resource_config=MagicMock(spec=ResourceConfig), - stopping_condition=MagicMock(spec=StoppingCondition), - output_data_config=MagicMock(spec=OutputDataConfig), + compute_config=DEFAULT_COMPUTE_CONFIG, + stopping_condition=DEFAULT_STOPPING_CONDITION, + output_data_config=DEFAULT_OUTPUT_DATA_CONFIG, ) return trainer @@ -132,10 +138,10 @@ def test_model_trainer_param_validation(test_case, modules_session): trainer = ModelTrainer(**test_case["init_params"], session=modules_session) assert trainer is not None assert trainer.training_image == DEFAULT_IMAGE - assert trainer.resource_config == DEFAULT_RESOURCE_CONFIG + assert trainer.compute_config == DEFAULT_COMPUTE_CONFIG assert trainer.output_data_config == DEFAULT_OUTPUT_DATA_CONFIG assert trainer.stopping_condition == DEFAULT_STOPPING_CONDITION - assert trainer.base_name == DEFAULT_BASE_NAME + assert trainer.base_job_name == DEFAULT_BASE_NAME @patch("sagemaker.modules.train.model_trainer.TrainingJob") @@ -151,11 +157,13 @@ def test_train_with_default_params(mock_training_job, model_trainer): @patch("sagemaker.modules.train.model_trainer.TrainingJob") @patch.object(ModelTrainer, "_get_input_data_config") def test_train_with_input_data_channels(mock_get_input_config, mock_training_job, model_trainer): - mock_input_data_channels = {"train": "train/dir", "test": "test/dir"} + train_data = InputData(channel_name="train", data_source="train/dir") + test_data = InputData(channel_name="test", data_source="test/dir") + mock_input_data_config = [train_data, test_data] - model_trainer.train(input_data_channels=mock_input_data_channels) + model_trainer.train(input_data_config=mock_input_data_config) - mock_get_input_config.assert_called_once_with(mock_input_data_channels) + mock_get_input_config.assert_called_once_with(mock_input_data_config) mock_training_job.create.assert_called_once() @@ -227,3 +235,154 @@ def test_create_input_data_channel(mock_upload_data, model_trainer, test_case): assert channel.data_source.file_system_data_source == test_case["data_source"] else: assert channel.data_source.s3_data_source.s3_uri == expected_s3_uri + + +@patch("sagemaker.modules.train.model_trainer.TrainingJob") +def test_metric_settings(mock_training_job, modules_session): + image_uri = DEFAULT_IMAGE + role = DEFAULT_ROLE + metric_definition = MetricDefinition( + name="test-metric", + regex="test-regex", + ) + + model_trainer = ModelTrainer( + training_image=image_uri, + session=modules_session, + role=role, + ).with_metric_settings( + enable_sage_maker_metrics_time_series=True, metric_definitions=[metric_definition] + ) + + assert model_trainer._enable_sage_maker_metrics_time_series + assert model_trainer._metric_definitions == [metric_definition] + + with patch("sagemaker.modules.train.model_trainer.Session.upload_data") as mock_upload_data: + mock_upload_data.return_value = "s3://dummy-bucket/dummy-prefix" + model_trainer.train() + + mock_training_job.create.assert_called_once() + assert mock_training_job.create.call_args.kwargs[ + "algorithm_specification" + ].metric_definitions == [metric_definition] + + assert mock_training_job.create.call_args.kwargs[ + "algorithm_specification" + ].enable_sage_maker_metrics_time_series + + +@patch("sagemaker.modules.train.model_trainer.TrainingJob") +def test_debugger_settings(mock_training_job, modules_session): + image_uri = DEFAULT_IMAGE + role = DEFAULT_ROLE + + debug_hook_config = DebugHookConfig(s3_output_path="s3://dummy-bucket/dummy-prefix") + debug_rule_config = DebugRuleConfiguration( + rule_configuration_name="rule-name", + rule_evaluator_image=image_uri, + rule_parameters={"parameter": "value"}, + ) + remote_debug_config = RemoteDebugConfig( + enable_remote_debug=True, + ) + profiler_config = ProfilerConfig(s3_output_path="s3://dummy-bucket/dummy-prefix") + profiler_rule_config = ProfilerRuleConfiguration( + rule_configuration_name="rule-name", + rule_evaluator_image=image_uri, + ) + tensor_board_output_config = TensorBoardOutputConfig( + s3_output_path="s3://dummy-bucket/dummy-prefix" + ) + + model_trainer = ModelTrainer( + training_image=image_uri, + session=modules_session, + role=role, + ).with_debugger_settings( + debug_hook_config=debug_hook_config, + debug_rule_configurations=debug_rule_config, + remote_debug_config=remote_debug_config, + profiler_config=profiler_config, + profiler_rule_configurations=profiler_rule_config, + tensor_board_output_config=tensor_board_output_config, + ) + + assert model_trainer._debug_hook_config == debug_hook_config + assert model_trainer._debug_rule_configurations == debug_rule_config + assert model_trainer._remote_debug_config == remote_debug_config + assert model_trainer._profiler_config == profiler_config + assert model_trainer._profiler_rule_configurations == profiler_rule_config + assert model_trainer._tensor_board_output_config == tensor_board_output_config + + with patch("sagemaker.modules.train.model_trainer.Session.upload_data") as mock_upload_data: + mock_upload_data.return_value = "s3://dummy-bucket/dummy-prefix" + model_trainer.train() + + mock_training_job.create.assert_called_once() + assert mock_training_job.create.call_args.kwargs["debug_hook_config"] == debug_hook_config + assert ( + mock_training_job.create.call_args.kwargs["debug_rule_configurations"] + == debug_rule_config + ) + assert ( + mock_training_job.create.call_args.kwargs["remote_debug_config"] == remote_debug_config + ) + assert mock_training_job.create.call_args.kwargs["profiler_config"] == profiler_config + assert ( + mock_training_job.create.call_args.kwargs["profiler_rule_configurations"] + == profiler_rule_config + ) + assert ( + mock_training_job.create.call_args.kwargs["tensor_board_output_config"] + == tensor_board_output_config + ) + + +@patch("sagemaker.modules.train.model_trainer.TrainingJob") +def test_additional_settings(mock_training_job, modules_session): + image_uri = DEFAULT_IMAGE + role = DEFAULT_ROLE + + retry_strategy = RetryStrategy( + maximum_retry_attempts=3, + ) + + experiment_config = ExperimentConfig( + experiment_name="experiment-name", + trial_name="trial-name", + ) + infra_check_config = InfraCheckConfig( + enable_infra_check=True, + ) + session_chaining_config = SessionChainingConfig( + enable_session_tag_chaining=True, + ) + model_trainer = ModelTrainer( + training_image=image_uri, + session=modules_session, + role=role, + ).with_additional_settings( + retry_strategy=retry_strategy, + experiment_config=experiment_config, + infra_check_config=infra_check_config, + session_chaining_config=session_chaining_config, + ) + + assert model_trainer._retry_strategy == retry_strategy + assert model_trainer._experiment_config == experiment_config + assert model_trainer._infra_check_config == infra_check_config + assert model_trainer._session_chaining_config == session_chaining_config + + with patch("sagemaker.modules.train.model_trainer.Session.upload_data") as mock_upload_data: + mock_upload_data.return_value = "s3://dummy-bucket/dummy-prefix" + model_trainer.train() + + mock_training_job.create.assert_called_once() + + assert mock_training_job.create.call_args.kwargs["retry_strategy"] == retry_strategy + assert mock_training_job.create.call_args.kwargs["experiment_config"] == experiment_config + assert mock_training_job.create.call_args.kwargs["infra_check_config"] == infra_check_config + assert ( + mock_training_job.create.call_args.kwargs["session_chaining_config"] + == session_chaining_config + ) From 8cc19a32e1c3989c68d657ee0fd213359170570b Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Thu, 7 Nov 2024 11:24:35 -0800 Subject: [PATCH 17/79] Mask Sensitive Env Logs in Container (#1568) --- src/sagemaker/modules/templates.py | 4 -- .../base_model_trainer.ipynb | 12 ++++- .../container_drivers/scripts/environment.py | 47 +++++++++++++++++++ .../scripts/test_enviornment.py | 43 ++++++++++++++++- 4 files changed, 99 insertions(+), 7 deletions(-) diff --git a/src/sagemaker/modules/templates.py b/src/sagemaker/modules/templates.py index 55ad44d81d..400594ca51 100644 --- a/src/sagemaker/modules/templates.py +++ b/src/sagemaker/modules/templates.py @@ -73,10 +73,6 @@ cat /opt/ml/input/config/inputdataconfig.json echo -echo "/opt/ml/input/config/hyperparameters.json:" -cat /opt/ml/input/config/hyperparameters.json -echo - echo "/opt/ml/input/data/sm_drivers/sourcecodeconfig.json" cat /opt/ml/input/data/sm_drivers/sourcecodeconfig.json echo diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index 213322aa75..e32b0211d2 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -74,16 +74,24 @@ "\n", "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", "\n", - "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", - "\n", "source_code_config = SourceCodeConfig(\n", " source_dir=\"basic-script-mode\",\n", " command=\"python custom_script.py\",\n", ")\n", "\n", + "hyperparameters = {\n", + " \"secret_token\": \"123456\",\n", + "}\n", + "\n", + "env_vars = {\n", + " \"PASSWORD\": \"123456\"\n", + "}\n", + "\n", "model_trainer = ModelTrainer(\n", " training_image=pytorch_image,\n", " source_code_config=source_code_config,\n", + " hyperparameters=hyperparameters,\n", + " environment=env_vars,\n", ")\n", "\n", "model_trainer.train()" diff --git a/src/sagemaker/modules/train/container_drivers/scripts/environment.py b/src/sagemaker/modules/train/container_drivers/scripts/environment.py index bb6ff634aa..163b0ea8b4 100644 --- a/src/sagemaker/modules/train/container_drivers/scripts/environment.py +++ b/src/sagemaker/modules/train/container_drivers/scripts/environment.py @@ -47,6 +47,9 @@ ENV_OUTPUT_FILE = "/opt/ml/input/data/sm_drivers/scripts/sm_training.env" +SENSITIVE_KEYWORDS = ["SECRET", "PASSWORD", "KEY", "TOKEN", "PRIVATE", "CREDS", "CREDENTIALS"] +HIDDEN_VALUE = "******" + def num_cpus() -> int: """Return the number of CPUs available in the current container. @@ -199,6 +202,50 @@ def set_env( else: f.write(f"export {key}='{value}'\n") + logger.info("Environment Variables:") + log_env_variables(env_vars_dict=env_vars) + + +def mask_sensitive_info(data): + """Recursively mask sensitive information in a dictionary.""" + if isinstance(data, dict): + for k, v in data.items(): + if isinstance(v, dict): + data[k] = mask_sensitive_info(v) + elif isinstance(v, str) and any( + keyword.lower() in k.lower() for keyword in SENSITIVE_KEYWORDS + ): + data[k] = HIDDEN_VALUE + return data + + +def log_key_value(key: str, value: str): + """Log a key-value pair, masking sensitive values if necessary.""" + if any(keyword.lower() in key.lower() for keyword in SENSITIVE_KEYWORDS): + logger.info("%s=%s", key, HIDDEN_VALUE) + elif isinstance(value, dict): + masked_value = mask_sensitive_info(value) + logger.info("%s=%s", key, json.dumps(masked_value)) + else: + try: + decoded_value = json.loads(value) + if isinstance(decoded_value, dict): + masked_value = mask_sensitive_info(decoded_value) + logger.info("%s=%s", key, json.dumps(masked_value)) + else: + logger.info("%s=%s", key, decoded_value) + except (json.JSONDecodeError, TypeError): + logger.info("%s=%s", key, value) + + +def log_env_variables(env_vars_dict: Dict[str, Any]): + """Log Environment Variables from the environment and an env_vars_dict.""" + for key, value in os.environ.items(): + log_key_value(key, value) + + for key, value in env_vars_dict.items(): + log_key_value(key, value) + def main(): """Main function to set the environment variables for the training job container.""" diff --git a/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py b/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py index 8dc6e330df..6d7c40f23a 100644 --- a/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py +++ b/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py @@ -14,10 +14,18 @@ from __future__ import absolute_import import os +import io +import logging from unittest.mock import patch -from sagemaker.modules.train.container_drivers.scripts.environment import set_env +from sagemaker.modules.train.container_drivers.scripts.environment import ( + set_env, + log_key_value, + log_env_variables, + mask_sensitive_info, + HIDDEN_VALUE, +) RESOURCE_CONFIG = dict( current_host="algo-1", @@ -129,6 +137,39 @@ def test_set_env(mock_num_cpus, mock_num_gpus, mock_num_neurons): assert not os.path.exists(OUTPUT_FILE) +@patch.dict(os.environ, {"SECRET_TOKEN": "122345678", "CLEAR_DATA": "123456789"}, clear=True) +def test_log_env_variables(): + log_stream = io.StringIO() + handler = logging.StreamHandler(log_stream) + + logger = logging.getLogger("sagemaker.modules.train.container_drivers.scripts.environment") + logger.addHandler(handler) + logger.setLevel(logging.INFO) + + env_vars = { + "SM_MODEL_DIR": "/opt/ml/model", + "SM_INPUT_DIR": "/opt/ml/input", + "SM_HPS": {"batch_size": 32, "learning_rate": 0.001, "access_token": "123456789"}, + "SM_HP_BATCH_SIZE": 32, + "SM_HP_LEARNING_RATE": 0.001, + "SM_HP_ACCESS_TOKEN": "123456789", + } + log_env_variables(env_vars_dict=env_vars) + + log_output = log_stream.getvalue() + + assert f"SECRET_TOKEN={HIDDEN_VALUE}" in log_output + assert "CLEAR_DATA=123456789" in log_output + assert "SM_MODEL_DIR=/opt/ml/model" in log_output + assert ( + f'SM_HPS={{"batch_size": 32, "learning_rate": 0.001, "access_token": "{HIDDEN_VALUE}"}}' + in log_output + ) + assert "SM_HP_BATCH_SIZE=32" in log_output + assert "SM_HP_LEARNING_RATE=0.001" in log_output + assert f"SM_HP_ACCESS_TOKEN={HIDDEN_VALUE}" in log_output + + def _remove_extra_lines(string): """Removes extra blank lines from a string.""" return "\n".join([line for line in string.splitlines() if line.strip()]) From a8ed4ec887b727fa5af4cd0841817c9164b48851 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Fri, 8 Nov 2024 10:38:38 -0800 Subject: [PATCH 18/79] Fix bug in script mode setup ModelTrainer (#1575) --- .gitignore | 1 + src/sagemaker/modules/constants.py | 1 + src/sagemaker/modules/templates.py | 4 +++ .../train/container_drivers/mpi_driver.py | 5 +-- .../train/container_drivers/pytorch_driver.py | 4 ++- .../modules/train/container_drivers/utils.py | 31 ++++++++++++------- src/sagemaker/modules/train/model_trainer.py | 12 +++++++ 7 files changed, 43 insertions(+), 15 deletions(-) diff --git a/.gitignore b/.gitignore index 39a442bcd6..b87341ca4d 100644 --- a/.gitignore +++ b/.gitignore @@ -34,6 +34,7 @@ env/ **/_repack_script_launcher.sh src/sagemaker/modules/train/container_drivers/sm_train.sh src/sagemaker/modules/train/container_drivers/sourcecodeconfig.json +src/sagemaker/modules/train/container_drivers/distribution.json tests/data/**/_repack_model.py tests/data/experiment/sagemaker-dev-1.0.tar.gz src/sagemaker/serve/tmp_workspace \ No newline at end of file diff --git a/src/sagemaker/modules/constants.py b/src/sagemaker/modules/constants.py index a7f9a897ff..97367b2a61 100644 --- a/src/sagemaker/modules/constants.py +++ b/src/sagemaker/modules/constants.py @@ -26,6 +26,7 @@ ) SOURCE_CODE_CONFIG_JSON = "sourcecodeconfig.json" +DISTRIBUTION_JSON = "distribution.json" TRAIN_SCRIPT = "sm_train.sh" DEFAULT_CONTAINER_ENTRYPOINT = ["/bin/bash"] diff --git a/src/sagemaker/modules/templates.py b/src/sagemaker/modules/templates.py index 400594ca51..491b0cb2b6 100644 --- a/src/sagemaker/modules/templates.py +++ b/src/sagemaker/modules/templates.py @@ -77,6 +77,10 @@ cat /opt/ml/input/data/sm_drivers/sourcecodeconfig.json echo +echo "/opt/ml/input/data/sm_drivers/distribution.json" +cat /opt/ml/input/data/sm_drivers/distribution.json +echo + echo "Setting up environment variables" $SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/scripts/environment.py source /opt/ml/input/data/sm_drivers/scripts/sm_training.env diff --git a/src/sagemaker/modules/train/container_drivers/mpi_driver.py b/src/sagemaker/modules/train/container_drivers/mpi_driver.py index 989c1cf3d3..29b047ea89 100644 --- a/src/sagemaker/modules/train/container_drivers/mpi_driver.py +++ b/src/sagemaker/modules/train/container_drivers/mpi_driver.py @@ -19,6 +19,7 @@ from utils import ( logger, read_source_code_config_json, + read_distribution_json, get_process_count, execute_commands, write_failure_file, @@ -55,7 +56,7 @@ def main(): """ source_code_config = read_source_code_config_json() - distribution = source_code_config.get("distribution", {}) + distribution = read_distribution_json() sm_distributed_settings = distribution.get("smdistributed_settings", {}) sm_current_host = os.environ["SM_CURRENT_HOST"] @@ -73,7 +74,7 @@ def main(): host_list = json.loads(os.environ["SM_HOSTS"]) host_count = int(os.environ["SM_HOST_COUNT"]) - process_count = get_process_count(source_code_config) + process_count = get_process_count(distribution) if process_count > 1: host_list = ["{}:{}".format(host, process_count) for host in host_list] diff --git a/src/sagemaker/modules/train/container_drivers/pytorch_driver.py b/src/sagemaker/modules/train/container_drivers/pytorch_driver.py index b5d72fbe76..049c9ed60a 100644 --- a/src/sagemaker/modules/train/container_drivers/pytorch_driver.py +++ b/src/sagemaker/modules/train/container_drivers/pytorch_driver.py @@ -20,6 +20,7 @@ from utils import ( logger, read_source_code_config_json, + read_distribution_json, get_process_count, get_python_executable, SM_EFA_NCCL_INSTANCES, @@ -62,8 +63,9 @@ def setup_env(): def create_commands(): """Create the Torch Distributed command to execute""" source_code_config = read_source_code_config_json() + distribution = read_distribution_json() - process_count = get_process_count(source_code_config) + process_count = get_process_count(distribution) host_count = int(os.environ["SM_HOST_COUNT"]) torch_cmd = [] diff --git a/src/sagemaker/modules/train/container_drivers/utils.py b/src/sagemaker/modules/train/container_drivers/utils.py index ae05df8994..219fcf4b62 100644 --- a/src/sagemaker/modules/train/container_drivers/utils.py +++ b/src/sagemaker/modules/train/container_drivers/utils.py @@ -36,8 +36,9 @@ TrainingJob - {os.environ['TRAINING_JOB_NAME']} """ -USER_CODE_PATH = "/opt/ml/input/data/code" -SOURCE_CODE_CONFIG_JSON = "/opt/ml/input/data/sm_code/sourcecodeconfig.json" +USER_CODE_PATH = "/opt/ml/input/data/sm_code" +SOURCE_CODE_CONFIG_JSON = "/opt/ml/input/data/sm_drivers/sourcecodeconfig.json" +DISTRIBUTION_JSON = "/opt/ml/input/data/sm_drivers/distribution.json" SM_EFA_NCCL_INSTANCES = [ "ml.g4dn.8xlarge", @@ -67,19 +68,25 @@ def write_failure_file(message: str = DEFAULT_FAILURE_MESSAGE): def read_source_code_config_json(source_code_config_file: Dict[str, Any] = SOURCE_CODE_CONFIG_JSON): """Read the source code config json file.""" with open(source_code_config_file, "r") as f: - distribution_config = json.load(f) - return distribution_config + source_code_config_json = json.load(f) + return source_code_config_json -def get_process_count(source_code_config: Dict[str, Any]) -> int: +def read_distribution_json(distribution_file: Dict[str, Any] = DISTRIBUTION_JSON): + """Read the distribution json file.""" + with open(distribution_file, "r") as f: + distribution_json = json.load(f) + return distribution_json + + +def get_process_count(distribution: Dict[str, Any]) -> int: """Get the number of processes to run on each node in the training job.""" - if source_code_config.get("distribution", {}).get("process_count_per_node") is not None: - return int(source_code_config["distribution"]["process_count_per_node"]) - if os.environ.get("SM_NUM_GPUS") is not None: - return int(os.environ["SM_NUM_GPUS"]) - if os.environ.get("SM_NUM_NEURONS") is not None: - return int(os.environ["SM_NUM_NEURONS"]) - return 1 # Default to 1 process per node + return ( + int(distribution.get("process_count_per_node", 0)) + or int(os.environ.get("SM_NUM_GPUS", 0)) + or int(os.environ.get("SM_NUM_NEURONS", 0)) + or 1 + ) def get_python_executable() -> str: diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index e9fd5c438f..8d103195aa 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -70,6 +70,7 @@ DEFAULT_CONTAINER_ENTRYPOINT, DEFAULT_CONTAINER_ARGUMENTS, SOURCE_CODE_CONFIG_JSON, + DISTRIBUTION_JSON, ) from sagemaker.modules.templates import ( TRAIN_SCRIPT_TEMPLATE, @@ -385,6 +386,7 @@ def train( self._prepare_train_script( source_code_config=self.source_code_config, + distribution_config=self.distribution_config, ) if self.distribution_config: smd_modelparallel_parameters = getattr( @@ -397,6 +399,8 @@ def train( smd_modelparallel_parameters ) self._write_source_code_config_json(self.source_code_config) + if self.distribution_config: + self._write_distribution_config_json(self.distribution_config) # Create an input channel for drivers packaged by the sdk sm_drivers_channel = self.create_input_data_channel(SM_DRIVERS, SM_DRIVERS_LOCAL_PATH) @@ -555,6 +559,14 @@ def _write_source_code_config_json(self, source_code_config: SourceCodeConfig): with open(file_path, "w") as f: f.write(source_code_config.model_dump_json()) + def _write_distribution_config_json( + self, distribution: Union[MPIDistributionConfig, TorchDistributionConfig] + ): + """Write the distribution configuration to a JSON file.""" + file_path = os.path.join(SM_DRIVERS_LOCAL_PATH, DISTRIBUTION_JSON) + with open(file_path, "w") as f: + f.write(distribution.model_dump_json()) + def _prepare_train_script( self, source_code_config: SourceCodeConfig, From ce55d45101da5dc27b6a2dbbeca3a33df7499da1 Mon Sep 17 00:00:00 2001 From: xiongz945 <54782408+xiongz945@users.noreply.github.com> Date: Fri, 8 Nov 2024 19:23:15 -0800 Subject: [PATCH 19/79] Feature: ModelBuilder supports HuggingFace Models with benchmark data and deployment configs (#1572) --- src/sagemaker/serve/builder/model_builder.py | 124 ++++- .../serve/builder/test_model_builder.py | 486 +++++++++++++++++- 2 files changed, 592 insertions(+), 18 deletions(-) diff --git a/src/sagemaker/serve/builder/model_builder.py b/src/sagemaker/serve/builder/model_builder.py index 802711e427..157222b437 100644 --- a/src/sagemaker/serve/builder/model_builder.py +++ b/src/sagemaker/serve/builder/model_builder.py @@ -14,6 +14,7 @@ from __future__ import absolute_import import importlib.util +import json import uuid from typing import Any, Type, List, Dict, Optional, Union from dataclasses import dataclass, field @@ -24,6 +25,8 @@ from pathlib import Path from sagemaker.enums import Tag +from sagemaker.jumpstart.accessors import JumpStartS3PayloadAccessor +from sagemaker.jumpstart.utils import get_jumpstart_content_bucket from sagemaker.s3 import S3Downloader from sagemaker import Session @@ -879,7 +882,7 @@ def build( # pylint: disable=R0911 if isinstance(self.model, str): model_task = None - if self._is_jumpstart_model_id(): + if self._is_jumpstart_model_id() or self._use_jumpstart_equivalent(): self.model_hub = ModelHub.JUMPSTART return self._build_for_jumpstart() self.model_hub = ModelHub.HUGGINGFACE @@ -1514,3 +1517,122 @@ def _optimize_prepare_for_hf(self): should_upload_artifacts=True, ) self.pysdk_model.env.update(env) + + def display_benchmark_metrics(self, **kwargs): + """Display Markdown Benchmark Metrics for deployment configs.""" + if not isinstance(self.model, str): + raise ValueError("Benchmarking is only supported for JumpStart or HuggingFace models") + if self._is_jumpstart_model_id() or self._use_jumpstart_equivalent(): + return super().display_benchmark_metrics(**kwargs) + else: + raise ValueError("This model does not have benchmark metrics yet") + + def get_deployment_config(self) -> Optional[Dict[str, Any]]: + """Gets the deployment config to apply to the model. + + Returns: + Optional[Dict[str, Any]]: Deployment config to apply to this model. + """ + if not isinstance(self.model, str): + raise ValueError( + "Deployment config is only supported for JumpStart or HuggingFace models" + ) + if self._is_jumpstart_model_id() or self._use_jumpstart_equivalent(): + return super().get_deployment_config() + else: + raise ValueError("This model does not have any deployment config yet") + + def list_deployment_configs(self) -> List[Dict[str, Any]]: + """List deployment configs for the model in the current region. + + Returns: + List[Dict[str, Any]]: A list of deployment configs. + """ + if not isinstance(self.model, str): + raise ValueError( + "Deployment config is only supported for JumpStart or HuggingFace models" + ) + if self._is_jumpstart_model_id() or self._use_jumpstart_equivalent(): + return super().list_deployment_configs() + else: + raise ValueError("This model does not have any deployment config yet") + + def set_deployment_config(self, config_name: str, instance_type: str) -> None: + """Sets the deployment config to apply to the model. + + Args: + config_name (str): + The name of the deployment config to apply to the model. + Call list_deployment_configs to see the list of config names. + instance_type (str): + The instance_type that the model will use after setting + the config. + """ + if not isinstance(self.model, str): + raise ValueError( + "Deployment config is only supported for JumpStart or HuggingFace models" + ) + if self._is_jumpstart_model_id() or self._use_jumpstart_equivalent(): + logger.warning( + "If there are existing deployment configurations, " + "they will be overwritten by the config %s", + config_name, + ) + return super().set_deployment_config(config_name, instance_type) + else: + raise ValueError(f"The deployment config {config_name} cannot be set on this model") + + def _use_jumpstart_equivalent(self): + """Check if the HuggingFace model has a JumpStart equivalent. + + Replace it with the equivalent if there's one + """ + if not hasattr(self, "_has_jumpstart_equivalent"): + self._jumpstart_mapping = self._retrieve_hugging_face_model_mapping() + self._has_jumpstart_equivalent = self.model in self._jumpstart_mapping + if self._has_jumpstart_equivalent: + huggingface_model_id = self.model + jumpstart_model_id = self._jumpstart_mapping[huggingface_model_id]["jumpstart-model-id"] + self.model = jumpstart_model_id + merged_date = self._jumpstart_mapping[huggingface_model_id].get("merged-at") + self._build_for_jumpstart() + compare_model_diff_message = ( + "If you want to identify the differences between the two, " + "please use model_uris.retrieve() to retrieve the model " + "artifact S3 URI and compare them." + ) + logger.warning( # pylint: disable=logging-fstring-interpolation + "Please note that for this model we are using the JumpStart's" + f'local copy "{jumpstart_model_id}" ' + f'of the HuggingFace model "{huggingface_model_id}" you chose. ' + "We strive to keep our local copy synced with the HF model hub closely. " + "This model was synced " + f"{f'on {merged_date}' if merged_date else 'before 11/04/2024'}. " + f"{compare_model_diff_message if not self._is_gated_model() else ''}" + ) + return True + return False + + def _retrieve_hugging_face_model_mapping(self): + """Retrieve the HuggingFace/JumpStart model mapping and preprocess it.""" + converted_mapping = {} + region = self.sagemaker_session.boto_region_name + try: + mapping_json_object = JumpStartS3PayloadAccessor.get_object_cached( + bucket=get_jumpstart_content_bucket(region), + key="hf_model_id_map_cache.json", + region=region, + s3_client=self.sagemaker_session.s3_client, + ) + mapping = json.loads(mapping_json_object) + except Exception: # pylint: disable=broad-except + return converted_mapping + + for k, v in mapping.items(): + converted_mapping[v["hf-model-id"]] = { + "jumpstart-model-id": k, + "jumpstart-model-version": v["jumpstart-model-version"], + "merged-at": v.get("merged-at"), + "hf-model-repo-sha": v.get("hf-model-repo-sha"), + } + return converted_mapping diff --git a/tests/unit/sagemaker/serve/builder/test_model_builder.py b/tests/unit/sagemaker/serve/builder/test_model_builder.py index 4e34c5f864..91d804433c 100644 --- a/tests/unit/sagemaker/serve/builder/test_model_builder.py +++ b/tests/unit/sagemaker/serve/builder/test_model_builder.py @@ -18,7 +18,19 @@ from pathlib import Path from copy import deepcopy +import deepdiff +import pytest +from sagemaker.enums import EndpointType + +from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig +from sagemaker.batch_inference.batch_transform_inference_config import BatchTransformInferenceConfig + +from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements + +from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig + from sagemaker.model import Model + from sagemaker.serve import SchemaBuilder from sagemaker.serve.builder.model_builder import ModelBuilder from sagemaker.serve.mode.function_pointers import Mode @@ -66,20 +78,17 @@ mock_session = MagicMock() +RESOURCE_REQUIREMENTS = ResourceRequirements( + requests={ + "num_cpus": 0.5, + "memory": 512, + "copies": 2, + }, + limits={}, +) -class TestModelBuilder(unittest.TestCase): - @patch("sagemaker.serve.builder.model_builder._ServeSettings") - def test_validation_in_progress_mode_supported(self, mock_serveSettings): - builder = ModelBuilder(model_server=ModelServer.TORCHSERVE) - self.assertRaisesRegex( - Exception, - "IN_PROCESS mode is only supported for MMS/Transformers server in beta release.", - builder.build, - Mode.IN_PROCESS, - mock_role_arn, - mock_session, - ) +class TestModelBuilder(unittest.TestCase): @patch("sagemaker.serve.builder.model_builder._ServeSettings") def test_validation_cannot_set_both_model_and_inference_spec(self, mock_serveSettings): builder = ModelBuilder(inference_spec="some value", model=Mock(spec=object)) @@ -2425,7 +2434,7 @@ def test_optimize( self.assertEqual(builder.env_vars["HUGGING_FACE_HUB_TOKEN"], "token") self.assertEqual(builder.model_server, ModelServer.DJL_SERVING) - mock_send_telemetry.assert_called_once() + assert mock_send_telemetry.call_count == 2 mock_sagemaker_session.sagemaker_client.create_optimization_job.assert_called_once_with( OptimizationJobName="my-optimization-job", DeploymentInstanceType="ml.g5.24xlarge", @@ -2881,6 +2890,366 @@ def test_optimize_for_hf_without_custom_s3_path( }, ) + def test_deploy_invalid_inputs(self): + model_builder = ModelBuilder( + model="meta-llama/Meta-Llama-3-8B-Instruct", + env_vars={"HUGGING_FACE_HUB_TOKEN": "token"}, + role_arn="role-arn", + instance_type="ml.g5.2xlarge", + ) + inputs = {"endpoint_name": "endpoint-001"} + + try: + model_builder.deploy(**inputs) + except ValueError as e: + assert "Model Needs to be built before deploying" in str(e) + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + def test_display_benchmark_metrics_non_string_model(self, mock_is_jumpstart): + """Test that ValueError is raised when model is not a string""" + builder = ModelBuilder(model=Mock()) # Non-string model + + self.assertRaisesRegex( + ValueError, + "Benchmarking is only supported for JumpStart or HuggingFace models", + builder.display_benchmark_metrics, + ) + mock_is_jumpstart.assert_not_called() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.jumpstart_builder.JumpStart.display_benchmark_metrics") + def test_display_benchmark_metrics_jumpstart_model( + self, mock_display_benchmark_metrics, mock_is_jumpstart + ): + """Test successful execution for jumpstart model""" + mock_is_jumpstart.return_value = True + + builder = ModelBuilder(model="jumpstart-model-id") + builder.display_benchmark_metrics() + + mock_is_jumpstart.assert_called_once() + mock_display_benchmark_metrics.assert_called_once() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._use_jumpstart_equivalent") + @patch("sagemaker.serve.builder.jumpstart_builder.JumpStart.display_benchmark_metrics") + def test_display_benchmark_metrics_with_jumpstart_equivalent( + self, mock_display_benchmark_metrics, mock_has_equivalent, mock_is_jumpstart + ): + """Test successful execution for model with jumpstart equivalent""" + mock_is_jumpstart.return_value = False + mock_has_equivalent.return_value = True + + builder = ModelBuilder(model="hf-model-id") + builder.display_benchmark_metrics() + + mock_is_jumpstart.assert_called_once() + mock_has_equivalent.assert_called_once() + mock_display_benchmark_metrics.assert_called_once() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._use_jumpstart_equivalent") + def test_display_benchmark_metrics_unsupported_model( + self, mock_has_equivalent, mock_is_jumpstart + ): + """Test that ValueError is raised for unsupported models""" + mock_is_jumpstart.return_value = False + mock_has_equivalent.return_value = False + + builder = ModelBuilder(model="huggingface-model-id") + + self.assertRaisesRegex( + ValueError, + "This model does not have benchmark metrics yet", + builder.display_benchmark_metrics, + ) + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + def test_get_deployment_config_non_string_model(self, mock_is_jumpstart): + """Test that ValueError is raised when model is not a string""" + builder = ModelBuilder(model=Mock()) # Non-string model + + self.assertRaisesRegex( + ValueError, + "Deployment config is only supported for JumpStart or HuggingFace models", + builder.get_deployment_config, + ) + mock_is_jumpstart.assert_not_called() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.jumpstart_builder.JumpStart.get_deployment_config") + def test_get_deployment_config_jumpstart_model( + self, mock_get_deployment_config, mock_is_jumpstart + ): + """Test successful execution for jumpstart model""" + mock_is_jumpstart.return_value = True + + builder = ModelBuilder(model="jumpstart-model-id") + builder.get_deployment_config() + + mock_is_jumpstart.assert_called_once() + mock_get_deployment_config.assert_called_once() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._use_jumpstart_equivalent") + @patch("sagemaker.serve.builder.jumpstart_builder.JumpStart.get_deployment_config") + def test_get_deployment_config_with_jumpstart_equivalent( + self, mock_get_deployment_config, mock_has_equivalent, mock_is_jumpstart + ): + """Test successful execution for model with jumpstart equivalent""" + mock_is_jumpstart.return_value = False + mock_has_equivalent.return_value = True + + builder = ModelBuilder(model="hf-model-id") + builder.get_deployment_config() + + mock_is_jumpstart.assert_called_once() + mock_has_equivalent.assert_called_once() + mock_get_deployment_config.assert_called_once() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._use_jumpstart_equivalent") + def test_get_deployment_config_unsupported_model(self, mock_has_equivalent, mock_is_jumpstart): + """Test that ValueError is raised for unsupported models""" + mock_is_jumpstart.return_value = False + mock_has_equivalent.return_value = False + + builder = ModelBuilder(model="huggingface-model-id") + + self.assertRaisesRegex( + ValueError, + "This model does not have any deployment config yet", + builder.get_deployment_config, + ) + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + def test_list_deployment_configs_non_string_model(self, mock_is_jumpstart): + """Test that ValueError is raised when model is not a string""" + builder = ModelBuilder(model=Mock()) # Non-string model + + self.assertRaisesRegex( + ValueError, + "Deployment config is only supported for JumpStart or HuggingFace models", + builder.list_deployment_configs, + ) + mock_is_jumpstart.assert_not_called() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.jumpstart_builder.JumpStart.list_deployment_configs") + def test_list_deployment_configs_jumpstart_model( + self, mock_list_deployment_configs, mock_is_jumpstart + ): + """Test successful execution for jumpstart model""" + mock_is_jumpstart.return_value = True + + builder = ModelBuilder(model="jumpstart-model-id") + builder.list_deployment_configs() + + mock_is_jumpstart.assert_called_once() + mock_list_deployment_configs.assert_called_once() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._use_jumpstart_equivalent") + @patch("sagemaker.serve.builder.jumpstart_builder.JumpStart.list_deployment_configs") + def test_list_deployment_configs_with_jumpstart_equivalent( + self, mock_list_deployment_configs, mock_has_equivalent, mock_is_jumpstart + ): + """Test successful execution for model with jumpstart equivalent""" + mock_is_jumpstart.return_value = False + mock_has_equivalent.return_value = True + + builder = ModelBuilder(model="hf-model-id") + builder.list_deployment_configs() + + mock_is_jumpstart.assert_called_once() + mock_has_equivalent.assert_called_once() + mock_list_deployment_configs.assert_called_once() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._use_jumpstart_equivalent") + def test_list_deployment_configs_unsupported_model( + self, mock_has_equivalent, mock_is_jumpstart + ): + """Test that ValueError is raised for unsupported models""" + mock_is_jumpstart.return_value = False + mock_has_equivalent.return_value = False + + builder = ModelBuilder(model="huggingface-model-id") + + self.assertRaisesRegex( + ValueError, + "This model does not have any deployment config yet", + builder.list_deployment_configs, + ) + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + def test_set_deployment_config_non_string_model(self, mock_is_jumpstart): + """Test that ValueError is raised when model is not a string""" + builder = ModelBuilder(model=Mock()) # Non-string model + instance_type = "ml.g5.xlarge" + config_name = "config-name" + self.assertRaisesRegex( + ValueError, + "Deployment config is only supported for JumpStart or HuggingFace models", + builder.set_deployment_config, + config_name, + instance_type, + ) + mock_is_jumpstart.assert_not_called() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.jumpstart_builder.JumpStart.set_deployment_config") + def test_set_deployment_config_jumpstart_model( + self, mock_set_deployment_config, mock_is_jumpstart + ): + """Test successful execution for jumpstart model""" + mock_is_jumpstart.return_value = True + instance_type = "ml.g5.xlarge" + config_name = "config-name" + + builder = ModelBuilder(model="jumpstart-model-id") + builder.set_deployment_config(config_name, instance_type) + + mock_is_jumpstart.assert_called_once() + mock_set_deployment_config.assert_called_once() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._use_jumpstart_equivalent") + @patch("sagemaker.serve.builder.jumpstart_builder.JumpStart.set_deployment_config") + def test_set_deployment_config_with_jumpstart_equivalent( + self, mock_set_deployment_config, mock_has_equivalent, mock_is_jumpstart + ): + """Test successful execution for model with jumpstart equivalent""" + mock_is_jumpstart.return_value = False + mock_has_equivalent.return_value = True + instance_type = "ml.g5.xlarge" + config_name = "config-name" + + builder = ModelBuilder(model="hf-model-id") + builder.set_deployment_config(config_name, instance_type) + + mock_is_jumpstart.assert_called_once() + mock_has_equivalent.assert_called_once() + mock_set_deployment_config.assert_called_once() + + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_jumpstart_model_id") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._use_jumpstart_equivalent") + def test_set_deployment_config_unsupported_model(self, mock_has_equivalent, mock_is_jumpstart): + """Test that ValueError is raised for unsupported models""" + mock_is_jumpstart.return_value = False + mock_has_equivalent.return_value = False + instance_type = "ml.g5.xlarge" + config_name = "config-name" + + builder = ModelBuilder(model="huggingface-model-id") + + self.assertRaisesRegex( + ValueError, + f"The deployment config {config_name} cannot be set on this model", + builder.set_deployment_config, + config_name, + instance_type, + ) + + @patch( + "sagemaker.serve.builder.model_builder.ModelBuilder._retrieve_hugging_face_model_mapping" + ) + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_gated_model") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_jumpstart") + def test_use_jumpstart_equivalent_return_true( + self, mock_build_for_jumpstart, mock_is_gated_model, mock_retrieve_mapping + ): + """Test that _use_jumpstart_equivalent returns True when equivalent exists""" + mock_retrieve_mapping.return_value = { + "HuggingFaceH4/zephyr-7b-beta": { + "jumpstart-model-id": "js-model", + "jumpstart-model-version": "1.0.0", + "hf-model-repo-sha": None, + } + } + mock_is_gated_model.return_value = False + + builder = ModelBuilder(model="HuggingFaceH4/zephyr-7b-beta") + + self.assertTrue(builder._use_jumpstart_equivalent()) + + @patch( + "sagemaker.serve.builder.model_builder.ModelBuilder._retrieve_hugging_face_model_mapping" + ) + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._is_gated_model") + @patch("sagemaker.serve.builder.model_builder.ModelBuilder._build_for_jumpstart") + def test_use_jumpstart_equivalent_return_true_with_schema_builder( + self, mock_build_for_jumpstart, mock_is_gated_model, mock_retrieve_mapping + ): + """Test that _use_jumpstart_equivalent returns True when equivalent exists""" + mock_retrieve_mapping.return_value = { + "HuggingFaceH4/zephyr-7b-beta": { + "jumpstart-model-id": "js-model", + "jumpstart-model-version": "1.0.0", + "hf-model-repo-sha": None, + } + } + mock_is_gated_model.return_value = False + + builder = ModelBuilder(model="HuggingFaceH4/zephyr-7b-beta", sagemaker_session=mock_session) + + self.assertTrue(builder._use_jumpstart_equivalent()) + self.assertIsNotNone(builder.schema_builder) + inputs, outputs = task.retrieve_local_schemas("text-generation") + self.assertEqual(builder.schema_builder.sample_input["inputs"], inputs["inputs"]) + self.assertEqual(builder.schema_builder.sample_output, outputs) + + @patch( + "sagemaker.serve.builder.model_builder.ModelBuilder._retrieve_hugging_face_model_mapping" + ) + def test_use_jumpstart_equivalent_return_false(self, mock_retrieve_mapping): + """Test that _use_jumpstart_equivalent returns false when equivalent doesn't exist""" + mock_retrieve_mapping.return_value = { + "hf-model-id": { + "jumpstart-model-id": "js-model", + "jumpstart-model-version": "1.0.0", + "hf-model-repo-sha": None, + } + } + + builder = ModelBuilder(model="model-id") + + self.assertFalse(builder._use_jumpstart_equivalent()) + + def test_use_jumpstart_equivalent_return_false_with_env_vars(self): + """Test that _use_jumpstart_equivalent returns false when env_vars is provided""" + builder = ModelBuilder(model="model-id", env_vars={"mock-key": "mock-value"}) + + self.assertFalse(builder._use_jumpstart_equivalent()) + + def test_use_jumpstart_equivalent_return_false_with_image_uri(self): + """Test that _use_jumpstart_equivalent returns false when image_uri is provided""" + builder = ModelBuilder(model="model-id", image_uri="mock-uri") + + self.assertFalse(builder._use_jumpstart_equivalent()) + + @patch("sagemaker.serve.builder.model_builder.JumpStartS3PayloadAccessor") + @patch("sagemaker.serve.builder.model_builder.get_jumpstart_content_bucket") + def test_retrieve_hugging_face_model_mapping(self, mock_content_bucket, mock_payload_accessor): + """Test that _retrieve_hugging_face_model_mapping returns the correct mapping""" + mock_get_object = Mock() + mock_get_object.return_value = ( + '{"js-model-id": {"hf-model-id": "hf-model", "jumpstart-model-version": "1.0.0"}}' + ) + mock_payload_accessor.get_object_cached = mock_get_object + expected_mapping = { + "hf-model": { + "jumpstart-model-id": "js-model-id", + "jumpstart-model-version": "1.0.0", + "hf-model-repo-sha": None, + "merged-at": None, + } + } + + builder = ModelBuilder(model="hf-model", sagemaker_session=mock_session) + + self.assertEqual(builder._retrieve_hugging_face_model_mapping(), expected_mapping) + @patch.object(ModelBuilder, "_prepare_for_mode") @patch.object(ModelBuilder, "_get_serve_setting", autospec=True) def test_optimize_with_gpu_instance_and_llama_3_1_and_compilation( @@ -3076,7 +3445,7 @@ def test_optimize_sharding_with_env_vars( self.assertEqual(builder.env_vars["HUGGING_FACE_HUB_TOKEN"], "token") self.assertEqual(builder.model_server, ModelServer.DJL_SERVING) - mock_send_telemetry.assert_called_once() + assert mock_send_telemetry.call_count == 2 mock_sagemaker_session.sagemaker_client.create_optimization_job.assert_called_once_with( OptimizationJobName="my-optimization-job", DeploymentInstanceType="ml.g5.24xlarge", @@ -3184,7 +3553,7 @@ def test_optimize_sharding_with_override_and_env_var( self.assertEqual(builder.env_vars["HUGGING_FACE_HUB_TOKEN"], "token") self.assertEqual(builder.model_server, ModelServer.DJL_SERVING) - mock_send_telemetry.assert_called_once() + assert mock_send_telemetry.call_count == 2 mock_sagemaker_session.sagemaker_client.create_optimization_job.assert_called_once_with( OptimizationJobName="my-optimization-job", DeploymentInstanceType="ml.g5.24xlarge", @@ -3298,7 +3667,7 @@ def test_optimize_sharding_with_override( self.assertEqual(builder.env_vars["HUGGING_FACE_HUB_TOKEN"], "token") self.assertEqual(builder.model_server, ModelServer.DJL_SERVING) - mock_send_telemetry.assert_called_once() + assert mock_send_telemetry.call_count == 2 mock_sagemaker_session.sagemaker_client.create_optimization_job.assert_called_once_with( OptimizationJobName="my-optimization-job", DeploymentInstanceType="ml.g5.24xlarge", @@ -3426,7 +3795,7 @@ def test_optimize_sharding_with_override_for_js( self.assertEqual(builder.env_vars["HUGGING_FACE_HUB_TOKEN"], "token") - mock_send_telemetry.assert_called_once() + assert mock_send_telemetry.call_count == 2 mock_sagemaker_session.sagemaker_client.create_optimization_job.assert_called_once_with( OptimizationJobName="my-optimization-job", ModelSource={"S3": {"S3Uri": ANY}}, @@ -3663,3 +4032,86 @@ def test_neuron_configurations_rule_set(self): speculative_decoding_config=None, compilation_config={}, ) + + +@pytest.mark.parametrize( + "test_case", + [ + { + "input_args": {"endpoint_name": "test"}, + "call_params": { + "instance_type": "ml.g5.2xlarge", + "initial_instance_count": 1, + "endpoint_name": "test", + }, + }, + { + "input_args": { + "endpoint_name": "test", + "inference_config": ServerlessInferenceConfig(), + }, + "call_params": { + "serverless_inference_config": ServerlessInferenceConfig(), + "endpoint_name": "test", + }, + }, + { + "input_args": { + "endpoint_name": "test", + "inference_config": AsyncInferenceConfig(output_path="op-path"), + }, + "call_params": { + "async_inference_config": AsyncInferenceConfig(output_path="op-path"), + "instance_type": "ml.g5.2xlarge", + "initial_instance_count": 1, + "endpoint_name": "test", + }, + }, + { + "input_args": {"endpoint_name": "test", "inference_config": RESOURCE_REQUIREMENTS}, + "call_params": { + "resources": RESOURCE_REQUIREMENTS, + "role": "role-arn", + "initial_instance_count": 1, + "instance_type": "ml.g5.2xlarge", + "mode": Mode.SAGEMAKER_ENDPOINT, + "endpoint_type": EndpointType.INFERENCE_COMPONENT_BASED, + }, + }, + { + "input_args": { + "inference_config": BatchTransformInferenceConfig( + instance_count=1, instance_type="ml.m5.large", output_path="op-path" + ) + }, + "call_params": { + "instance_count": 1, + "instance_type": "ml.m5.large", + "output_path": "op-path", + }, + "id": "Batch", + }, + ], + ids=["Real Time", "Serverless", "Async", "Multi-Model", "Batch"], +) +@patch("sagemaker.serve.builder.model_builder.unique_name_from_base") +def test_deploy(mock_unique_name_from_base, test_case): + mock_unique_name_from_base.return_value = "test" + model: Model = MagicMock() + model_builder = ModelBuilder( + model="meta-llama/Meta-Llama-3-8B-Instruct", + env_vars={"HUGGING_FACE_HUB_TOKEN": "token"}, + role_arn="role-arn", + instance_type="ml.g5.2xlarge", + ) + setattr(model_builder, "built_model", model) + + model_builder.deploy(**test_case["input_args"]) + + if "id" in test_case and test_case["id"] == "Batch": + args, kwargs = model.transformer.call_args_list[0] + else: + args, kwargs = model.deploy.call_args_list[0] + + diff = deepdiff.DeepDiff(kwargs, test_case["call_params"]) + assert diff == {} \ No newline at end of file From 1ad75c9f5c10b36bc540c8b2cda5286eb8b35ecc Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Sun, 10 Nov 2024 18:23:10 -0800 Subject: [PATCH 20/79] Simplify Config Class Names and DistributedRunner structures (#1573) --- .gitignore | 4 +- src/sagemaker/modules/__init__.py | 4 + src/sagemaker/modules/configs.py | 126 ++-------- src/sagemaker/modules/constants.py | 4 +- src/sagemaker/modules/distributed.py | 126 ++++++++++ src/sagemaker/modules/templates.py | 16 +- .../base_model_trainer.ipynb | 63 +++-- .../train/container_drivers/mpi_driver.py | 19 +- .../train/container_drivers/mpi_utils.py | 36 +-- .../{pytorch_driver.py => torchrun_driver.py} | 14 +- .../modules/train/container_drivers/utils.py | 33 ++- src/sagemaker/modules/train/model_trainer.py | 229 ++++++++---------- .../data/modules/script_mode/custom_script.py | 146 +++++++++++ .../modules/script_mode/data/test/x_test.npy | Bin 0 -> 19456 bytes .../modules/script_mode/data/test/y_test.npy | Bin 0 -> 2544 bytes .../script_mode/data/train/x_train.npy | Bin 0 -> 77184 bytes .../script_mode/data/train/y_train.npy | Bin 0 -> 9760 bytes .../modules/script_mode/pytorch_model_def.py | 23 ++ .../data/modules/script_mode/requirements.txt | 3 + .../container_drivers/test_mpi_driver.py | 41 ++-- ...orch_driver.py => test_torchrun_driver.py} | 77 +++--- .../modules/train/test_model_trainer.py | 142 +++++++++-- 22 files changed, 693 insertions(+), 413 deletions(-) create mode 100644 src/sagemaker/modules/distributed.py rename src/sagemaker/modules/train/container_drivers/{pytorch_driver.py => torchrun_driver.py} (91%) create mode 100644 tests/data/modules/script_mode/custom_script.py create mode 100644 tests/data/modules/script_mode/data/test/x_test.npy create mode 100644 tests/data/modules/script_mode/data/test/y_test.npy create mode 100644 tests/data/modules/script_mode/data/train/x_train.npy create mode 100644 tests/data/modules/script_mode/data/train/y_train.npy create mode 100644 tests/data/modules/script_mode/pytorch_model_def.py create mode 100644 tests/data/modules/script_mode/requirements.txt rename tests/unit/sagemaker/modules/train/container_drivers/{test_pytorch_driver.py => test_torchrun_driver.py} (52%) diff --git a/.gitignore b/.gitignore index b87341ca4d..d966fa1ccc 100644 --- a/.gitignore +++ b/.gitignore @@ -33,8 +33,8 @@ env/ *.html **/_repack_script_launcher.sh src/sagemaker/modules/train/container_drivers/sm_train.sh -src/sagemaker/modules/train/container_drivers/sourcecodeconfig.json -src/sagemaker/modules/train/container_drivers/distribution.json +src/sagemaker/modules/train/container_drivers/sourcecode.json +src/sagemaker/modules/train/container_drivers/distributed_runner.json tests/data/**/_repack_model.py tests/data/experiment/sagemaker-dev-1.0.tar.gz src/sagemaker/serve/tmp_workspace \ No newline at end of file diff --git a/src/sagemaker/modules/__init__.py b/src/sagemaker/modules/__init__.py index 67758a7af1..9a25d10e4e 100644 --- a/src/sagemaker/modules/__init__.py +++ b/src/sagemaker/modules/__init__.py @@ -16,3 +16,7 @@ from sagemaker_core.main.utils import logger as sagemaker_core_logger logger = sagemaker_core_logger + +from sagemaker.modules.train.model_trainer import ( # noqa: F401 E402 # pylint: disable=C0413 + ModelTrainer, +) diff --git a/src/sagemaker/modules/configs.py b/src/sagemaker/modules/configs.py index eee064c0cf..75f06797bd 100644 --- a/src/sagemaker/modules/configs.py +++ b/src/sagemaker/modules/configs.py @@ -21,7 +21,7 @@ from __future__ import absolute_import -from typing import Optional, Union, Dict, Any, List +from typing import Optional, Union from pydantic import BaseModel, model_validator import sagemaker_core.shapes as shapes @@ -54,15 +54,10 @@ CheckpointConfig, ) -from sagemaker.modules import logger from sagemaker.modules.utils import convert_unassigned_to_none __all__ = [ - "SourceCodeConfig", - "TorchDistributionConfig", - "MPIDistributionConfig", - "SMDistributedSettings", - "DistributionConfig", + "SourceCode", "StoppingCondition", "RetryStrategy", "OutputDataConfig", @@ -87,107 +82,16 @@ "InstanceGroup", "TensorBoardOutputConfig", "CheckpointConfig", - "ComputeConfig", - "NetworkingConfig", + "Compute", + "Networking", "InputData", ] -class SMDistributedSettings(BaseModel): - """SMDistributedSettings. +class SourceCode(BaseModel): + """SourceCode. - The SMDistributedSettings is used to configure distributed training when - using the smdistributed library. - - Attributes: - enable_dataparallel (Optional[bool]): - Whether to enable data parallelism. - enable_modelparallel (Optional[bool]): - Whether to enable model parallelism. - modelparallel_parameters (Optional[Dict[str, Any]]): - The parameters for model parallelism. - """ - - enable_dataparallel: Optional[bool] = False - enable_modelparallel: Optional[bool] = False - modelparallel_parameters: Optional[Dict[str, Any]] = None - - -class DistributionConfig(BaseModel): - """Base class for distribution configurations.""" - - _distribution_type: str - - -class TorchDistributionConfig(DistributionConfig): - """TorchDistributionConfig. - - The TorchDistributionConfig uses `torchrun` or `torch.distributed.launch` in the backend to - launch distributed training. - - SMDistributed Library Information: - - `TorchDistributionConfig` can be used for SMModelParallel V2. - - For SMDataParallel or SMModelParallel V1, it is recommended to use the - `MPIDistributionConfig.` - - - Attributes: - smdistributed_settings (Optional[SMDistributedSettings]): - The settings for smdistributed library. - process_count_per_node (int): - The number of processes to run on each node in the training job. - Will default to the number of CPUs or GPUs available in the container. - """ - - _distribution_type: str = "torch_distributed" - - smdistributed_settings: Optional[SMDistributedSettings] = None - process_count_per_node: Optional[int] = None - - @model_validator(mode="after") - def _validate_model(cls, model): # pylint: disable=E0213 - """Validate the model.""" - if ( - getattr(model, "smddistributed_settings", None) - and model.smddistributed_settings.enable_dataparallel - ): - logger.warning( - "For smdistributed data parallelism, it is recommended to use " - + "MPIDistributionConfig." - ) - return model - - -class MPIDistributionConfig(DistributionConfig): - """MPIDistributionConfig. - - The MPIDistributionConfig uses `mpirun` in the backend to launch distributed training. - - SMDistributed Library Information: - - `MPIDistributionConfig` can be used for SMDataParallel and SMModelParallel V1. - - For SMModelParallel V2, it is recommended to use the `TorchDistributionConfig`. - - Attributes: - smdistributed_settings (Optional[SMDistributedSettings]): - The settings for smdistributed library. - process_count_per_node (int): - The number of processes to run on each node in the training job. - Will default to the number of CPUs or GPUs available in the container. - mpi_additional_options (Optional[str]): - The custom MPI options to use for the training job. - """ - - _distribution_type: str = "mpi" - - smdistributed_settings: Optional[SMDistributedSettings] = None - process_count_per_node: Optional[int] = None - mpi_additional_options: Optional[List[str]] = None - - -class SourceCodeConfig(BaseModel): - """SourceCodeConfig. - - This config allows the user to specify the source code location, dependencies, + The SourceCode class allows the user to specify the source code location, dependencies, entry script, or commands to be executed in the training job container. Attributes: @@ -210,10 +114,10 @@ class SourceCodeConfig(BaseModel): command: Optional[str] = None -class ComputeConfig(shapes.ResourceConfig): - """ComputeConfig. +class Compute(shapes.ResourceConfig): + """Compute. - The ComputeConfig is a subclass of `sagemaker_core.shapes.ResourceConfig` + The Compute class is a subclass of `sagemaker_core.shapes.ResourceConfig` and allows the user to specify the compute resources for the training job. Attributes: @@ -245,7 +149,7 @@ class ComputeConfig(shapes.ResourceConfig): enable_managed_spot_training: Optional[bool] = None @model_validator(mode="after") - def _model_validator(self) -> "ComputeConfig": + def _model_validator(self) -> "Compute": """Convert Unassigned values to None.""" return convert_unassigned_to_none(self) @@ -259,10 +163,10 @@ def _to_resource_config(self) -> shapes.ResourceConfig: return shapes.ResourceConfig(**filtered_dict) -class NetworkingConfig(shapes.VpcConfig): - """NetworkingConfig. +class Networking(shapes.VpcConfig): + """Networking. - The NetworkingConifg is a subclass of `sagemaker_core.shapes.VpcConfig ` and + The Networking class is a subclass of `sagemaker_core.shapes.VpcConfig ` and allows the user to specify the networking configuration for the training job. Attributes: @@ -290,7 +194,7 @@ class NetworkingConfig(shapes.VpcConfig): enable_inter_container_traffic_encryption: Optional[bool] = None @model_validator(mode="after") - def _model_validator(self) -> "NetworkingConfig": + def _model_validator(self) -> "Networking": """Convert Unassigned values to None.""" return convert_unassigned_to_none(self) diff --git a/src/sagemaker/modules/constants.py b/src/sagemaker/modules/constants.py index 97367b2a61..271c74313f 100644 --- a/src/sagemaker/modules/constants.py +++ b/src/sagemaker/modules/constants.py @@ -25,8 +25,8 @@ os.path.dirname(os.path.abspath(__file__)), "train/container_drivers" ) -SOURCE_CODE_CONFIG_JSON = "sourcecodeconfig.json" -DISTRIBUTION_JSON = "distribution.json" +SOURCE_CODE_JSON = "sourcecode.json" +DISTRIBUTED_RUNNER_JSON = "distributed_runner.json" TRAIN_SCRIPT = "sm_train.sh" DEFAULT_CONTAINER_ENTRYPOINT = ["/bin/bash"] diff --git a/src/sagemaker/modules/distributed.py b/src/sagemaker/modules/distributed.py new file mode 100644 index 0000000000..b6d6967833 --- /dev/null +++ b/src/sagemaker/modules/distributed.py @@ -0,0 +1,126 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Distributed module.""" +from __future__ import absolute_import + +from typing import Optional, Dict, Any, List +from pydantic import BaseModel, PrivateAttr + + +class DistributedRunner(BaseModel): + """Base class for DistributedRunner Class""" + + _type: str = PrivateAttr() + + def model_dump(self, *args, **kwargs): + """Dump the model to a dictionary.""" + result = super().model_dump(*args, **kwargs) + result["_type"] = self._type + return result + + +class Torchrun(DistributedRunner): + """TorchDistribution. + + The TorchDistribution runner uses `torchrun` or `torch.distributed.launch` in the backend to + launch distributed training. + + Attributes: + process_count_per_node (int): + The number of processes to run on each node in the training job. + Will default to the number of GPUs available in the container. + """ + + _type: str = PrivateAttr(default="torchrun") + + process_count_per_node: Optional[int] = None + + +class TorchrunSMP(DistributedRunner): + """TorchrunSMP. + + The TorchrunSMP runner uses `torchrun` or `torch.distributed.launch` in the backend + to launch distributed training. This strategy is used for a PyTorch job using the SageMaker + Model Parallelism library v2. For more information on the model parallelism parameters, see: + https://docs.aws.amazon.com/sagemaker/latest/dg/distributed-model-parallel-v2-reference.html#distributed-model-parallel-v2-reference-init-config + + Attributes: + process_count_per_node (int): + The number of processes to run on each node in the training job. + Will default to the number of GPUs available in the container. + hybrid_shard_degree (Optional[int]): + Specifies a sharded parallelism degree for the model. + sm_activation_offloading (Optional[bool]): + Specifies whether to enable the SMP activation offloading implementation. + activation_loading_horizon (Optional[int]): + An integer specifying the activation offloading horizon type for FSDP. This is the + maximum number of checkpointed or offloaded layers whose inputs can be in the GPU + memory simultaneously. + fsdp_cache_flush_warnings (Optional[bool]): + Detects and warns if cache flushes happen in the PyTorch memory manager, because they + can degrade computational performance. + allow_empty_shards (Optional[bool]): + Whether to allow empty shards when sharding tensors if tensor is not divisible. This is + an experimental fix for crash during checkpointing in certain scenarios. Disabling this + falls back to the original PyTorch behavior. + tensor_parallel_degree (Optional[int]): + Specifies a tensor parallelism degree. The value must be between 1 and world_size. + context_parallel_degree (Optional[int]): + Specifies the context parallelism degree. The value must be between 1 and world_size , + and must be <= hybrid_shard_degree. + expert_parallel_degree (Optional[int]): + Specifies a expert parallelism degree. The value must be between 1 and world_size. + random_seed (Optional[int]): + A seed number for the random operations in distributed modules by SMP tensor + parallelism or expert parallelism. + """ + + _type: str = PrivateAttr(default="torchrun") + + process_count_per_node: Optional[int] = None + hybrid_shard_degree: Optional[int] = None + sm_activation_offloading: Optional[bool] = None + activation_loading_horizon: Optional[int] = None + fsdp_cache_flush_warnings: Optional[bool] = None + allow_empty_shards: Optional[bool] = None + tensor_parallel_degree: Optional[int] = None + context_parallel_degree: Optional[int] = None + expert_parallel_degree: Optional[int] = None + random_seed: Optional[int] = None + + def _to_mp_parameters_dict(self) -> Dict[str, Any]: + """Convert to a dictionary of MP parameters.""" + mp_parameters = self.model_dump(exclude_none=True) + mp_parameters.pop("_type") + if mp_parameters.get("process_count_per_node") is not None: + mp_parameters.pop("process_count_per_node") + return mp_parameters + + +class MPI(DistributedRunner): + """MPI. + + The MPI runner uses `mpirun` in the backend to launch distributed training. + + Attributes: + process_count_per_node (int): + The number of processes to run on each node in the training job. + Will default to the number of GPUs available in the container. + mpi_additional_options (Optional[str]): + The custom MPI options to use for the training job. + """ + + _type: str = PrivateAttr(default="mpi") + + process_count_per_node: Optional[int] = None + mpi_additional_options: Optional[List[str]] = None diff --git a/src/sagemaker/modules/templates.py b/src/sagemaker/modules/templates.py index 491b0cb2b6..1c2e65ff8c 100644 --- a/src/sagemaker/modules/templates.py +++ b/src/sagemaker/modules/templates.py @@ -19,13 +19,13 @@ eval $CMD """ -EXECUTE_PYTORCH_DRIVER = """ -echo "Running PyTorch training driver" -$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/pytorch_driver.py +EXEUCTE_TORCHRUN_DRIVER = """ +echo "Running Torchrun driver" +$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/torchrun_driver.py """ EXECUTE_MPI_DRIVER = """ -echo "Running MPI training driver" +echo "Running MPI driver" $SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/mpi_driver.py """ @@ -73,12 +73,12 @@ cat /opt/ml/input/config/inputdataconfig.json echo -echo "/opt/ml/input/data/sm_drivers/sourcecodeconfig.json" -cat /opt/ml/input/data/sm_drivers/sourcecodeconfig.json +echo "/opt/ml/input/data/sm_drivers/sourcecode.json" +cat /opt/ml/input/data/sm_drivers/sourcecode.json echo -echo "/opt/ml/input/data/sm_drivers/distribution.json" -cat /opt/ml/input/data/sm_drivers/distribution.json +echo "/opt/ml/input/data/sm_drivers/distributed_runner.json" +cat /opt/ml/input/data/sm_drivers/distributed_runner.json echo echo "Setting up environment variables" diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index e32b0211d2..c839566dc3 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -27,16 +27,16 @@ "outputs": [], "source": [ "from sagemaker.modules.train import ModelTrainer\n", - "from sagemaker.modules.configs import SourceCodeConfig\n", + "from sagemaker.modules.configs import SourceCode\n", "\n", "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", "\n", - "source_code_config = SourceCodeConfig(\n", + "source_code = SourceCode(\n", " command=\"echo 'Hello World' && env\",\n", ")\n", "model_trainer = ModelTrainer(\n", " training_image=pytorch_image,\n", - " source_code_config=source_code_config,\n", + " source_code=source_code,\n", ")" ] }, @@ -70,11 +70,11 @@ "outputs": [], "source": [ "from sagemaker.modules.train import ModelTrainer\n", - "from sagemaker.modules.configs import SourceCodeConfig\n", + "from sagemaker.modules.configs import SourceCode\n", "\n", "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", "\n", - "source_code_config = SourceCodeConfig(\n", + "source_code = SourceCode(\n", " source_dir=\"basic-script-mode\",\n", " command=\"python custom_script.py\",\n", ")\n", @@ -89,7 +89,7 @@ "\n", "model_trainer = ModelTrainer(\n", " training_image=pytorch_image,\n", - " source_code_config=source_code_config,\n", + " source_code=source_code,\n", " hyperparameters=hyperparameters,\n", " environment=env_vars,\n", ")\n", @@ -117,9 +117,9 @@ "metadata": {}, "outputs": [], "source": [ - "from sagemaker.modules.configs import SourceCodeConfig\n", + "from sagemaker.modules.configs import SourceCode\n", "\n", - "source_code_config = SourceCodeConfig(\n", + "source_code = SourceCode(\n", " source_dir=\"basic-script-mode\",\n", " requirements=\"requirements.txt\",\n", " entry_script=\"custom_script.py\",\n", @@ -127,7 +127,7 @@ "\n", "model_trainer = ModelTrainer(\n", " training_image=pytorch_image,\n", - " source_code_config=source_code_config,\n", + " source_code=source_code,\n", ")" ] }, @@ -296,7 +296,7 @@ "outputs": [], "source": [ "from sagemaker.modules.train import ModelTrainer\n", - "from sagemaker.modules.configs import ComputeConfig, SourceCodeConfig, InputData\n", + "from sagemaker.modules.configs import Compute, SourceCode, InputData\n", "\n", "env = {}\n", "env[\"FI_PROVIDER\"] = \"efa\"\n", @@ -307,10 +307,11 @@ "env[\"FI_EFA_USE_DEVICE_RDMA\"] = \"1\"\n", "env[\"RDMAV_FORK_SAFE\"] = \"1\"\n", "\n", - "compute_config = ComputeConfig(\n", + "compute = Compute(\n", " instance_count=2,\n", " instance_type=\"ml.p4d.24xlarge\",\n", " volume_size_in_gb=96,\n", + " keep_alive_period_in_seconds=3600\n", ")\n", "\n", "hugging_face_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n", @@ -335,7 +336,7 @@ "metadata": {}, "outputs": [], "source": [ - "source_code_config = SourceCodeConfig(\n", + "source_code = SourceCode(\n", " source_dir=\"distributed-training/scripts\",\n", " requirements=\"requirements.txt\",\n", " command=\"torchrun --nnodes 2 \\\n", @@ -348,10 +349,10 @@ "\n", "model_trainer = ModelTrainer(\n", " training_image=hugging_face_image,\n", - " compute_config=compute_config,\n", + " compute=compute,\n", " environment=env,\n", " hyperparameters=hyperparameters,\n", - " source_code_config=source_code_config,\n", + " source_code=source_code,\n", ")" ] }, @@ -365,7 +366,7 @@ " channel_name=\"dataset\",\n", " data_source=training_input_path,\n", ")\n", - "model_trainer.train(input_data_config=[test_data])" + "model_trainer.train(input_data_config=[test_data], wait=False)" ] }, { @@ -383,13 +384,18 @@ "source": [ "from sagemaker.modules.train import ModelTrainer\n", "from sagemaker.modules.configs import (\n", - " ComputeConfig, SourceCodeConfig, TorchDistributionConfig, InputData\n", + " Compute, SourceCode, InputData\n", + ")\n", + "from sagemaker.modules.distributed import (\n", + " Torchrun,\n", + " MPI\n", ")\n", "\n", - "compute_config = ComputeConfig(\n", + "compute = Compute(\n", " instance_count=2,\n", " instance_type=\"ml.p4d.24xlarge\",\n", " volume_size_in_gb=96,\n", + " keep_alive_period_in_seconds=3600\n", ")\n", "\n", "hugging_face_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n", @@ -414,18 +420,31 @@ "metadata": {}, "outputs": [], "source": [ - "source_code_config = SourceCodeConfig(\n", + "source_code = SourceCode(\n", " source_dir=\"distributed-training/scripts\",\n", " requirements=\"requirements.txt\",\n", " entry_script=\"run_clm_no_trainer.py\",\n", ")\n", "\n", + "# Run using Torchrun\n", + "torchrun = Torchrun()\n", + "\n", + "# Run using MPI\n", + "mpi = MPI(\n", + " mpi_additional_options=[\n", + " \"-x\",\n", + " \"MASTER_ADDR=algo-1\",\n", + " \"-x\",\n", + " \"MASTER_PORT=7777\",\n", + " ]\n", + ")\n", + "\n", "model_trainer = ModelTrainer(\n", " training_image=hugging_face_image,\n", - " compute_config=compute_config,\n", + " compute=compute,\n", " hyperparameters=hyperparameters,\n", - " source_code_config=source_code_config,\n", - " distribution_config=TorchDistributionConfig(),\n", + " source_code=source_code,\n", + " distributed_runner=mpi,\n", ")" ] }, @@ -439,7 +458,7 @@ " channel_name=\"dataset\",\n", " data_source=training_input_path,\n", ")\n", - "model_trainer.train(input_data_config=[test_data])" + "model_trainer.train(input_data_config=[test_data], wait=False)" ] }, { diff --git a/src/sagemaker/modules/train/container_drivers/mpi_driver.py b/src/sagemaker/modules/train/container_drivers/mpi_driver.py index 29b047ea89..90c13df8d5 100644 --- a/src/sagemaker/modules/train/container_drivers/mpi_driver.py +++ b/src/sagemaker/modules/train/container_drivers/mpi_driver.py @@ -14,12 +14,13 @@ from __future__ import absolute_import import os +import sys import json from utils import ( logger, - read_source_code_config_json, - read_distribution_json, + read_source_code_json, + read_distributed_runner_json, get_process_count, execute_commands, write_failure_file, @@ -55,9 +56,8 @@ def main(): 5. Exit """ - source_code_config = read_source_code_config_json() - distribution = read_distribution_json() - sm_distributed_settings = distribution.get("smdistributed_settings", {}) + source_code = read_source_code_json() + distribution = read_distributed_runner_json() sm_current_host = os.environ["SM_CURRENT_HOST"] sm_hosts = json.loads(os.environ["SM_HOSTS"]) @@ -83,18 +83,17 @@ def main(): host_count=host_count, host_list=host_list, num_processes=process_count, - smdataparallel_enabled=sm_distributed_settings.get("enable_dataparallel", False), - smmodelparallel_enabled=sm_distributed_settings.get("enable_modelparallel", False), additional_options=distribution.get("mpi_additional_options", []), - entry_script_path=os.path.join(USER_CODE_PATH, source_code_config["entry_script"]), + entry_script_path=os.path.join(USER_CODE_PATH, source_code["entry_script"]), ) logger.info(f"Executing command: {mpi_command}") exit_code, error_traceback = execute_commands(mpi_command) + write_status_file_to_workers(worker_hosts) + if exit_code != 0: write_failure_file(error_traceback) - - write_status_file_to_workers(worker_hosts) + sys.exit(exit_code) if __name__ == "__main__": diff --git a/src/sagemaker/modules/train/container_drivers/mpi_utils.py b/src/sagemaker/modules/train/container_drivers/mpi_utils.py index 2c19986751..0fcfa265a3 100644 --- a/src/sagemaker/modules/train/container_drivers/mpi_utils.py +++ b/src/sagemaker/modules/train/container_drivers/mpi_utils.py @@ -16,7 +16,6 @@ import os import time import subprocess -import json from typing import List @@ -29,7 +28,7 @@ def _write_status_file(host: str, status_file: str) -> bool: """Write the status file to the provided host.""" try: - logger.info(f"Start writing mpirun finished status to {host}") + logger.info("Writing finished status file (%s) to %s", status_file, host) subprocess.run( ["ssh", host, "touch", f"{status_file}"], capture_output=True, @@ -188,8 +187,6 @@ def get_mpirun_command( host_count: int, host_list: List[str], num_processes: int, - smdataparallel_enabled: bool, - smmodelparallel_enabled: bool, additional_options: List[str], entry_script_path: str, ): @@ -258,37 +255,6 @@ def get_mpirun_command( if credential in os.environ: mpirun_command.extend(["-x", credential]) - if smdataparallel_enabled: - if host_count == 1: - smdataparallel_flag = "SMDATAPARALLEL_USE_HOMOGENEOUS=1" - mpirun_command.extend(["-x", smdataparallel_flag]) - else: - smdataparallel_flag = "SMDATAPARALLEL_USE_SINGLENODE=1" - smdataparallel_server_port = 7592 - smdataparallel_server_addr = "algo-1" - - mpirun_command.extend(["-x", smdataparallel_flag]) - mpirun_command.extend( - [ - "-x", - f"SMDATAPARALLEL_SERVER_ADDR={smdataparallel_server_addr}", - "-x", - f"SMDATAPARALLEL_SERVER_PORT={smdataparallel_server_port}", - "-x", - f"SAGEMAKER_INSTANCE_TYPE={instance_type}", - ] - ) - - if validate_smddprun(): - mpirun_command.extend(["smddprun"]) - - if smmodelparallel_enabled: - mp_parameters = json.loads(os.environ.get("SM_HP_MP_PARAMETERS", "{}")) - ddp_dist_backend = mp_parameters.get("ddp_dist_backend", "auto") - if ddp_dist_backend == "auto": - if validate_smddpmprun(): - mpirun_command.extend(["smddpmprun", "-i", instance_type, "--allow-bypass"]) - mpirun_command.extend([get_python_executable()]) mpirun_command.extend(["-m", "mpi4py", entry_script_path]) return mpirun_command diff --git a/src/sagemaker/modules/train/container_drivers/pytorch_driver.py b/src/sagemaker/modules/train/container_drivers/torchrun_driver.py similarity index 91% rename from src/sagemaker/modules/train/container_drivers/pytorch_driver.py rename to src/sagemaker/modules/train/container_drivers/torchrun_driver.py index 049c9ed60a..799f56ca93 100644 --- a/src/sagemaker/modules/train/container_drivers/pytorch_driver.py +++ b/src/sagemaker/modules/train/container_drivers/torchrun_driver.py @@ -10,17 +10,18 @@ # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. -"""This module is the entry point for the PyTorch driver script.""" +"""This module is the entry point for the Torchrun driver script.""" from __future__ import absolute_import import os +import sys from typing import List, Tuple from utils import ( logger, - read_source_code_config_json, - read_distribution_json, + read_source_code_json, + read_distributed_runner_json, get_process_count, get_python_executable, SM_EFA_NCCL_INSTANCES, @@ -62,8 +63,8 @@ def setup_env(): def create_commands(): """Create the Torch Distributed command to execute""" - source_code_config = read_source_code_config_json() - distribution = read_distribution_json() + source_code = read_source_code_json() + distribution = read_distributed_runner_json() process_count = get_process_count(distribution) host_count = int(os.environ["SM_HOST_COUNT"]) @@ -90,7 +91,7 @@ def create_commands(): ] ) - torch_cmd.extend([os.path.join(USER_CODE_PATH, source_code_config["entry_script"])]) + torch_cmd.extend([os.path.join(USER_CODE_PATH, source_code["entry_script"])]) return torch_cmd @@ -113,6 +114,7 @@ def main(): exit_code, traceback = execute_commands(torch_cmd) if exit_code != 0: write_failure_file(traceback) + sys.exit(exit_code) if __name__ == "__main__": diff --git a/src/sagemaker/modules/train/container_drivers/utils.py b/src/sagemaker/modules/train/container_drivers/utils.py index 219fcf4b62..42745b70ff 100644 --- a/src/sagemaker/modules/train/container_drivers/utils.py +++ b/src/sagemaker/modules/train/container_drivers/utils.py @@ -37,8 +37,9 @@ """ USER_CODE_PATH = "/opt/ml/input/data/sm_code" -SOURCE_CODE_CONFIG_JSON = "/opt/ml/input/data/sm_drivers/sourcecodeconfig.json" -DISTRIBUTION_JSON = "/opt/ml/input/data/sm_drivers/distribution.json" +SOURCE_CODE_JSON = "/opt/ml/input/data/sm_drivers/sourcecode.json" +DISTRIBUTED_RUNNER_JSON = "/opt/ml/input/data/sm_drivers/distributed_runner.json" + SM_EFA_NCCL_INSTANCES = [ "ml.g4dn.8xlarge", @@ -65,24 +66,30 @@ def write_failure_file(message: str = DEFAULT_FAILURE_MESSAGE): f.write(message) -def read_source_code_config_json(source_code_config_file: Dict[str, Any] = SOURCE_CODE_CONFIG_JSON): +def read_source_code_json(source_code_json: Dict[str, Any] = SOURCE_CODE_JSON): """Read the source code config json file.""" - with open(source_code_config_file, "r") as f: - source_code_config_json = json.load(f) - return source_code_config_json + try: + with open(source_code_json, "r") as f: + source_code_dict = json.load(f) or {} + except FileNotFoundError: + source_code_dict = {} + return source_code_dict -def read_distribution_json(distribution_file: Dict[str, Any] = DISTRIBUTION_JSON): - """Read the distribution json file.""" - with open(distribution_file, "r") as f: - distribution_json = json.load(f) - return distribution_json +def read_distributed_runner_json(distributed_json: Dict[str, Any] = DISTRIBUTED_RUNNER_JSON): + """Read the distribution config json file.""" + try: + with open(distributed_json, "r") as f: + distributed_runner_dict = json.load(f) or {} + except FileNotFoundError: + distributed_runner_dict = {} + return distributed_runner_dict -def get_process_count(distribution: Dict[str, Any]) -> int: +def get_process_count(distributed_runner_dict: Dict[str, Any]) -> int: """Get the number of processes to run on each node in the training job.""" return ( - int(distribution.get("process_count_per_node", 0)) + int(distributed_runner_dict.get("process_count_per_node", 0)) or int(os.environ.get("SM_NUM_GPUS", 0)) or int(os.environ.get("SM_NUM_NEURONS", 0)) or 1 diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 8d103195aa..7b9abf23c1 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -22,23 +22,19 @@ from sagemaker_core.resources import TrainingJob from sagemaker_core.shapes import AlgorithmSpecification -from sagemaker.fw_utils import validate_mp_config from sagemaker import get_execution_role, Session from sagemaker.modules.configs import ( - ComputeConfig, + Compute, StoppingCondition, RetryStrategy, OutputDataConfig, - SourceCodeConfig, - MPIDistributionConfig, - TorchDistributionConfig, - SMDistributedSettings, + SourceCode, TrainingImageConfig, Channel, DataSource, S3DataSource, FileSystemDataSource, - NetworkingConfig, + Networking, Tag, MetricDefinition, DebugHookConfig, @@ -53,6 +49,10 @@ CheckpointConfig, InputData, ) +from sagemaker.modules.distributed import ( + DistributedRunner, + TorchrunSMP, +) from sagemaker.modules.utils import ( _get_repo_name_from_image, _get_unique_name, @@ -69,14 +69,14 @@ TRAIN_SCRIPT, DEFAULT_CONTAINER_ENTRYPOINT, DEFAULT_CONTAINER_ARGUMENTS, - SOURCE_CODE_CONFIG_JSON, - DISTRIBUTION_JSON, + SOURCE_CODE_JSON, + DISTRIBUTED_RUNNER_JSON, ) from sagemaker.modules.templates import ( TRAIN_SCRIPT_TEMPLATE, EXECUTE_BASE_COMMANDS, EXECUTE_MPI_DRIVER, - EXECUTE_PYTORCH_DRIVER, + EXEUCTE_TORCHRUN_DRIVER, ) from sagemaker.modules import logger @@ -87,13 +87,13 @@ class ModelTrainer(BaseModel): Example: ```python from sagemaker.modules.train import ModelTrainer - from sagemaker.modules.configs import SourceCodeConfig, ComputeConfig, InputDataSource + from sagemaker.modules.configs import SourceCode, Compute, InputData - source_code_config = SourceCodeConfig(source_dir="source", entry_script="train.py") + source_code = SourceCode(source_dir="source", entry_script="train.py") training_image = "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-training-image" model_trainer = ModelTrainer( training_image=training_image, - source_code_config=source_code_config, + source_code=source_code, ) train_data = InputData(channel_name="train", data_source="s3://bucket/train") @@ -111,19 +111,17 @@ class ModelTrainer(BaseModel): The base name for the training job. If not specified, a default name will be generated using the algorithm name or training image. - source_code_config (Optional[SourceCodeConfig]): + source_code (Optional[SourceCode]): The source code configuration. This is used to configure the source code for running the training job. - distribution_config (Optional[Union[ - MPIDistributionConfig, TorchDistributionConfig - ]]): - The distribution settings for the training job. This is used to configure - a distributed training job. If specifed, a `source_code_config` must also + distributed_runner (Optional[DistributedRunner]): + The distributed runner for the training job. This is used to configure + a distributed training job. If specifed, `source_code` must also be provided. - compute_config (Optional[ComputeConfig]): + compute (Optional[Compute]): The compute configuration. This is used to specify the compute resources for the training job. If not specified, will default to 1 instance of ml.m5.xlarge. - networking_config (Optional[NetworkingConfig]): + networking (Optional[Networking]): The networking configuration. This is used to specify the networking settings for the training job. stopping_condition (Optional[StoppingCondition]): @@ -169,10 +167,10 @@ class ModelTrainer(BaseModel): session: Optional[Session] = None role: Optional[str] = None base_job_name: Optional[str] = None - source_code_config: Optional[SourceCodeConfig] = None - distribution_config: Optional[Union[MPIDistributionConfig, TorchDistributionConfig]] = None - compute_config: Optional[ComputeConfig] = None - networking_config: Optional[NetworkingConfig] = None + source_code: Optional[SourceCode] = None + distributed_runner: Optional[DistributedRunner] = None + compute: Optional[Compute] = None + networking: Optional[Networking] = None stopping_condition: Optional[StoppingCondition] = None training_image: Optional[str] = None training_image_config: Optional[TrainingImageConfig] = None @@ -192,7 +190,6 @@ class ModelTrainer(BaseModel): # Debugger settings _debug_hook_config: Optional[DebugHookConfig] = PrivateAttr(default=None) _debug_rule_configurations: Optional[List[DebugRuleConfiguration]] = PrivateAttr(default=None) - _remote_debug_config: Optional[RemoteDebugConfig] = PrivateAttr(default=None) _profiler_config: Optional[ProfilerConfig] = PrivateAttr(default=None) _profiler_rule_configurations: Optional[List[ProfilerRuleConfiguration]] = PrivateAttr( default=None @@ -204,6 +201,7 @@ class ModelTrainer(BaseModel): _experiment_config: Optional[ExperimentConfig] = PrivateAttr(default=None) _infra_check_config: Optional[InfraCheckConfig] = PrivateAttr(default=None) _session_chaining_config: Optional[SessionChainingConfig] = PrivateAttr(default=None) + _remote_debug_config: Optional[RemoteDebugConfig] = PrivateAttr(default=None) def _validate_training_image_and_algorithm_name( self, training_image: Optional[str], algorithm_name: Optional[str] @@ -218,46 +216,28 @@ def _validate_training_image_and_algorithm_name( "Only one of 'training_image' or 'algorithm_name' must be provided.", ) - # TODO: Move to use pydantic model validators - def _validate_sm_distributed_settings( - self, sm_distributed_settings: Optional[SMDistributedSettings] - ): - """Validate the SM distributed settings.""" - if ( - sm_distributed_settings.enable_dataparallel - and sm_distributed_settings.enable_modelparallel - ): - raise ValueError( - "Both 'enable_dataparallel' and 'enable_modelparallel' cannot be True." - ) - if sm_distributed_settings.modelparallel_parameters: - validate_mp_config(sm_distributed_settings.modelparallel_parameters) - - def _validate_distribution_config( + def _validate_distributed_runner( self, - source_code_config: Optional[SourceCodeConfig], - distribution_config: Optional[Union[MPIDistributionConfig, TorchDistributionConfig]], + source_code: Optional[SourceCode], + distributed_runner: Optional[DistributedRunner], ): """Validate the distribution configuration.""" - if distribution_config and not source_code_config.entry_script: + if distributed_runner and not source_code.entry_script: raise ValueError( - "Must provide 'entry_script' if 'distribution' " - + "is provided in 'source_code_config'.", + "Must provide 'entry_script' if 'distribution' " + "is provided in 'source_code'.", ) - if distribution_config and distribution_config.smdistributed_settings: - self._validate_sm_distributed_settings(distribution_config.smdistributed_settings) # TODO: Move to use pydantic model validators - def _validate_source_code_config(self, source_code_config: Optional[SourceCodeConfig]): + def _validate_source_code(self, source_code: Optional[SourceCode]): """Validate the source code configuration.""" - if source_code_config: - if source_code_config.requirements or source_code_config.entry_script: - source_dir = source_code_config.source_dir - requirements = source_code_config.requirements - entry_script = source_code_config.entry_script + if source_code: + if source_code.requirements or source_code.entry_script: + source_dir = source_code.source_dir + requirements = source_code.requirements + entry_script = source_code.entry_script if not source_dir: raise ValueError( - "If 'requirements' or 'entry_script' is provided in 'source_code_config', " + "If 'requirements' or 'entry_script' is provided in 'source_code', " + "'source_dir' must also be provided.", ) if not _is_valid_path(source_dir, path_type="Directory"): @@ -286,8 +266,8 @@ def _validate_source_code_config(self, source_code_config: Optional[SourceCodeCo def model_post_init(self, __context: Any): """Post init method to perform custom validation and set default values.""" self._validate_training_image_and_algorithm_name(self.training_image, self.algorithm_name) - self._validate_source_code_config(self.source_code_config) - self._validate_distribution_config(self.source_code_config, self.distribution_config) + self._validate_source_code(self.source_code) + self._validate_distributed_runner(self.source_code, self.distributed_runner) if self.session is None: self.session = Session() @@ -304,13 +284,13 @@ def model_post_init(self, __context: Any): self.base_job_name = f"{_get_repo_name_from_image(self.training_image)}-job" logger.warning(f"Base name not provided. Using default name:\n{self.base_job_name}") - if self.compute_config is None: - self.compute_config = ComputeConfig( + if self.compute is None: + self.compute = Compute( instance_type=DEFAULT_INSTANCE_TYPE, instance_count=1, volume_size_in_gb=30, ) - logger.warning(f"ComputeConfig not provided. Using default:\n{self.compute_config}") + logger.warning(f"Compute not provided. Using default:\n{self.compute}") if self.stopping_condition is None: self.stopping_condition = StoppingCondition( @@ -334,7 +314,7 @@ def model_post_init(self, __context: Any): f"OutputDataConfig not provided. Using default:\n{self.output_data_config}" ) - # TODO: Autodetect which image to use if source_code_config is provided + # TODO: Autodetect which image to use if source_code is provided if self.training_image: logger.info(f"Training image URI: {self.training_image}") @@ -342,8 +322,8 @@ def model_post_init(self, __context: Any): def train( self, input_data_config: Optional[List[Union[Channel, InputData]]] = None, - wait: bool = True, - logs: bool = True, + wait: Optional[bool] = True, + logs: Optional[bool] = True, ): """Train a model using AWS SageMaker. @@ -374,43 +354,35 @@ def train( container_entrypoint = None container_arguments = None - if self.source_code_config: + if self.source_code: # If source code is provided, create a channel for the source code - # The source code will be mounted at /opt/ml/input/data/code in the container - if self.source_code_config.source_dir: + # The source code will be mounted at /opt/ml/input/data/sm_code in the container + if self.source_code.source_dir: source_code_channel = self.create_input_data_channel( - SM_CODE, self.source_code_config.source_dir + SM_CODE, self.source_code.source_dir ) input_data_config.append(source_code_channel) self._prepare_train_script( - source_code_config=self.source_code_config, - distribution_config=self.distribution_config, + source_code=self.source_code, distributed_runner=self.distributed_runner ) - if self.distribution_config: - smd_modelparallel_parameters = getattr( - self.distribution_config.smdistributed_settings, - "modelparallel_parameters", - None, - ) - if smd_modelparallel_parameters: - string_hyper_parameters["mp_parameters"] = json.dumps( - smd_modelparallel_parameters - ) - self._write_source_code_config_json(self.source_code_config) - if self.distribution_config: - self._write_distribution_config_json(self.distribution_config) + if isinstance(self.distributed_runner, TorchrunSMP): + mp_parameters = self.distributed_runner._to_mp_parameters_dict() + string_hyper_parameters["mp_parameters"] = json.dumps(mp_parameters) + + self._write_source_code_json(self.source_code) + self._write_distributed_runner_json(self.distributed_runner) # Create an input channel for drivers packaged by the sdk sm_drivers_channel = self.create_input_data_channel(SM_DRIVERS, SM_DRIVERS_LOCAL_PATH) input_data_config.append(sm_drivers_channel) - # If source_code_config is provided, we will always use + # If source_code is provided, we will always use # the default container entrypoint and arguments - # to execute the train.sh script. - # Any commands generated from the source_code_config will be - # executed from the train.sh script. + # to execute the sm_train.sh script. + # Any commands generated from the source_code will be + # executed from the sm_train.sh script. container_entrypoint = DEFAULT_CONTAINER_ENTRYPOINT container_arguments = DEFAULT_CONTAINER_ARGUMENTS @@ -425,8 +397,8 @@ def train( enable_sage_maker_metrics_time_series=self._enable_sage_maker_metrics_time_series, ) - resource_config = self.compute_config._to_resource_config() - vpc_config = self.networking_config._to_vpc_config() if self.networking_config else None + resource_config = self.compute._to_resource_config() + vpc_config = self.networking._to_vpc_config() if self.networking else None training_job = TrainingJob.create( training_job_name=_get_unique_name(self.base_job_name), @@ -443,14 +415,14 @@ def train( output_data_config=self.output_data_config, checkpoint_config=self.checkpoint_config, environment=self.environment, - enable_managed_spot_training=self.compute_config.enable_managed_spot_training, + enable_managed_spot_training=self.compute.enable_managed_spot_training, enable_inter_container_traffic_encryption=( - self.networking_config.enable_inter_container_traffic_encryption - if self.networking_config + self.networking.enable_inter_container_traffic_encryption + if self.networking else None ), enable_network_isolation=( - self.networking_config.enable_network_isolation if self.networking_config else None + self.networking.enable_network_isolation if self.networking else None ), # Private Instance Attributes debug_hook_config=self._debug_hook_config, @@ -553,76 +525,73 @@ def _get_input_data_config( ) return channels - def _write_source_code_config_json(self, source_code_config: SourceCodeConfig): + def _write_source_code_json(self, source_code: SourceCode): """Write the source code configuration to a JSON file.""" - file_path = os.path.join(SM_DRIVERS_LOCAL_PATH, SOURCE_CODE_CONFIG_JSON) + file_path = os.path.join(SM_DRIVERS_LOCAL_PATH, SOURCE_CODE_JSON) with open(file_path, "w") as f: - f.write(source_code_config.model_dump_json()) + dump = source_code.model_dump(exclude_none=True) if source_code else {} + f.write(json.dumps(dump)) - def _write_distribution_config_json( - self, distribution: Union[MPIDistributionConfig, TorchDistributionConfig] + def _write_distributed_runner_json( + self, distributed_runner: Optional[DistributedRunner] = None ): - """Write the distribution configuration to a JSON file.""" - file_path = os.path.join(SM_DRIVERS_LOCAL_PATH, DISTRIBUTION_JSON) + """Write the distributed runner configuration to a JSON file.""" + file_path = os.path.join(SM_DRIVERS_LOCAL_PATH, DISTRIBUTED_RUNNER_JSON) with open(file_path, "w") as f: - f.write(distribution.model_dump_json()) + dump = distributed_runner.model_dump(exclude_none=True) if distributed_runner else {} + f.write(json.dumps(dump)) def _prepare_train_script( self, - source_code_config: SourceCodeConfig, - distribution_config: Optional[Union[MPIDistributionConfig, TorchDistributionConfig]] = None, + source_code: SourceCode, + distributed_runner: Optional[DistributedRunner] = None, ): """Prepare the training script to be executed in the training job container. Args: - source_code_config (SourceCodeConfig): The source code configuration. + source_code (SourceCodeConfig): The source code configuration. """ base_command = "" - if source_code_config.command: - if source_code_config.entry_script: + if source_code.command: + if source_code.entry_script: logger.warning( "Both 'command' and 'entry_script' are provided in the SourceCodeConfig. " + "Defaulting to 'command'." ) - base_command = source_code_config.command.split() + base_command = source_code.command.split() base_command = " ".join(base_command) - if ( - source_code_config.entry_script - and not source_code_config.command - and not distribution_config - ): - if source_code_config.entry_script.endswith(".py"): - base_command = f"$SM_PYTHON_CMD {source_code_config.entry_script}" - elif source_code_config.entry_script.endswith(".sh"): + if source_code.entry_script and not source_code.command and not distributed_runner: + if source_code.entry_script.endswith(".py"): + base_command = f"$SM_PYTHON_CMD {source_code.entry_script}" + elif source_code.entry_script.endswith(".sh"): base_command = ( - f"chmod +x {source_code_config.entry_script} && " - f"bash {source_code_config.entry_script}" + f"chmod +x {source_code.entry_script} && " f"bash {source_code.entry_script}" ) else: raise ValueError( - f"Unsupported entry script: {source_code_config.entry_script}." + f"Unsupported entry script: {source_code.entry_script}." + "Only .py and .sh scripts are supported." ) install_requirements = "" - if source_code_config.requirements: + if source_code.requirements: install_requirements = "echo 'Installing requirements'\n" - install_requirements = f"$SM_PIP_CMD install -r {source_code_config.requirements}" + install_requirements = f"$SM_PIP_CMD install -r {source_code.requirements}" working_dir = "" - if source_code_config.source_dir: + if source_code.source_dir: working_dir = f"cd {SM_CODE_CONTAINER_PATH}" if base_command: execute_driver = EXECUTE_BASE_COMMANDS.format(base_command=base_command) - elif distribution_config: - distribution_type = distribution_config._distribution_type + elif distributed_runner: + distribution_type = distributed_runner._type if distribution_type == "mpi": execute_driver = EXECUTE_MPI_DRIVER - elif distribution_type == "torch_distributed": - execute_driver = EXECUTE_PYTORCH_DRIVER + elif distribution_type == "torchrun": + execute_driver = EXEUCTE_TORCHRUN_DRIVER else: raise ValueError(f"Unsupported distribution type: {distribution_type}.") @@ -676,7 +645,6 @@ def with_debugger_settings( self, debug_hook_config: Optional[DebugHookConfig] = None, debug_rule_configurations: Optional[List[DebugRuleConfiguration]] = None, - remote_debug_config: Optional[RemoteDebugConfig] = None, profiler_config: Optional[ProfilerConfig] = None, profiler_rule_configurations: Optional[List[ProfilerRuleConfiguration]] = None, tensor_board_output_config: Optional[TensorBoardOutputConfig] = None, @@ -708,9 +676,6 @@ def with_debugger_settings( debug_rule_configurations (Optional[List[DebugRuleConfiguration]]): Configuration information for Amazon SageMaker Debugger rules for debugging output ensors. - remote_debug_config (Optional[RemoteDebugConfig]): - Configuration for remote debugging. To learn more see: - https://docs.aws.amazon.com/sagemaker/latest/dg/train-remote-debugging.html profiler_config (ProfilerConfig): Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths. @@ -723,7 +688,6 @@ def with_debugger_settings( """ self._debug_hook_config = debug_hook_config self._debug_rule_configurations = debug_rule_configurations - self._remote_debug_config = remote_debug_config self._profiler_config = profiler_config self._profiler_rule_configurations = profiler_rule_configurations self._tensor_board_output_config = tensor_board_output_config @@ -735,6 +699,7 @@ def with_additional_settings( experiment_config: Optional[ExperimentConfig] = None, infra_check_config: Optional[InfraCheckConfig] = None, session_chaining_config: Optional[SessionChainingConfig] = None, + remote_debug_config: Optional[RemoteDebugConfig] = None, ) -> "ModelTrainer": """Set any additional settings for the training job. @@ -761,9 +726,13 @@ def with_additional_settings( session_chaining_config (Optional[SessionChainingConfig]): Contains information about attribute-based access control (ABAC) for the training job. + remote_debug_config (Optional[RemoteDebugConfig]): + Configuration for remote debugging through AWS Systems Manager. To learn more see: + https://docs.aws.amazon.com/sagemaker/latest/dg/train-remote-debugging.html """ self._retry_strategy = retry_strategy self._experiment_config = experiment_config self._infra_check_config = infra_check_config self._session_chaining_config = session_chaining_config + self._remote_debug_config = remote_debug_config return self diff --git a/tests/data/modules/script_mode/custom_script.py b/tests/data/modules/script_mode/custom_script.py new file mode 100644 index 0000000000..acacf6c19e --- /dev/null +++ b/tests/data/modules/script_mode/custom_script.py @@ -0,0 +1,146 @@ +# flake8: noqa +import argparse +import numpy as np +import os +import sys +import logging +import json +import shutil +import torch +import torch.nn as nn +from torch.utils.data import DataLoader, TensorDataset +from pytorch_model_def import get_model + +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) +logger.addHandler(logging.StreamHandler(sys.stdout)) +current_dir = os.path.dirname(os.path.abspath(__file__)) + + +def get_train_data(train_dir): + """ + Get the training data and convert to tensors + """ + + x_train = np.load(os.path.join(train_dir, "x_train.npy")) + y_train = np.load(os.path.join(train_dir, "y_train.npy")) + logger.info(f"x train: {x_train.shape}, y train: {y_train.shape}") + + return torch.from_numpy(x_train), torch.from_numpy(y_train) + + +def get_test_data(test_dir): + """ + Get the testing data and convert to tensors + """ + + x_test = np.load(os.path.join(test_dir, "x_test.npy")) + y_test = np.load(os.path.join(test_dir, "y_test.npy")) + logger.info(f"x test: {x_test.shape}, y test: {y_test.shape}") + + return torch.from_numpy(x_test), torch.from_numpy(y_test) + + +def model_fn(model_dir): + """ + Load the model for inference + """ + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = get_model() + model.load_state_dict(torch.load(model_dir + "/model.pth")) + model.eval() + return model.to(device) + + +def input_fn(request_body, request_content_type): + """ + Deserialize and prepare the prediction input + """ + + if request_content_type == "application/json": + request = json.loads(request_body) + train_inputs = torch.tensor(request) + return train_inputs + + +def predict_fn(input_data, model): + """ + Apply model to the incoming request + """ + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model.to(device) + model.eval() + with torch.no_grad(): + return model(input_data.float()).numpy()[0] + + +def train(): + """ + Train the PyTorch model + """ + # Directories: train, test and model + train_dir = os.path.join(current_dir, "data/train") + test_dir = os.path.join(current_dir, "data/test") + model_dir = os.environ.get("SM_MODEL_DIR", os.path.join(current_dir, "data/model")) + + # Load the training and testing data + x_train, y_train = get_train_data(train_dir) + x_test, y_test = get_test_data(test_dir) + train_ds = TensorDataset(x_train, y_train) + + # Training parameters - used to configure the training loop + batch_size = 64 + epochs = 1 + learning_rate = 0.1 + logger.info( + "batch_size = {}, epochs = {}, learning rate = {}".format(batch_size, epochs, learning_rate) + ) + + train_dl = DataLoader(train_ds, batch_size, shuffle=True) + + # Define the model, loss function and optimizer + model = get_model() + model = model.to(device) + criterion = nn.MSELoss() + optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) + + # Train the model + for epoch in range(epochs): + for x_train_batch, y_train_batch in train_dl: + y = model(x_train_batch.float()) + loss = criterion(y.flatten(), y_train_batch.float()) + optimizer.zero_grad() + loss.backward() + optimizer.step() + epoch += 1 + logger.info(f"epoch: {epoch} -> loss: {loss}") + + # Test the model + with torch.no_grad(): + y = model(x_test.float()).flatten() + mse = ((y - y_test) ** 2).sum() / y_test.shape[0] + print("\nTest MSE:", mse.numpy()) + + # Save the model + os.makedirs(model_dir, exist_ok=True) + torch.save(model.state_dict(), model_dir + "/model.pth") + inference_code_path = model_dir + "/code/" + + if not os.path.exists(inference_code_path): + os.mkdir(inference_code_path) + logger.info("Created a folder at {}!".format(inference_code_path)) + + code_dir = os.environ.get("SM_CHANNEL_CODE", current_dir) + shutil.copy(os.path.join(code_dir, "custom_script.py"), inference_code_path) + shutil.copy(os.path.join(code_dir, "pytorch_model_def.py"), inference_code_path) + logger.info("Saving models files to {}".format(inference_code_path)) + + +if __name__ == "__main__": + print("Running the training job ...\n") + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + train() diff --git a/tests/data/modules/script_mode/data/test/x_test.npy b/tests/data/modules/script_mode/data/test/x_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..a9977e39c042722d4103758e390eaffa6653b937 GIT binary patch literal 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zSbw0C`fd%v9p;lVZJ=ib*g!p&^G) Date: Sun, 10 Nov 2024 19:40:09 -0800 Subject: [PATCH 21/79] Remove ignored files --- .../container_drivers/distributed_runner.json | 1 - .../train/container_drivers/sm_train.sh | 64 ------------------- .../train/container_drivers/sourcecode.json | 1 - 3 files changed, 66 deletions(-) delete mode 100644 src/sagemaker/modules/train/container_drivers/distributed_runner.json delete mode 100644 src/sagemaker/modules/train/container_drivers/sm_train.sh delete mode 100644 src/sagemaker/modules/train/container_drivers/sourcecode.json diff --git a/src/sagemaker/modules/train/container_drivers/distributed_runner.json b/src/sagemaker/modules/train/container_drivers/distributed_runner.json deleted file mode 100644 index a90706c134..0000000000 --- a/src/sagemaker/modules/train/container_drivers/distributed_runner.json +++ /dev/null @@ -1 +0,0 @@ -{"mpi_additional_options": ["-x", "MASTER_ADDR=algo-1", "-x", "MASTER_PORT=7777"], "_type": "mpi"} \ No newline at end of file diff --git a/src/sagemaker/modules/train/container_drivers/sm_train.sh b/src/sagemaker/modules/train/container_drivers/sm_train.sh deleted file mode 100644 index cc3915c887..0000000000 --- a/src/sagemaker/modules/train/container_drivers/sm_train.sh +++ /dev/null @@ -1,64 +0,0 @@ - -#!/bin/bash -set -e -echo "Starting training script" - -handle_error() { - EXIT_STATUS=$? - echo "An error occurred with exit code $EXIT_STATUS" - if [ ! -s /opt/ml/output/failure ]; then - echo "Training Execution failed. For more details, see CloudWatch logs at 'aws/sagemaker/TrainingJobs'. -TrainingJob - $TRAINING_JOB_NAME" >> /opt/ml/output/failure - fi - exit $EXIT_STATUS -} - -check_python() { - if command -v python3 &>/dev/null; then - SM_PYTHON_CMD="python3" - SM_PIP_CMD="pip3" - echo "Found python3" - elif command -v python &>/dev/null; then - SM_PYTHON_CMD="python" - SM_PIP_CMD="pip" - echo "Found python" - else - echo "Python may not be installed" - return 1 - fi -} - -trap 'handle_error' ERR - -check_python - -$SM_PYTHON_CMD --version - -echo "/opt/ml/input/config/resourceconfig.json:" -cat /opt/ml/input/config/resourceconfig.json -echo - -echo "/opt/ml/input/config/inputdataconfig.json:" -cat /opt/ml/input/config/inputdataconfig.json -echo - -echo "/opt/ml/input/data/sm_drivers/sourcecode.json" -cat /opt/ml/input/data/sm_drivers/sourcecode.json -echo - -echo "/opt/ml/input/data/sm_drivers/distributed_runner.json" -cat /opt/ml/input/data/sm_drivers/distributed_runner.json -echo - -echo "Setting up environment variables" -$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/scripts/environment.py -source /opt/ml/input/data/sm_drivers/scripts/sm_training.env - -cd /opt/ml/input/data/sm_code -$SM_PIP_CMD install -r requirements.txt - -echo "Running MPI driver" -$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/mpi_driver.py - - -echo "Training Container Execution Completed" diff --git a/src/sagemaker/modules/train/container_drivers/sourcecode.json b/src/sagemaker/modules/train/container_drivers/sourcecode.json deleted file mode 100644 index bee6559762..0000000000 --- a/src/sagemaker/modules/train/container_drivers/sourcecode.json +++ /dev/null @@ -1 +0,0 @@ -{"source_dir": "distributed-training/scripts", "requirements": "requirements.txt", "entry_script": "run_clm_no_trainer.py"} \ No newline at end of file From fa6ae28ca9e8bd3c54593431d8ea0d572cfde650 Mon Sep 17 00:00:00 2001 From: Pravali Uppugunduri <46845440+pravali96@users.noreply.github.com> Date: Mon, 11 Nov 2024 11:06:55 -0800 Subject: [PATCH 22/79] Add in_process mode support for DJL and TorchServe servers (#1570) * add in-process mode for DJL server * fix format * add inference_spec as a member of DJL * add the validations for model server * fix typo * fix test assertion * add unit-testing * have a common server for inprocess mode * fix failing tests * add support to torchserve * fix tests to include torchserve servers * use custom inference_spec code instead of HF pipelines * fix tests for app.py * fix unit test failure * fix format * use schema_builder for serialization and deserialization * remove task field * remove unused import --- src/sagemaker/serve/builder/djl_builder.py | 36 ++++++++--- src/sagemaker/serve/builder/model_builder.py | 25 ++++++-- .../serve/builder/transformers_builder.py | 4 +- src/sagemaker/serve/mode/in_process_mode.py | 19 +++--- .../in_process_model_server}/app.py | 48 ++++++++------- .../in_process_server.py | 60 +++++++++++++++++++ .../model_server/multi_model_server/server.py | 59 ------------------ src/sagemaker/serve/utils/predictors.py | 6 +- .../serve/builder/test_djl_builder.py | 57 +++++++++++++++++- .../serve/builder/test_model_builder.py | 2 +- .../serve/mode/test_in_process_mode.py | 37 ++++++++++-- .../in_process_model_server}/test_app.py | 38 ++++++------ 12 files changed, 257 insertions(+), 134 deletions(-) rename src/sagemaker/serve/{ => model_server/in_process_model_server}/app.py (63%) create mode 100644 src/sagemaker/serve/model_server/in_process_model_server/in_process_server.py rename tests/unit/sagemaker/serve/{ => model_server/in_process_model_server}/test_app.py (60%) diff --git a/src/sagemaker/serve/builder/djl_builder.py b/src/sagemaker/serve/builder/djl_builder.py index 608e1c604f..d7be1fab7b 100644 --- a/src/sagemaker/serve/builder/djl_builder.py +++ b/src/sagemaker/serve/builder/djl_builder.py @@ -47,7 +47,7 @@ from sagemaker.serve.model_server.djl_serving.prepare import ( _create_dir_structure, ) -from sagemaker.serve.utils.predictors import DjlLocalModePredictor +from sagemaker.serve.utils.predictors import InProcessModePredictor, DjlLocalModePredictor from sagemaker.serve.utils.types import ModelServer from sagemaker.serve.mode.function_pointers import Mode from sagemaker.serve.utils.telemetry_logger import _capture_telemetry @@ -55,6 +55,7 @@ from sagemaker.base_predictor import PredictorBase logger = logging.getLogger(__name__) +LOCAL_MODES = [Mode.LOCAL_CONTAINER, Mode.IN_PROCESS] # Match JumpStart DJL entrypoint format _CODE_FOLDER = "code" @@ -77,6 +78,7 @@ def __init__(self): self.mode = None self.model_server = None self.image_uri = None + self.inference_spec = None self._is_custom_image_uri = False self.image_config = None self.vpc_config = None @@ -96,11 +98,11 @@ def __init__(self): @abstractmethod def _prepare_for_mode(self): - """Placeholder docstring""" + """Abstract method""" @abstractmethod def _get_client_translators(self): - """Placeholder docstring""" + """Abstract method""" def _is_djl(self): """Placeholder docstring""" @@ -146,7 +148,7 @@ def _create_djl_model(self) -> Type[Model]: @_capture_telemetry("djl.deploy") def _djl_model_builder_deploy_wrapper(self, *args, **kwargs) -> Type[PredictorBase]: - """Placeholder docstring""" + """Returns predictor depending on local mode or endpoint mode""" timeout = kwargs.get("model_data_download_timeout") if timeout: self.env_vars.update({"MODEL_LOADING_TIMEOUT": str(timeout)}) @@ -189,6 +191,18 @@ def _djl_model_builder_deploy_wrapper(self, *args, **kwargs) -> Type[PredictorBa serializer = self.schema_builder.input_serializer deserializer = self.schema_builder._output_deserializer + + if self.mode == Mode.IN_PROCESS: + + predictor = InProcessModePredictor( + self.modes[str(Mode.IN_PROCESS)], serializer, deserializer + ) + + self.modes[str(Mode.IN_PROCESS)].create_server( + predictor, + ) + return predictor + if self.mode == Mode.LOCAL_CONTAINER: timeout = kwargs.get("model_data_download_timeout") @@ -249,9 +263,15 @@ def _build_for_hf_djl(self): _create_dir_structure(self.model_path) if not hasattr(self, "pysdk_model"): - self.env_vars.update({"HF_MODEL_ID": self.model}) + if self.inference_spec is not None: + self.env_vars.update({"HF_MODEL_ID": self.inference_spec.get_model()}) + else: + self.env_vars.update({"HF_MODEL_ID": self.model}) + + logger.info(self.env_vars) + self.hf_model_config = _get_model_config_properties_from_hf( - self.model, self.env_vars.get("HF_TOKEN") + self.env_vars.get("HF_MODEL_ID"), self.env_vars.get("HF_TOKEN") ) default_djl_configurations, _default_max_new_tokens = _get_default_djl_configurations( self.model, self.hf_model_config, self.schema_builder @@ -260,9 +280,10 @@ def _build_for_hf_djl(self): self.schema_builder.sample_input["parameters"][ "max_new_tokens" ] = _default_max_new_tokens + self.pysdk_model = self._create_djl_model() - if self.mode == Mode.LOCAL_CONTAINER: + if self.mode in LOCAL_MODES: self._prepare_for_mode() return self.pysdk_model @@ -451,7 +472,6 @@ def _build_for_djl(self): """Placeholder docstring""" self._validate_djl_serving_sample_data() self.secret_key = None - self.pysdk_model = self._build_for_hf_djl() self.pysdk_model.tune = self._tune_for_hf_djl if self.role_arn: diff --git a/src/sagemaker/serve/builder/model_builder.py b/src/sagemaker/serve/builder/model_builder.py index 157222b437..bdbd08ff55 100644 --- a/src/sagemaker/serve/builder/model_builder.py +++ b/src/sagemaker/serve/builder/model_builder.py @@ -81,7 +81,7 @@ _extract_speculative_draft_model_provider, _jumpstart_speculative_decoding, ) -from sagemaker.serve.utils.predictors import _get_local_mode_predictor +from sagemaker.serve.utils.predictors import _get_local_mode_predictor, InProcessModePredictor from sagemaker.serve.utils.hardware_detector import ( _get_gpu_info, _get_gpu_info_fallback, @@ -566,6 +566,18 @@ def _model_builder_deploy_wrapper( if mode and mode != self.mode: self._overwrite_mode_in_deploy(overwrite_mode=mode) + if self.mode == Mode.IN_PROCESS: + serializer, deserializer = self._get_client_translators() + + predictor = InProcessModePredictor( + self.modes[str(Mode.IN_PROCESS)], serializer, deserializer + ) + + self.modes[str(Mode.IN_PROCESS)].create_server( + predictor, + ) + return predictor + if self.mode == Mode.LOCAL_CONTAINER: serializer, deserializer = self._get_client_translators() predictor = _get_local_mode_predictor( @@ -919,11 +931,16 @@ def build( # pylint: disable=R0911 def _build_validations(self): """Validations needed for model server overrides, or auto-detection or fallback""" - if self.mode == Mode.IN_PROCESS and self.model_server is not ModelServer.MMS: + if ( + self.mode == Mode.IN_PROCESS + and self.model_server is not ModelServer.MMS + and self.model_server is not ModelServer.DJL_SERVING + and self.model_server is not ModelServer.TORCHSERVE + ): raise ValueError( - "IN_PROCESS mode is only supported for MMS/Transformers server in beta release." + "IN_PROCESS mode is only supported for the following servers " + "in beta release: MMS/Transformers, TORCHSERVE, DJL_SERVING server" ) - if self.inference_spec and self.model: raise ValueError("Can only set one of the following: model, inference_spec.") diff --git a/src/sagemaker/serve/builder/transformers_builder.py b/src/sagemaker/serve/builder/transformers_builder.py index b7baf6b513..0388a9a05d 100644 --- a/src/sagemaker/serve/builder/transformers_builder.py +++ b/src/sagemaker/serve/builder/transformers_builder.py @@ -38,7 +38,7 @@ from sagemaker.serve.utils.optimize_utils import _is_optimized from sagemaker.serve.utils.predictors import ( TransformersLocalModePredictor, - TransformersInProcessModePredictor, + InProcessModePredictor, ) from sagemaker.serve.utils.types import ModelServer from sagemaker.serve.mode.function_pointers import Mode @@ -237,7 +237,7 @@ def _transformers_model_builder_deploy_wrapper(self, *args, **kwargs) -> Type[Pr if self.mode == Mode.IN_PROCESS: timeout = kwargs.get("model_data_download_timeout") - predictor = TransformersInProcessModePredictor( + predictor = InProcessModePredictor( self.modes[str(Mode.IN_PROCESS)], serializer, deserializer ) diff --git a/src/sagemaker/serve/mode/in_process_mode.py b/src/sagemaker/serve/mode/in_process_mode.py index 60d4f91e34..9df15fb6cc 100644 --- a/src/sagemaker/serve/mode/in_process_mode.py +++ b/src/sagemaker/serve/mode/in_process_mode.py @@ -13,20 +13,15 @@ from sagemaker.serve.builder.schema_builder import SchemaBuilder from sagemaker.serve.utils.types import ModelServer from sagemaker.serve.utils.exceptions import InProcessDeepPingException -from sagemaker.serve.model_server.multi_model_server.server import InProcessMultiModelServer +from sagemaker.serve.model_server.in_process_model_server.in_process_server import InProcessServing from sagemaker.session import Session logger = logging.getLogger(__name__) -_PING_HEALTH_CHECK_FAIL_MSG = ( - "Ping health check did not pass. " - + "Please increase container_timeout_seconds or review your inference code." -) +_PING_HEALTH_CHECK_FAIL_MSG = "Ping health check did not pass. Please review your inference code." -class InProcessMode( - InProcessMultiModelServer, -): +class InProcessMode(InProcessServing): """A class that holds methods to deploy model to a container in process environment""" def __init__( @@ -70,7 +65,13 @@ def create_server( logger.info("Waiting for model server %s to start up...", self.model_server) if self.model_server == ModelServer.MMS: - self._ping_local_server = self._multi_model_server_deep_ping + self._ping_local_server = self._deep_ping + self._start_serving() + elif self.model_server == ModelServer.DJL_SERVING: + self._ping_local_server = self._deep_ping + self._start_serving() + elif self.model_server == ModelServer.TORCHSERVE: + self._ping_local_server = self._deep_ping self._start_serving() # allow some time for server to be ready. diff --git a/src/sagemaker/serve/app.py b/src/sagemaker/serve/model_server/in_process_model_server/app.py similarity index 63% rename from src/sagemaker/serve/app.py rename to src/sagemaker/serve/model_server/in_process_model_server/app.py index fd9dd6a93a..31281bc39f 100644 --- a/src/sagemaker/serve/app.py +++ b/src/sagemaker/serve/model_server/in_process_model_server/app.py @@ -3,10 +3,13 @@ from __future__ import absolute_import import asyncio +import io import logging import threading from typing import Optional +from sagemaker.serve.spec.inference_spec import InferenceSpec +from sagemaker.serve.builder.schema_builder import SchemaBuilder logger = logging.getLogger(__name__) @@ -17,12 +20,6 @@ logger.error("Unable to import uvicorn, check if uvicorn is installed.") -try: - from transformers import pipeline -except ImportError: - logger.error("Unable to import transformers, check if transformers is installed.") - - try: from fastapi import FastAPI, Request, APIRouter except ImportError: @@ -30,32 +27,37 @@ class InProcessServer: - """Placeholder docstring""" + """Generic In-Process Server for Serving Models using InferenceSpec""" - def __init__(self, model_id: Optional[str] = None, task: Optional[str] = None): + def __init__( + self, + inference_spec: Optional[InferenceSpec] = None, + schema_builder: Optional[SchemaBuilder] = None, + ): self._thread = None self._loop = None self._stop_event = asyncio.Event() self._router = APIRouter() - self._model_id = model_id - self._task = task self.server = None self.port = None self.host = None - # TODO: Pick up device automatically. - self._generator = pipeline(task, model=model_id, device="cpu") - - # pylint: disable=unused-variable - @self._router.post("/generate") - async def generate_text(prompt: Request): - """Placeholder docstring""" - str_prompt = await prompt.json() - str_prompt = str_prompt["inputs"] if "inputs" in str_prompt else str_prompt - - generated_text = self._generator( - str_prompt, max_length=30, num_return_sequences=1, truncation=True + self.inference_spec = inference_spec + self.schema_builder = schema_builder + self._load_model = self.inference_spec.load(model_dir=None) + + @self._router.post("/invoke") + async def invoke(request: Request): + """Generate text based on the provided prompt""" + + request_header = request.headers + request_body = await request.body() + content_type = request_header.get("Content-Type", None) + input_data = schema_builder.input_deserializer.deserialize( + io.BytesIO(request_body), content_type[0] ) - return generated_text + logger.debug(f"Received request: {input_data}") + response = self.inference_spec.invoke(input_data, self._load_model) + return response self._create_server() diff --git a/src/sagemaker/serve/model_server/in_process_model_server/in_process_server.py b/src/sagemaker/serve/model_server/in_process_model_server/in_process_server.py new file mode 100644 index 0000000000..a5c58c6091 --- /dev/null +++ b/src/sagemaker/serve/model_server/in_process_model_server/in_process_server.py @@ -0,0 +1,60 @@ +"""Module for In_process Serving""" + +from __future__ import absolute_import + +import requests +import logging +from sagemaker.serve.utils.exceptions import LocalModelInvocationException +from sagemaker.base_predictor import PredictorBase + +logger = logging.getLogger(__name__) + + +class InProcessServing: + """In Process Mode server instance""" + + def _start_serving(self): + """Initializes the start of the server""" + from sagemaker.serve.model_server.in_process_model_server.app import InProcessServer + + self.server = InProcessServer( + inference_spec=self.inference_spec, schema_builder=self.schema_builder + ) + self.server.start_server() + + def _stop_serving(self): + """Stops the server""" + self.server.stop_server() + + def _invoke_serving(self, request: object, content_type: str, accept: str): + """Placeholder docstring""" + try: + response = requests.post( + f"http://{self.server.host}:{self.server.port}/invoke", + data=request, + headers={"Content-Type": content_type, "Accept": accept}, + timeout=600, + ) + response.raise_for_status() + + return response.content + except Exception as e: + if "Connection refused" in str(e): + raise Exception( + "Unable to send request to the local server: Connection refused." + ) from e + raise Exception("Unable to send request to the local container server %s", str(e)) + + def _deep_ping(self, predictor: PredictorBase): + """Sends a deep ping to ensure prediction""" + healthy = False + response = None + try: + response = predictor.predict(self.schema_builder.sample_input) + healthy = response is not None + # pylint: disable=broad-except + except Exception as e: + if "422 Client Error: Unprocessable Entity for url" in str(e): + raise LocalModelInvocationException(str(e)) + + return healthy, response diff --git a/src/sagemaker/serve/model_server/multi_model_server/server.py b/src/sagemaker/serve/model_server/multi_model_server/server.py index 69d5d2e5e7..2fab727c05 100644 --- a/src/sagemaker/serve/model_server/multi_model_server/server.py +++ b/src/sagemaker/serve/model_server/multi_model_server/server.py @@ -2,8 +2,6 @@ from __future__ import absolute_import -import json - import requests import logging import platform @@ -11,7 +9,6 @@ from sagemaker import Session, fw_utils from sagemaker.serve.utils.exceptions import LocalModelInvocationException -from sagemaker.serve.utils.exceptions import InProcessDeepPingException from sagemaker.base_predictor import PredictorBase from sagemaker.s3_utils import determine_bucket_and_prefix, parse_s3_url, s3_path_join from sagemaker.s3 import S3Uploader @@ -24,62 +21,6 @@ logger = logging.getLogger(__name__) -class InProcessMultiModelServer: - """In Process Mode Multi Model server instance""" - - def _start_serving(self): - """Initializes the start of the server""" - from sagemaker.serve.app import InProcessServer - - if hasattr(self, "inference_spec"): - model_id = self.inference_spec.get_model() - if not model_id: - raise ValueError("Model id was not provided in Inference Spec.") - else: - model_id = None - self.server = InProcessServer(model_id=model_id) - - self.server.start_server() - - def _stop_serving(self): - """Stops the server""" - self.server.stop_server() - - def _invoke_multi_model_server_serving(self, request: bytes, content_type: str, accept: str): - """Placeholder docstring""" - try: - response = requests.post( - f"http://{self.server.host}:{self.server.port}/generate", - data=request, - headers={"Content-Type": content_type, "Accept": accept}, - timeout=600, - ) - response.raise_for_status() - if isinstance(response.content, bytes): - return json.loads(response.content.decode("utf-8")) - return response.content - except Exception as e: - if "Connection refused" in str(e): - raise Exception( - "Unable to send request to the local server: Connection refused." - ) from e - raise Exception("Unable to send request to the local server.") from e - - def _multi_model_server_deep_ping(self, predictor: PredictorBase): - """Sends a deep ping to ensure prediction""" - healthy = False - response = None - try: - response = predictor.predict(self.schema_builder.sample_input) - healthy = response is not None - # pylint: disable=broad-except - except Exception as e: - if "422 Client Error: Unprocessable Entity for url" in str(e): - raise InProcessDeepPingException(str(e)) - - return healthy, response - - class LocalMultiModelServer: """Local Multi Model server instance""" diff --git a/src/sagemaker/serve/utils/predictors.py b/src/sagemaker/serve/utils/predictors.py index 89ec2253f1..6346bb3111 100644 --- a/src/sagemaker/serve/utils/predictors.py +++ b/src/sagemaker/serve/utils/predictors.py @@ -212,8 +212,8 @@ def delete_predictor(self): self._mode_obj.destroy_server() -class TransformersInProcessModePredictor(PredictorBase): - """Lightweight Transformers predictor for in process mode deployment""" +class InProcessModePredictor(PredictorBase): + """Lightweight predictor for in process mode deployment""" def __init__( self, @@ -227,7 +227,7 @@ def __init__( def predict(self, data): """Placeholder docstring""" - return self._mode_obj._invoke_multi_model_server_serving( + return self._mode_obj._invoke_serving( self.serializer.serialize(data), self.content_type, self.deserializer.ACCEPT[0], diff --git a/tests/unit/sagemaker/serve/builder/test_djl_builder.py b/tests/unit/sagemaker/serve/builder/test_djl_builder.py index 69f8c7a8d5..9c4488fa3e 100644 --- a/tests/unit/sagemaker/serve/builder/test_djl_builder.py +++ b/tests/unit/sagemaker/serve/builder/test_djl_builder.py @@ -24,7 +24,7 @@ LocalModelOutOfMemoryException, LocalModelInvocationException, ) -from sagemaker.serve.utils.predictors import DjlLocalModePredictor +from sagemaker.serve.utils.predictors import DjlLocalModePredictor, InProcessModePredictor from tests.unit.sagemaker.serve.constants import MOCK_IMAGE_CONFIG, MOCK_VPC_CONFIG mock_model_id = "TheBloke/Llama-2-7b-chat-fp16" @@ -46,6 +46,8 @@ "OPTION_DTYPE": "bf16", "MODEL_LOADING_TIMEOUT": "1800", } +mock_inference_spec = MagicMock() +mock_inference_spec.get_model = "TheBloke/Llama-2-7b-chat-fp16" mock_schema_builder = MagicMock() mock_schema_builder.sample_input = mock_sample_input @@ -115,6 +117,59 @@ def test_build_deploy_for_djl_local_container( with self.assertRaises(ValueError) as _: model.deploy(mode=Mode.IN_PROCESS) + @patch("sagemaker.serve.builder.djl_builder._capture_telemetry", side_effect=None) + @patch( + "sagemaker.serve.builder.jumpstart_builder.JumpStart._is_jumpstart_model_id", + return_value=False, + ) + @patch("sagemaker.serve.builder.djl_builder._get_ram_usage_mb", return_value=1024) + @patch("sagemaker.serve.builder.djl_builder._get_nb_instance", return_value="ml.g5.24xlarge") + @patch( + "sagemaker.serve.builder.djl_builder._get_default_djl_configurations", + return_value=(mock_default_configs, 128), + ) + def test_build_deploy_for_djl_in_process( + self, + mock_default_djl_config, + mock_get_nb_instance, + mock_get_ram_usage_mb, + mock_is_jumpstart_model, + mock_telemetry, + ): + builder = ModelBuilder( + model=mock_model_id, + name="mock_model_name", + schema_builder=mock_schema_builder, + mode=Mode.IN_PROCESS, + model_server=ModelServer.DJL_SERVING, + image_config=MOCK_IMAGE_CONFIG, + vpc_config=MOCK_VPC_CONFIG, + ) + + builder._prepare_for_mode = MagicMock() + builder._prepare_for_mode.side_effect = None + + model = builder.build() + assert model.name == "mock_model_name" + + builder.serve_settings.telemetry_opt_out = True + + assert isinstance(model, DJLModel) + assert builder.schema_builder.sample_input["parameters"]["max_new_tokens"] == 128 + assert builder.nb_instance_type == "ml.g5.24xlarge" + assert model.image_config == MOCK_IMAGE_CONFIG + assert model.vpc_config == MOCK_VPC_CONFIG + assert "lmi" in builder.image_uri + + builder.modes[str(Mode.IN_PROCESS)] = MagicMock() + predictor = model.deploy(model_data_download_timeout=1800) + + assert builder.env_vars["MODEL_LOADING_TIMEOUT"] == "1800" + assert isinstance(predictor, InProcessModePredictor) + + builder._original_deploy = MagicMock() + builder._prepare_for_mode.return_value = (None, {}) + @patch("sagemaker.serve.builder.djl_builder._capture_telemetry", side_effect=None) @patch( "sagemaker.serve.builder.jumpstart_builder.JumpStart._is_jumpstart_model_id", diff --git a/tests/unit/sagemaker/serve/builder/test_model_builder.py b/tests/unit/sagemaker/serve/builder/test_model_builder.py index 91d804433c..7355fe4f38 100644 --- a/tests/unit/sagemaker/serve/builder/test_model_builder.py +++ b/tests/unit/sagemaker/serve/builder/test_model_builder.py @@ -4114,4 +4114,4 @@ def test_deploy(mock_unique_name_from_base, test_case): args, kwargs = model.deploy.call_args_list[0] diff = deepdiff.DeepDiff(kwargs, test_case["call_params"]) - assert diff == {} \ No newline at end of file + assert diff == {} diff --git a/tests/unit/sagemaker/serve/mode/test_in_process_mode.py b/tests/unit/sagemaker/serve/mode/test_in_process_mode.py index 0b1747029a..f6bbfb9b00 100644 --- a/tests/unit/sagemaker/serve/mode/test_in_process_mode.py +++ b/tests/unit/sagemaker/serve/mode/test_in_process_mode.py @@ -32,7 +32,7 @@ class TestInProcessMode(unittest.TestCase): @patch("sagemaker.serve.mode.in_process_mode.Path") @patch("sagemaker.serve.spec.inference_spec.InferenceSpec") @patch("sagemaker.session.Session") - def test_load_happy(self, mock_session, mock_inference_spec, mock_path): + def test_load_happy_transformers(self, mock_session, mock_inference_spec, mock_path): mock_path.return_value.exists.side_effect = lambda *args, **kwargs: True mock_path.return_value.is_dir.side_effect = lambda *args, **kwargs: True @@ -56,6 +56,33 @@ def test_load_happy(self, mock_session, mock_inference_spec, mock_path): self.assertEqual(in_process_mode.model_path, "model_path") self.assertEqual(in_process_mode.env_vars, {"key": "val"}) + @patch("sagemaker.serve.mode.in_process_mode.Path") + @patch("sagemaker.serve.spec.inference_spec.InferenceSpec") + @patch("sagemaker.session.Session") + def test_load_happy_djl_serving(self, mock_session, mock_inference_spec, mock_path): + mock_path.return_value.exists.side_effect = lambda *args, **kwargs: True + mock_path.return_value.is_dir.side_effect = lambda *args, **kwargs: True + + mock_inference_spec.load.side_effect = lambda *args, **kwargs: "Dummy load" + + mock_schema_builder = SchemaBuilder(mock_sample_input, mock_sample_output) + in_process_mode = InProcessMode( + model_server=ModelServer.DJL_SERVING, + inference_spec=mock_inference_spec, + schema_builder=mock_schema_builder, + session=mock_session, + model_path="model_path", + env_vars={"key": "val"}, + ) + + res = in_process_mode.load(model_path="/tmp/model-builder/code/") + + self.assertEqual(res, "Dummy load") + self.assertEqual(in_process_mode.inference_spec, mock_inference_spec) + self.assertEqual(in_process_mode.schema_builder, mock_schema_builder) + self.assertEqual(in_process_mode.model_path, "model_path") + self.assertEqual(in_process_mode.env_vars, {"key": "val"}) + @patch("sagemaker.serve.mode.in_process_mode.Path") @patch("sagemaker.serve.spec.inference_spec.InferenceSpec") @patch("sagemaker.session.Session") @@ -119,7 +146,7 @@ def test_create_server_happy( model_path="model_path", ) - in_process_mode._multi_model_server_deep_ping = mock_multi_model_server_deep_ping + in_process_mode._deep_ping = mock_multi_model_server_deep_ping in_process_mode._start_serving = mock_start_serving in_process_mode.create_server(predictor=mock_predictor) @@ -160,13 +187,13 @@ def test_create_server_ex( model_path="model_path", ) - in_process_mode._multi_model_server_deep_ping = mock_multi_model_server_deep_ping + in_process_mode._deep_ping = mock_multi_model_server_deep_ping in_process_mode._start_serving = mock_start_serving self.assertRaises(InProcessDeepPingException, in_process_mode.create_server, mock_predictor) @patch( - "sagemaker.serve.model_server.multi_model_server.server.InProcessMultiModelServer._stop_serving" + "sagemaker.serve.model_server.in_process_model_server.in_process_server.InProcessServing._stop_serving" ) @patch("sagemaker.serve.spec.inference_spec.InferenceSpec") @patch("sagemaker.session.Session") @@ -177,7 +204,7 @@ def test_destroy_server( mock_stop_serving, ): in_process_mode = InProcessMode( - model_server=ModelServer.MMS, + model_server=ModelServer.DJL_SERVING, inference_spec=mock_inference_spec, schema_builder=SchemaBuilder(mock_sample_input, mock_sample_output), session=mock_session, diff --git a/tests/unit/sagemaker/serve/test_app.py b/tests/unit/sagemaker/serve/model_server/in_process_model_server/test_app.py similarity index 60% rename from tests/unit/sagemaker/serve/test_app.py rename to tests/unit/sagemaker/serve/model_server/in_process_model_server/test_app.py index 5b9a2218bb..65ba80c370 100644 --- a/tests/unit/sagemaker/serve/test_app.py +++ b/tests/unit/sagemaker/serve/model_server/in_process_model_server/test_app.py @@ -16,7 +16,7 @@ import pytest from unittest.mock import patch, Mock -from sagemaker.serve.app import InProcessServer +from sagemaker.serve.model_server.in_process_model_server.app import InProcessServer from tests.integ.sagemaker.serve.constants import ( PYTHON_VERSION_IS_NOT_310, ) @@ -29,29 +29,29 @@ class TestAppInProcessServer(unittest.TestCase): PYTHON_VERSION_IS_NOT_310, reason="The goal of these tests are to test the serving components of our feature", ) - @patch("sagemaker.serve.app.threading") - @patch("sagemaker.serve.app.pipeline") - def test_in_process_server_init(self, mock_pipeline, mock_threading): + @patch("sagemaker.serve.model_server.in_process_model_server.app.threading") + @patch("sagemaker.serve.spec.inference_spec.InferenceSpec") + def test_in_process_server_init(self, mock_inference_spec, mock_threading): mock_generator = Mock() mock_generator.side_effect = None - in_process_server = InProcessServer(model_id=mock_model_id) + in_process_server = InProcessServer(inference_spec=mock_inference_spec) in_process_server._generator = mock_generator @pytest.mark.skipif( PYTHON_VERSION_IS_NOT_310, reason="The goal of these test are to test the serving components of our feature", ) - @patch("sagemaker.serve.app.logger") - @patch("sagemaker.serve.app.threading") - @patch("sagemaker.serve.app.pipeline") - def test_start_server(self, mock_pipeline, mock_threading, mock_logger): + @patch("sagemaker.serve.model_server.in_process_model_server.app.logger") + @patch("sagemaker.serve.model_server.in_process_model_server.app.threading") + @patch("sagemaker.serve.spec.inference_spec.InferenceSpec") + def test_start_server(self, mock_inference_spec, mock_threading, mock_logger): mock_generator = Mock() mock_generator.side_effect = None mock_thread = Mock() mock_threading.Thread.return_value = mock_thread - in_process_server = InProcessServer(model_id=mock_model_id) + in_process_server = InProcessServer(inference_spec=mock_inference_spec) in_process_server._generator = mock_generator in_process_server.start_server() @@ -63,27 +63,27 @@ def test_start_server(self, mock_pipeline, mock_threading, mock_logger): PYTHON_VERSION_IS_NOT_310, reason="The goal of these test are to test the serving components of our feature", ) - @patch("sagemaker.serve.app.asyncio") - @patch("sagemaker.serve.app.pipeline") - def test_start_run_async_in_thread(self, mock_pipeline, mock_asyncio): - mock_pipeline.side_effect = None + @patch("sagemaker.serve.model_server.in_process_model_server.app.asyncio") + @patch("sagemaker.serve.spec.inference_spec.InferenceSpec") + def test_start_run_async_in_thread(self, mock_inference_spec, mock_asyncio): + mock_inference_spec.load.side_effect = lambda *args, **kwargs: "Dummy load" mock_loop = Mock() mock_asyncio.new_event_loop.side_effect = lambda: mock_loop - in_process_server = InProcessServer(model_id=mock_model_id) + in_process_server = InProcessServer(inference_spec=mock_inference_spec) in_process_server._start_run_async_in_thread() mock_asyncio.set_event_loop.assert_called_once_with(mock_loop) mock_loop.run_until_complete.assert_called() - @patch("sagemaker.serve.app.pipeline") - async def test_serve(self, mock_pipeline): - mock_pipeline.side_effect = None + @patch("sagemaker.serve.spec.inference_spec.InferenceSpec") + async def test_serve(self, mock_inference_spec): + mock_inference_spec.load.side_effect = lambda *args, **kwargs: "Dummy load" mock_server = Mock() - in_process_server = InProcessServer(model_id=mock_model_id) + in_process_server = InProcessServer(inference_spec=mock_inference_spec) in_process_server.server = mock_server await in_process_server._serve() From 24b0dc0c3714660e19bfdea0adfa885de86f2bd8 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Mon, 11 Nov 2024 16:45:03 -0800 Subject: [PATCH 23/79] Pass hyperparameters as CLI args (#1577) --- src/sagemaker/modules/__init__.py | 4 - src/sagemaker/modules/configs.py | 4 +- src/sagemaker/modules/distributed.py | 4 +- src/sagemaker/modules/templates.py | 7 +- .../base_model_trainer.ipynb | 10 +- .../container_drivers/basic_script_driver.py | 79 ++++++++++++ .../train/container_drivers/mpi_driver.py | 8 +- .../train/container_drivers/mpi_utils.py | 15 ++- .../container_drivers/scripts/environment.py | 12 +- .../container_drivers/torchrun_driver.py | 13 +- .../modules/train/container_drivers/utils.py | 83 +++++++++++- src/sagemaker/modules/train/model_trainer.py | 58 +++++---- src/sagemaker/modules/utils.py | 28 +++- tests/data/modules/params-script/train.py | 104 +++++++++++++++ tests/data/modules/params-script/train.sh | 11 ++ .../modules/train/test_model_trainer.py | 104 +++++++++++++++ .../scripts/test_enviornment.py | 7 +- .../container_drivers/test_mpi_driver.py | 6 + .../container_drivers/test_torchrun_driver.py | 10 ++ .../train/container_drivers/test_utils.py | 121 ++++++++++++++++++ .../modules/train/test_model_trainer.py | 79 +++++++----- 21 files changed, 672 insertions(+), 95 deletions(-) create mode 100644 src/sagemaker/modules/train/container_drivers/basic_script_driver.py create mode 100644 tests/data/modules/params-script/train.py create mode 100644 tests/data/modules/params-script/train.sh create mode 100644 tests/integ/sagemaker/modules/train/test_model_trainer.py create mode 100644 tests/unit/sagemaker/modules/train/container_drivers/test_utils.py diff --git a/src/sagemaker/modules/__init__.py b/src/sagemaker/modules/__init__.py index 9a25d10e4e..67758a7af1 100644 --- a/src/sagemaker/modules/__init__.py +++ b/src/sagemaker/modules/__init__.py @@ -16,7 +16,3 @@ from sagemaker_core.main.utils import logger as sagemaker_core_logger logger = sagemaker_core_logger - -from sagemaker.modules.train.model_trainer import ( # noqa: F401 E402 # pylint: disable=C0413 - ModelTrainer, -) diff --git a/src/sagemaker/modules/configs.py b/src/sagemaker/modules/configs.py index 75f06797bd..bb84f2d780 100644 --- a/src/sagemaker/modules/configs.py +++ b/src/sagemaker/modules/configs.py @@ -123,9 +123,9 @@ class Compute(shapes.ResourceConfig): Attributes: instance_type (Optional[str]): The ML compute instance type. For information about available instance types, - see https://aws.amazon.com/sagemaker/pricing/. Default: ml.m5.xlarge + see https://aws.amazon.com/sagemaker/pricing/. instance_count (Optional[int]): The number of ML compute instances to use. For distributed - training, provide a value greater than 1. Default: 1 + training, provide a value greater than 1. volume_size_in_gb (Optional[int]): The size of the ML storage volume that you want to provision. ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML diff --git a/src/sagemaker/modules/distributed.py b/src/sagemaker/modules/distributed.py index b6d6967833..32ea7e81a6 100644 --- a/src/sagemaker/modules/distributed.py +++ b/src/sagemaker/modules/distributed.py @@ -30,9 +30,9 @@ def model_dump(self, *args, **kwargs): class Torchrun(DistributedRunner): - """TorchDistribution. + """TorchDistributed. - The TorchDistribution runner uses `torchrun` or `torch.distributed.launch` in the backend to + The Torchrun distributed runner uses `torchrun` or `torch.distributed.launch` in the backend to launch distributed training. Attributes: diff --git a/src/sagemaker/modules/templates.py b/src/sagemaker/modules/templates.py index 1c2e65ff8c..0a8f6d1f12 100644 --- a/src/sagemaker/modules/templates.py +++ b/src/sagemaker/modules/templates.py @@ -15,10 +15,15 @@ EXECUTE_BASE_COMMANDS = """ CMD="{base_command}" -echo "Running command: $CMD" +echo "Executing command: $CMD" eval $CMD """ +EXECUTE_BASIC_SCRIPT_DRIVER = """ +echo "Running Basic Script driver" +$SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/basic_script_driver.py +""" + EXEUCTE_TORCHRUN_DRIVER = """ echo "Running Torchrun driver" $SM_PYTHON_CMD /opt/ml/input/data/sm_drivers/torchrun_driver.py diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index c839566dc3..e674a4e041 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -117,8 +117,11 @@ "metadata": {}, "outputs": [], "source": [ + "from sagemaker.modules.train import ModelTrainer\n", "from sagemaker.modules.configs import SourceCode\n", "\n", + "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", + "\n", "source_code = SourceCode(\n", " source_dir=\"basic-script-mode\",\n", " requirements=\"requirements.txt\",\n", @@ -460,13 +463,6 @@ ")\n", "model_trainer.train(input_data_config=[test_data], wait=False)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/src/sagemaker/modules/train/container_drivers/basic_script_driver.py b/src/sagemaker/modules/train/container_drivers/basic_script_driver.py new file mode 100644 index 0000000000..cb0278bc9f --- /dev/null +++ b/src/sagemaker/modules/train/container_drivers/basic_script_driver.py @@ -0,0 +1,79 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module is the entry point for the Basic Script Driver.""" +from __future__ import absolute_import + +import sys +import shlex + +from typing import List + +from utils import ( + logger, + get_python_executable, + read_source_code_json, + read_hyperparameters_json, + execute_commands, + write_failure_file, + hyperparameters_to_cli_args, +) + + +def create_commands() -> List[str]: + """Create the commands to execute.""" + source_code = read_source_code_json() + hyperparameters = read_hyperparameters_json() + python_executable = get_python_executable() + + entry_script = source_code["entry_script"] + args = hyperparameters_to_cli_args(hyperparameters) + if entry_script.endswith(".py"): + commands = [python_executable, entry_script] + commands += args + elif entry_script.endswith(".sh"): + args_str = " ".join(shlex.quote(arg) for arg in args) + commands = [ + "/bin/sh", + "-c", + f"chmod +x {entry_script} && ./{entry_script} {args_str}", + ] + else: + raise ValueError( + f"Unsupported entry script type: {entry_script}. Only .py and .sh are supported." + ) + return commands + + +def main(): + """Main function for the Basic Script Driver. + + This function is the entry point for the Basic Script Driver. + + Execution Lifecycle: + 1. Read the source code and hyperparameters JSON files. + 2. Set hyperparameters as command line arguments. + 3. Create the commands to execute. + 4. Execute the commands. + """ + + cmd = create_commands() + + logger.info(f"Executing command: {' '.join(cmd)}") + exit_code, traceback = execute_commands(cmd) + if exit_code != 0: + write_failure_file(traceback) + sys.exit(exit_code) + + +if __name__ == "__main__": + main() diff --git a/src/sagemaker/modules/train/container_drivers/mpi_driver.py b/src/sagemaker/modules/train/container_drivers/mpi_driver.py index 90c13df8d5..b0e501fd9e 100644 --- a/src/sagemaker/modules/train/container_drivers/mpi_driver.py +++ b/src/sagemaker/modules/train/container_drivers/mpi_driver.py @@ -21,6 +21,8 @@ logger, read_source_code_json, read_distributed_runner_json, + read_hyperparameters_json, + hyperparameters_to_cli_args, get_process_count, execute_commands, write_failure_file, @@ -58,6 +60,7 @@ def main(): """ source_code = read_source_code_json() distribution = read_distributed_runner_json() + hyperparameters = read_hyperparameters_json() sm_current_host = os.environ["SM_CURRENT_HOST"] sm_hosts = json.loads(os.environ["SM_HOSTS"]) @@ -87,7 +90,10 @@ def main(): entry_script_path=os.path.join(USER_CODE_PATH, source_code["entry_script"]), ) - logger.info(f"Executing command: {mpi_command}") + args = hyperparameters_to_cli_args(hyperparameters) + mpi_command += args + + logger.info(f"Executing command: {' '.join(mpi_command)}") exit_code, error_traceback = execute_commands(mpi_command) write_status_file_to_workers(worker_hosts) diff --git a/src/sagemaker/modules/train/container_drivers/mpi_utils.py b/src/sagemaker/modules/train/container_drivers/mpi_utils.py index 0fcfa265a3..a2d4fcfc87 100644 --- a/src/sagemaker/modules/train/container_drivers/mpi_utils.py +++ b/src/sagemaker/modules/train/container_drivers/mpi_utils.py @@ -22,13 +22,14 @@ from utils import logger, SM_EFA_NCCL_INSTANCES, SM_EFA_RDMA_INSTANCES, get_python_executable FINISHED_STATUS_FILE = "/tmp/done.algo-1" +READY_FILE = "/tmp/ready.%s" DEFAULT_SSH_PORT = 22 -def _write_status_file(host: str, status_file: str) -> bool: - """Write the status file to the provided host.""" +def _write_file_to_host(host: str, status_file: str) -> bool: + """Write the a file to the provided host.""" try: - logger.info("Writing finished status file (%s) to %s", status_file, host) + logger.info(f"Writing {status_file} to {host}") subprocess.run( ["ssh", host, "touch", f"{status_file}"], capture_output=True, @@ -46,7 +47,7 @@ def write_status_file_to_workers(worker_hosts: List[str], status_file: str = FIN """Write the status file to all worker nodes.""" for worker in worker_hosts: retry = 0 - while not _write_status_file(worker, status_file): + while not _write_file_to_host(worker, status_file): time.sleep(5) retry += 1 if retry > 5: @@ -102,7 +103,10 @@ def _wait_for_workers(worker_hosts: List[str], port: int = DEFAULT_SSH_PORT, tim while True: logger.info("Master is attempting to connect to all workers...") - all_workers_connected = all(_can_connect(worker, port) for worker in worker_hosts) + all_workers_connected = all( + _can_connect(worker, port) and os.path.exists(READY_FILE % worker) + for worker in worker_hosts + ) if all_workers_connected: logger.info("Master can connect to all worker nodes.") @@ -131,6 +135,7 @@ def bootstrap_worker_node(master_host: str, status_file: str = FINISHED_STATUS_F """Bootstrap the worker nodes.""" logger.info("Bootstrapping worker node...") _wait_for_master(master_host) + _write_file_to_host(master_host, READY_FILE % os.environ["SM_CURRENT_HOST"]) _wait_for_status_file(status_file) diff --git a/src/sagemaker/modules/train/container_drivers/scripts/environment.py b/src/sagemaker/modules/train/container_drivers/scripts/environment.py index 163b0ea8b4..b02833b5af 100644 --- a/src/sagemaker/modules/train/container_drivers/scripts/environment.py +++ b/src/sagemaker/modules/train/container_drivers/scripts/environment.py @@ -21,6 +21,11 @@ import sys import logging +parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) +sys.path.insert(0, parent_dir) + +from utils import safe_serialize # noqa: E402 # pylint: disable=C0413 + # Initialize logger SM_LOG_LEVEL = os.environ.get("SM_LOG_LEVEL", 20) logger = logging.getLogger(__name__) @@ -147,7 +152,7 @@ def set_env( # Hyperparameters env_vars["SM_HPS"] = hyperparameters_config for key, value in hyperparameters_config.items(): - env_vars[f"SM_HP_{key.upper()}"] = value + env_vars[f"SM_HP_{key.upper()}"] = safe_serialize(value) # Host Variables current_host = resource_config["current_host"] @@ -197,10 +202,7 @@ def set_env( } with open(output_file, "w") as f: for key, value in env_vars.items(): - if isinstance(value, (list, dict)): - f.write(f"export {key}='{json.dumps(value)}'\n") - else: - f.write(f"export {key}='{value}'\n") + f.write(f"export {key}='{safe_serialize(value)}'\n") logger.info("Environment Variables:") log_env_variables(env_vars_dict=env_vars) diff --git a/src/sagemaker/modules/train/container_drivers/torchrun_driver.py b/src/sagemaker/modules/train/container_drivers/torchrun_driver.py index 799f56ca93..95548d35be 100644 --- a/src/sagemaker/modules/train/container_drivers/torchrun_driver.py +++ b/src/sagemaker/modules/train/container_drivers/torchrun_driver.py @@ -22,13 +22,15 @@ logger, read_source_code_json, read_distributed_runner_json, + read_hyperparameters_json, + hyperparameters_to_cli_args, get_process_count, get_python_executable, - SM_EFA_NCCL_INSTANCES, - SM_EFA_RDMA_INSTANCES, execute_commands, write_failure_file, USER_CODE_PATH, + SM_EFA_NCCL_INSTANCES, + SM_EFA_RDMA_INSTANCES, ) @@ -65,6 +67,7 @@ def create_commands(): """Create the Torch Distributed command to execute""" source_code = read_source_code_json() distribution = read_distributed_runner_json() + hyperparameters = read_hyperparameters_json() process_count = get_process_count(distribution) host_count = int(os.environ["SM_HOST_COUNT"]) @@ -92,6 +95,10 @@ def create_commands(): ) torch_cmd.extend([os.path.join(USER_CODE_PATH, source_code["entry_script"])]) + + args = hyperparameters_to_cli_args(hyperparameters) + torch_cmd += args + return torch_cmd @@ -110,7 +117,7 @@ def main(): """ setup_env() torch_cmd = create_commands() - logger.info(f"Executing command: {torch_cmd}") + logger.info(f"Executing command: {' '.join(torch_cmd)}") exit_code, traceback = execute_commands(torch_cmd) if exit_code != 0: write_failure_file(traceback) diff --git a/src/sagemaker/modules/train/container_drivers/utils.py b/src/sagemaker/modules/train/container_drivers/utils.py index 42745b70ff..93e3e4dc03 100644 --- a/src/sagemaker/modules/train/container_drivers/utils.py +++ b/src/sagemaker/modules/train/container_drivers/utils.py @@ -20,7 +20,7 @@ import traceback import json -from typing import List, Dict, Any, Tuple, IO +from typing import List, Dict, Any, Tuple, IO, Optional # Initialize logger SM_LOG_LEVEL = os.environ.get("SM_LOG_LEVEL", 20) @@ -30,16 +30,17 @@ logger.setLevel(int(SM_LOG_LEVEL)) FAILURE_FILE = "/opt/ml/output/failure" -DEFAULT_FAILURE_MESSAGE = f""" +DEFAULT_FAILURE_MESSAGE = """ Training Execution failed. For more details, see CloudWatch logs at 'aws/sagemaker/TrainingJobs'. -TrainingJob - {os.environ['TRAINING_JOB_NAME']} +TrainingJob - {training_job_name} """ USER_CODE_PATH = "/opt/ml/input/data/sm_code" SOURCE_CODE_JSON = "/opt/ml/input/data/sm_drivers/sourcecode.json" DISTRIBUTED_RUNNER_JSON = "/opt/ml/input/data/sm_drivers/distributed_runner.json" +HYPERPARAMETERS_JSON = "/opt/ml/input/config/hyperparameters.json" SM_EFA_NCCL_INSTANCES = [ "ml.g4dn.8xlarge", @@ -59,8 +60,10 @@ ] -def write_failure_file(message: str = DEFAULT_FAILURE_MESSAGE): +def write_failure_file(message: Optional[str] = None): """Write a failure file with the message.""" + if message is None: + message = DEFAULT_FAILURE_MESSAGE.format(training_job_name=os.environ["TRAINING_JOB_NAME"]) if not os.path.exists(FAILURE_FILE): with open(FAILURE_FILE, "w") as f: f.write(message) @@ -86,6 +89,16 @@ def read_distributed_runner_json(distributed_json: Dict[str, Any] = DISTRIBUTED_ return distributed_runner_dict +def read_hyperparameters_json(hyperparameters_json: Dict[str, Any] = HYPERPARAMETERS_JSON): + """Read the hyperparameters config json file.""" + try: + with open(hyperparameters_json, "r") as f: + hyperparameters_dict = json.load(f) or {} + except FileNotFoundError: + hyperparameters_dict = {} + return hyperparameters_dict + + def get_process_count(distributed_runner_dict: Dict[str, Any]) -> int: """Get the number of processes to run on each node in the training job.""" return ( @@ -96,6 +109,68 @@ def get_process_count(distributed_runner_dict: Dict[str, Any]) -> int: ) +def hyperparameters_to_cli_args(hyperparameters: Dict[str, Any]) -> List[str]: + """Convert the hyperparameters to CLI arguments.""" + cli_args = [] + for key, value in hyperparameters.items(): + value = safe_deserialize(value) + cli_args.extend([f"--{key}", safe_serialize(value)]) + + return cli_args + + +def safe_deserialize(data: Any) -> Any: + """Safely deserialize data from a JSON string. + + This function handles the following cases: + 1. If `data` is not a string, it returns the input as-is. + 2. If `data` is a string and matches common boolean values ("true" or "false"), + it returns the corresponding boolean value (True or False). + 3. If `data` is a JSON-encoded string, it attempts to deserialize it using `json.loads()`. + 4. If `data` is a string but cannot be decoded as JSON, it returns the original string. + + Returns: + Any: The deserialized data, or the original input if it cannot be JSON-decoded. + """ + if not isinstance(data, str): + return data + + lower_data = data.lower() + if lower_data in ["true"]: + return True + if lower_data in ["false"]: + return False + + try: + return json.loads(data) + except json.JSONDecodeError: + return data + + +def safe_serialize(data): + """Serialize the data without wrapping strings in quotes. + + This function handles the following cases: + 1. If `data` is a string, it returns the string as-is without wrapping in quotes. + 2. If `data` is serializable (e.g., a dictionary, list, int, float), it returns + the JSON-encoded string using `json.dumps()`. + 3. If `data` cannot be serialized (e.g., a custom object), it returns the string + representation of the data using `str(data)`. + + Args: + data (Any): The data to serialize. + + Returns: + str: The serialized JSON-compatible string or the string representation of the input. + """ + if isinstance(data, str): + return data + try: + return json.dumps(data) + except TypeError: + return str(data) + + def get_python_executable() -> str: """Get the python executable path.""" return sys.executable diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 7b9abf23c1..efd5011055 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -15,6 +15,8 @@ import os import json +import shutil +from tempfile import TemporaryDirectory from typing import Optional, List, Union, Dict, Any from pydantic import BaseModel, ConfigDict, PrivateAttr, validate_call @@ -58,6 +60,7 @@ _get_unique_name, _is_valid_path, _is_valid_s3_uri, + safe_serialize, ) from sagemaker.modules.types import DataSourceType from sagemaker.modules.constants import ( @@ -77,6 +80,7 @@ EXECUTE_BASE_COMMANDS, EXECUTE_MPI_DRIVER, EXEUCTE_TORCHRUN_DRIVER, + EXECUTE_BASIC_SCRIPT_DRIVER, ) from sagemaker.modules import logger @@ -350,12 +354,15 @@ def train( string_hyper_parameters = {} if self.hyperparameters: for hyper_parameter, value in self.hyperparameters.items(): - string_hyper_parameters[hyper_parameter] = str(value) + string_hyper_parameters[hyper_parameter] = safe_serialize(value) container_entrypoint = None container_arguments = None if self.source_code: + drivers_dir = TemporaryDirectory() + shutil.copytree(SM_DRIVERS_LOCAL_PATH, drivers_dir.name, dirs_exist_ok=True) + # If source code is provided, create a channel for the source code # The source code will be mounted at /opt/ml/input/data/sm_code in the container if self.source_code.source_dir: @@ -365,17 +372,22 @@ def train( input_data_config.append(source_code_channel) self._prepare_train_script( - source_code=self.source_code, distributed_runner=self.distributed_runner + tmp_dir=drivers_dir, + source_code=self.source_code, + distributed_runner=self.distributed_runner, ) + if isinstance(self.distributed_runner, TorchrunSMP): mp_parameters = self.distributed_runner._to_mp_parameters_dict() - string_hyper_parameters["mp_parameters"] = json.dumps(mp_parameters) + string_hyper_parameters["mp_parameters"] = safe_serialize(mp_parameters) - self._write_source_code_json(self.source_code) - self._write_distributed_runner_json(self.distributed_runner) + self._write_source_code_json(tmp_dir=drivers_dir, source_code=self.source_code) + self._write_distributed_runner_json( + tmp_dir=drivers_dir, distributed_runner=self.distributed_runner + ) # Create an input channel for drivers packaged by the sdk - sm_drivers_channel = self.create_input_data_channel(SM_DRIVERS, SM_DRIVERS_LOCAL_PATH) + sm_drivers_channel = self.create_input_data_channel(SM_DRIVERS, drivers_dir.name) input_data_config.append(sm_drivers_channel) # If source_code is provided, we will always use @@ -525,24 +537,27 @@ def _get_input_data_config( ) return channels - def _write_source_code_json(self, source_code: SourceCode): + def _write_source_code_json(self, tmp_dir: TemporaryDirectory, source_code: SourceCode): """Write the source code configuration to a JSON file.""" - file_path = os.path.join(SM_DRIVERS_LOCAL_PATH, SOURCE_CODE_JSON) + file_path = os.path.join(tmp_dir.name, SOURCE_CODE_JSON) with open(file_path, "w") as f: dump = source_code.model_dump(exclude_none=True) if source_code else {} f.write(json.dumps(dump)) def _write_distributed_runner_json( - self, distributed_runner: Optional[DistributedRunner] = None + self, + tmp_dir: TemporaryDirectory, + distributed_runner: Optional[DistributedRunner] = None, ): """Write the distributed runner configuration to a JSON file.""" - file_path = os.path.join(SM_DRIVERS_LOCAL_PATH, DISTRIBUTED_RUNNER_JSON) + file_path = os.path.join(tmp_dir.name, DISTRIBUTED_RUNNER_JSON) with open(file_path, "w") as f: dump = distributed_runner.model_dump(exclude_none=True) if distributed_runner else {} f.write(json.dumps(dump)) def _prepare_train_script( self, + tmp_dir: TemporaryDirectory, source_code: SourceCode, distributed_runner: Optional[DistributedRunner] = None, ): @@ -562,19 +577,6 @@ def _prepare_train_script( base_command = source_code.command.split() base_command = " ".join(base_command) - if source_code.entry_script and not source_code.command and not distributed_runner: - if source_code.entry_script.endswith(".py"): - base_command = f"$SM_PYTHON_CMD {source_code.entry_script}" - elif source_code.entry_script.endswith(".sh"): - base_command = ( - f"chmod +x {source_code.entry_script} && " f"bash {source_code.entry_script}" - ) - else: - raise ValueError( - f"Unsupported entry script: {source_code.entry_script}." - + "Only .py and .sh scripts are supported." - ) - install_requirements = "" if source_code.requirements: install_requirements = "echo 'Installing requirements'\n" @@ -594,6 +596,13 @@ def _prepare_train_script( execute_driver = EXEUCTE_TORCHRUN_DRIVER else: raise ValueError(f"Unsupported distribution type: {distribution_type}.") + elif source_code.entry_script and not source_code.command and not distributed_runner: + if not source_code.entry_script.endswith((".py", ".sh")): + raise ValueError( + f"Unsupported entry script: {source_code.entry_script}." + + "Only .py and .sh scripts are supported." + ) + execute_driver = EXECUTE_BASIC_SCRIPT_DRIVER train_script = TRAIN_SCRIPT_TEMPLATE.format( working_dir=working_dir, @@ -601,8 +610,7 @@ def _prepare_train_script( execute_driver=execute_driver, ) - os.makedirs(SM_DRIVERS_LOCAL_PATH, exist_ok=True) - with open(os.path.join(SM_DRIVERS_LOCAL_PATH, TRAIN_SCRIPT), "w") as f: + with open(os.path.join(tmp_dir.name, TRAIN_SCRIPT), "w") as f: f.write(train_script) def with_metric_settings( diff --git a/src/sagemaker/modules/utils.py b/src/sagemaker/modules/utils.py index b401d3e414..133685db63 100644 --- a/src/sagemaker/modules/utils.py +++ b/src/sagemaker/modules/utils.py @@ -13,8 +13,8 @@ """Utils module.""" from __future__ import absolute_import -import re import os +import json from datetime import datetime from typing import Literal, Any @@ -87,8 +87,8 @@ def _get_unique_name(base, max_length=63): str: The unique name """ current_time = datetime.now().strftime("%Y%m%d%H%M%S") + base = base.replace("_", "-") unique_name = f"{base}-{current_time}" - unique_name = re.sub(r"[^a-zA-Z0-9-]", "", unique_name) # Remove invalid characters unique_name = unique_name[:max_length] # Truncate to max_length return unique_name @@ -117,3 +117,27 @@ def convert_unassigned_to_none(instance) -> Any: if isinstance(value, Unassigned): setattr(instance, name, None) return instance + + +def safe_serialize(data): + """Serialize the data without wrapping strings in quotes. + + This function handles the following cases: + 1. If `data` is a string, it returns the string as-is without wrapping in quotes. + 2. If `data` is serializable (e.g., a dictionary, list, int, float), it returns + the JSON-encoded string using `json.dumps()`. + 3. If `data` cannot be serialized (e.g., a custom object), it returns the string + representation of the data using `str(data)`. + + Args: + data (Any): The data to serialize. + + Returns: + str: The serialized JSON-compatible string or the string representation of the input. + """ + if isinstance(data, str): + return data + try: + return json.dumps(data) + except TypeError: + return str(data) diff --git a/tests/data/modules/params-script/train.py b/tests/data/modules/params-script/train.py new file mode 100644 index 0000000000..742684802d --- /dev/null +++ b/tests/data/modules/params-script/train.py @@ -0,0 +1,104 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Script to test hyperparameters contract.""" +from __future__ import absolute_import + +import argparse +import json +import os + +EXPECTED_HYPERPARAMETERS = { + "integer": 1, + "boolean": True, + "float": 3.14, + "string": "Hello World", + "list": [1, 2, 3], + "dict": { + "string": "value", + "integer": 3, + "list": [1, 2, 3], + "dict": {"key": "value"}, + "boolean": True, + }, +} + + +def parse_args(): + parser = argparse.ArgumentParser(description="Test Hyperparameters") + parser.add_argument( + "--string", + type=str, + default=None, + required=True, + ) + parser.add_argument( + "--integer", + type=int, + default=None, + required=True, + ) + parser.add_argument( + "--float", + type=float, + default=None, + required=True, + ) + parser.add_argument( + "--boolean", + type=lambda x: json.loads(x), + default=None, + required=True, + ) + parser.add_argument( + "--list", + type=lambda x: json.loads(x), + default=None, + required=True, + ) + parser.add_argument( + "--dict", + type=lambda x: json.loads(x), + default=None, + required=True, + ) + return parser.parse_args() + + +def main(): + args = parse_args() + print(args) + + assert isinstance(args.string, str) + assert isinstance(args.integer, int) + assert isinstance(args.boolean, bool) + assert isinstance(args.float, float) + assert isinstance(args.list, list) + assert isinstance(args.dict, dict) + + assert args.string == EXPECTED_HYPERPARAMETERS["string"] + assert args.integer == EXPECTED_HYPERPARAMETERS["integer"] + assert args.boolean == EXPECTED_HYPERPARAMETERS["boolean"] + assert args.float == EXPECTED_HYPERPARAMETERS["float"] + assert args.list == EXPECTED_HYPERPARAMETERS["list"] + assert args.dict == EXPECTED_HYPERPARAMETERS["dict"] + + assert os.environ["SM_HP_STRING"] == EXPECTED_HYPERPARAMETERS["string"] + assert int(os.environ["SM_HP_INTEGER"]) == EXPECTED_HYPERPARAMETERS["integer"] + assert float(os.environ["SM_HP_FLOAT"]) == EXPECTED_HYPERPARAMETERS["float"] + assert json.loads(os.environ["SM_HP_BOOLEAN"]) == EXPECTED_HYPERPARAMETERS["boolean"] + assert json.loads(os.environ["SM_HP_LIST"]) == EXPECTED_HYPERPARAMETERS["list"] + assert json.loads(os.environ["SM_HP_DICT"]) == EXPECTED_HYPERPARAMETERS["dict"] + + +if __name__ == "__main__": + main() diff --git a/tests/data/modules/params-script/train.sh b/tests/data/modules/params-script/train.sh new file mode 100644 index 0000000000..20f9a3c57a --- /dev/null +++ b/tests/data/modules/params-script/train.sh @@ -0,0 +1,11 @@ +#!/bin/bash +set -e + +echo "Do some extra work here..." + +CMD="python train.py $@" +echo "Executing Command: $CMD" + +python train.py "$@" + +echo "Done!" diff --git a/tests/integ/sagemaker/modules/train/test_model_trainer.py b/tests/integ/sagemaker/modules/train/test_model_trainer.py new file mode 100644 index 0000000000..a4f02cd77c --- /dev/null +++ b/tests/integ/sagemaker/modules/train/test_model_trainer.py @@ -0,0 +1,104 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module contains code to test image builder""" +from __future__ import absolute_import + +from tests.integ import DATA_DIR + +from sagemaker.modules.train import ModelTrainer +from sagemaker.modules.configs import SourceCode, Compute +from sagemaker.modules.distributed import MPI, Torchrun + +EXPECTED_HYPERPARAMETERS = { + "integer": 1, + "boolean": True, + "float": 3.14, + "string": "Hello World", + "list": [1, 2, 3], + "dict": { + "string": "value", + "integer": 3, + "list": [1, 2, 3], + "dict": {"key": "value"}, + "boolean": True, + }, +} + +DEFAULT_CPU_IMAGE = "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310" + + +def test_hp_contract_basic_py_script(): + source_code = SourceCode( + source_dir=f"{DATA_DIR}/modules/params-script", + entry_script="train.py", + ) + + model_trainer = ModelTrainer( + training_image=DEFAULT_CPU_IMAGE, + hyperparameters=EXPECTED_HYPERPARAMETERS, + source_code=source_code, + base_job_name="hp-contract-basic-py-script", + ) + + model_trainer.train() + + +def test_hp_contract_basic_sh_script(): + source_code = SourceCode( + source_dir=f"{DATA_DIR}/modules/params-script", + entry_script="train.sh", + ) + model_trainer = ModelTrainer( + training_image=DEFAULT_CPU_IMAGE, + hyperparameters=EXPECTED_HYPERPARAMETERS, + source_code=source_code, + base_job_name="hp-contract-basic-sh-script", + ) + + model_trainer.train() + + +def test_hp_contract_mpi_script(): + source_code = SourceCode( + source_dir=f"{DATA_DIR}/modules/params-script", + entry_script="train.py", + ) + compute = Compute(instance_type="ml.m5.xlarge", instance_count=2) + model_trainer = ModelTrainer( + training_image=DEFAULT_CPU_IMAGE, + compute=compute, + hyperparameters=EXPECTED_HYPERPARAMETERS, + source_code=source_code, + distributed_runner=MPI(), + base_job_name="hp-contract-mpi-script", + ) + + model_trainer.train() + + +def test_hp_contract_torchrun_script(): + source_code = SourceCode( + source_dir=f"{DATA_DIR}/modules/params-script", + entry_script="train.py", + ) + compute = Compute(instance_type="ml.m5.xlarge", instance_count=2) + model_trainer = ModelTrainer( + training_image=DEFAULT_CPU_IMAGE, + compute=compute, + hyperparameters=EXPECTED_HYPERPARAMETERS, + source_code=source_code, + distributed_runner=Torchrun(), + base_job_name="hp-contract-torchrun-script", + ) + + model_trainer.train() diff --git a/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py b/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py index 6d7c40f23a..938a9b05a3 100644 --- a/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py +++ b/tests/unit/sagemaker/modules/train/container_drivers/scripts/test_enviornment.py @@ -26,6 +26,7 @@ mask_sensitive_info, HIDDEN_VALUE, ) +from sagemaker.modules.train.container_drivers.utils import safe_serialize RESOURCE_CONFIG = dict( current_host="algo-1", @@ -114,7 +115,11 @@ @patch("sagemaker.modules.train.container_drivers.scripts.environment.num_cpus", return_value=8) @patch("sagemaker.modules.train.container_drivers.scripts.environment.num_gpus", return_value=0) @patch("sagemaker.modules.train.container_drivers.scripts.environment.num_neurons", return_value=0) -def test_set_env(mock_num_cpus, mock_num_gpus, mock_num_neurons): +@patch( + "sagemaker.modules.train.container_drivers.scripts.environment.safe_serialize", + side_effect=safe_serialize, +) +def test_set_env(mock_safe_serialize, mock_num_cpus, mock_num_gpus, mock_num_neurons): with patch.dict(os.environ, {"TRAINING_JOB_NAME": "test-job"}): set_env( resource_config=RESOURCE_CONFIG, diff --git a/tests/unit/sagemaker/modules/train/container_drivers/test_mpi_driver.py b/tests/unit/sagemaker/modules/train/container_drivers/test_mpi_driver.py index 02d88c4d4e..9c9fc8feb7 100644 --- a/tests/unit/sagemaker/modules/train/container_drivers/test_mpi_driver.py +++ b/tests/unit/sagemaker/modules/train/container_drivers/test_mpi_driver.py @@ -70,11 +70,13 @@ @patch("sagemaker.modules.train.container_drivers.mpi_driver.start_sshd_daemon") @patch("sagemaker.modules.train.container_drivers.mpi_driver.bootstrap_master_node") @patch("sagemaker.modules.train.container_drivers.mpi_driver.bootstrap_worker_node") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.hyperparameters_to_cli_args") @patch("sagemaker.modules.train.container_drivers.mpi_driver.get_mpirun_command") @patch("sagemaker.modules.train.container_drivers.mpi_driver.execute_commands") def test_mpi_driver_worker( mock_execute_commands, mock_get_mpirun_command, + mock_hyperparameters_to_cli_args, mock_bootstrap_worker_node, mock_bootstrap_master_node, mock_start_sshd_daemon, @@ -82,6 +84,7 @@ def test_mpi_driver_worker( mock_read_source_code_json, mock_read_distributed_runner_json, ): + mock_hyperparameters_to_cli_args.return_value = [] mock_read_source_code_json.return_value = DUMMY_SOURCE_CODE mock_read_distributed_runner_json.return_value = DUMMY_DISTRIBUTED_RUNNER @@ -112,6 +115,7 @@ def test_mpi_driver_worker( @patch("sagemaker.modules.train.container_drivers.mpi_driver.bootstrap_master_node") @patch("sagemaker.modules.train.container_drivers.mpi_driver.bootstrap_worker_node") @patch("sagemaker.modules.train.container_drivers.mpi_driver.get_process_count") +@patch("sagemaker.modules.train.container_drivers.mpi_driver.hyperparameters_to_cli_args") @patch("sagemaker.modules.train.container_drivers.mpi_driver.get_mpirun_command") @patch("sagemaker.modules.train.container_drivers.mpi_driver.execute_commands") @patch("sagemaker.modules.train.container_drivers.mpi_driver.write_status_file_to_workers") @@ -119,6 +123,7 @@ def test_mpi_driver_master( mock_write_status_file_to_workers, mock_execute_commands, mock_get_mpirun_command, + mock_hyperparameters_to_cli_args, mock_get_process_count, mock_bootstrap_worker_node, mock_bootstrap_master_node, @@ -127,6 +132,7 @@ def test_mpi_driver_master( mock_read_source_code_config_json, mock_read_distributed_runner_json, ): + mock_hyperparameters_to_cli_args.return_value = [] mock_read_source_code_config_json.return_value = DUMMY_SOURCE_CODE mock_read_distributed_runner_json.return_value = DUMMY_DISTRIBUTED_RUNNER mock_get_mpirun_command.return_value = DUMMY_MPI_COMMAND diff --git a/tests/unit/sagemaker/modules/train/container_drivers/test_torchrun_driver.py b/tests/unit/sagemaker/modules/train/container_drivers/test_torchrun_driver.py index fc75546cb1..f32b440e39 100644 --- a/tests/unit/sagemaker/modules/train/container_drivers/test_torchrun_driver.py +++ b/tests/unit/sagemaker/modules/train/container_drivers/test_torchrun_driver.py @@ -86,7 +86,12 @@ def test_get_base_pytorch_command_torch_distributed_launch( "sagemaker.modules.train.container_drivers.torchrun_driver.read_distributed_runner_json", return_value=DUMMY_DISTRIBUTED_RUNNER, ) +@patch( + "sagemaker.modules.train.container_drivers.torchrun_driver.hyperparameters_to_cli_args", + return_value=[], +) def test_create_commands_single_node( + mock_hyperparameters_to_cli_args, mock_read_distributed_runner_json, mock_read_source_code_json, mock_get_base_pytorch_command, @@ -137,7 +142,12 @@ def test_create_commands_single_node( "sagemaker.modules.train.container_drivers.torchrun_driver.read_distributed_runner_json", return_value=DUMMY_DISTRIBUTED_RUNNER, ) +@patch( + "sagemaker.modules.train.container_drivers.torchrun_driver.hyperparameters_to_cli_args", + return_value=[], +) def test_create_commands_multi_node( + mock_hyperparameters_to_cli_args, mock_read_distributed_runner_json, mock_read_source_code_json, mock_get_base_pytorch_command, diff --git a/tests/unit/sagemaker/modules/train/container_drivers/test_utils.py b/tests/unit/sagemaker/modules/train/container_drivers/test_utils.py new file mode 100644 index 0000000000..aba97996b0 --- /dev/null +++ b/tests/unit/sagemaker/modules/train/container_drivers/test_utils.py @@ -0,0 +1,121 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Container Utils Unit Tests.""" +from __future__ import absolute_import + +from sagemaker.modules.train.container_drivers.utils import ( + safe_deserialize, + safe_serialize, + hyperparameters_to_cli_args, +) + +SM_HPS = { + "boolean": "true", + "dict": '{"string":"value","integer":3,"list":[1,2,3],"dict":{"key":"value"},"boolean":true}', + "float": "3.14", + "integer": "1", + "list": "[1,2,3]", + "string": "Hello World", +} + + +def test_hyperparameters_to_cli_args(): + args = hyperparameters_to_cli_args(SM_HPS) + + assert args == [ + "--boolean", + "true", + "--dict", + '{"string": "value", "integer": 3, "list": [1, 2, 3], "dict": {"key": "value"}, "boolean": true}', + "--float", + "3.14", + "--integer", + "1", + "--list", + "[1, 2, 3]", + "--string", + "Hello World", + ] + + +def test_safe_deserialize_not_a_string(): + assert safe_deserialize(123) == 123 + assert safe_deserialize([1, 2, 3]) == [1, 2, 3] + assert safe_deserialize({"key": "value"}) == {"key": "value"} + + +def test_safe_deserialize_boolean_strings(): + assert safe_deserialize("true") is True + assert safe_deserialize("false") is False + assert safe_deserialize("True") is True + assert safe_deserialize("False") is False + + +def test_safe_deserialize_valid_json_string(): + json_data = '{"key": "value", "number": 123, "boolean": true}' + expected_output = {"key": "value", "number": 123, "boolean": True} + assert safe_deserialize(json_data) == expected_output + + assert safe_deserialize("Hello World") == "Hello World" + assert safe_deserialize("12345") == 12345 + + assert safe_deserialize("3.14") == 3.14 + assert safe_deserialize("[1,2,3]") == [1, 2, 3] + + +def test_safe_deserialize_invalid_json_string(): + invalid_json = '{"key": value}' # Missing quotes around value so not valid json + assert safe_deserialize(invalid_json) == invalid_json + + +def test_safe_deserialize_null_string(): + assert safe_deserialize("null") == None # noqa: E711 + assert safe_deserialize("None") == "None" + + +def test_safe_serialize_string(): + assert safe_serialize("Hello World") == "Hello World" + assert safe_serialize("12345") == "12345" + assert safe_serialize("true") == "true" + + +def test_safe_serialize_serializable_data(): + assert safe_serialize({"key": "value", "number": 123, "boolean": True}) == ( + '{"key": "value", "number": 123, "boolean": true}' + ) + assert safe_serialize([1, 2, 3]) == "[1, 2, 3]" + assert safe_serialize(123) == "123" + assert safe_serialize(3.14) == "3.14" + assert safe_serialize(True) == "true" + assert safe_serialize(False) == "false" + assert safe_serialize(None) == "null" + + +def test_safe_serialize_custom_object(): + class CustomObject: + def __str__(self): + return "CustomObject" + + obj = CustomObject() + assert safe_serialize(obj) == "CustomObject" + + +def test_safe_serialize_invalid_data(): + invalid_data = {"key": set([1, 2, 3])} # Sets are not JSON serializable + assert safe_serialize(invalid_data) == str(invalid_data) + + +def test_safe_serialize_empty_data(): + assert safe_serialize("") == "" + assert safe_serialize([]) == "[]" + assert safe_serialize({}) == "{}" diff --git a/tests/unit/sagemaker/modules/train/test_model_trainer.py b/tests/unit/sagemaker/modules/train/test_model_trainer.py index 2435dacb40..42bab9a4b6 100644 --- a/tests/unit/sagemaker/modules/train/test_model_trainer.py +++ b/tests/unit/sagemaker/modules/train/test_model_trainer.py @@ -13,6 +13,8 @@ """ModelTrainer Tests.""" from __future__ import absolute_import +import shutil +import tempfile import json import os import pytest @@ -22,7 +24,6 @@ from sagemaker.modules.train.model_trainer import ModelTrainer from sagemaker.modules.constants import ( DEFAULT_INSTANCE_TYPE, - SM_DRIVERS_LOCAL_PATH, DISTRIBUTED_RUNNER_JSON, SOURCE_CODE_JSON, TRAIN_SCRIPT, @@ -442,43 +443,55 @@ def test_additional_settings(mock_training_job, modules_session): ], ) @patch("sagemaker.modules.train.model_trainer.TrainingJob") -def test_train_with_distributed_runner(mock_training_job, test_case, modules_session): +@patch("sagemaker.modules.train.model_trainer.TemporaryDirectory") +def test_train_with_distributed_runner( + mock_tmp_dir, mock_training_job, test_case, request, modules_session +): modules_session.upload_data.return_value = ( f"s3://{DEFAULT_BUCKET}/{DEFAULT_BASE_NAME}-job/input/test" ) - expected_train_script_path = f"{SM_DRIVERS_LOCAL_PATH}/{TRAIN_SCRIPT}" - expected_runner_json_path = f"{SM_DRIVERS_LOCAL_PATH}/{DISTRIBUTED_RUNNER_JSON}" - expected_source_code_json_path = f"{SM_DRIVERS_LOCAL_PATH}/{SOURCE_CODE_JSON}" - - model_trainer = ModelTrainer( - session=modules_session, - training_image=DEFAULT_IMAGE, - source_code=test_case["source_code"], - distributed_runner=test_case["distributed_runner"], - ) + tmp_dir = tempfile.TemporaryDirectory() + tmp_dir._cleanup = False + tmp_dir.cleanup = lambda: None + mock_tmp_dir.return_value = tmp_dir + + expected_train_script_path = os.path.join(tmp_dir.name, TRAIN_SCRIPT) + expected_runner_json_path = os.path.join(tmp_dir.name, DISTRIBUTED_RUNNER_JSON) + expected_source_code_json_path = os.path.join(tmp_dir.name, SOURCE_CODE_JSON) + + try: + model_trainer = ModelTrainer( + session=modules_session, + training_image=DEFAULT_IMAGE, + source_code=test_case["source_code"], + distributed_runner=test_case["distributed_runner"], + ) - model_trainer.train() - mock_training_job.create.assert_called_once() - assert mock_training_job.create.call_args.kwargs["hyper_parameters"] == ( - test_case["expected_hyperparameters"] - ) + model_trainer.train() + mock_training_job.create.assert_called_once() + assert mock_training_job.create.call_args.kwargs["hyper_parameters"] == ( + test_case["expected_hyperparameters"] + ) - assert os.path.exists(expected_train_script_path) - with open(expected_train_script_path, "r") as f: - train_script_content = f.read() - assert test_case["expected_template"] in train_script_content + assert os.path.exists(expected_train_script_path) + with open(expected_train_script_path, "r") as f: + train_script_content = f.read() + assert test_case["expected_template"] in train_script_content - assert os.path.exists(expected_runner_json_path) - with open(expected_runner_json_path, "r") as f: - runner_json_content = f.read() - assert test_case["distributed_runner"].model_dump(exclude_none=True) == ( - json.loads(runner_json_content) - ) + assert os.path.exists(expected_runner_json_path) + with open(expected_runner_json_path, "r") as f: + runner_json_content = f.read() + assert test_case["distributed_runner"].model_dump(exclude_none=True) == ( + json.loads(runner_json_content) + ) - assert os.path.exists(expected_source_code_json_path) - with open(expected_source_code_json_path, "r") as f: - source_code_json_content = f.read() - assert test_case["source_code"].model_dump(exclude_none=True) == ( - json.loads(source_code_json_content) - ) + assert os.path.exists(expected_source_code_json_path) + with open(expected_source_code_json_path, "r") as f: + source_code_json_content = f.read() + assert test_case["source_code"].model_dump(exclude_none=True) == ( + json.loads(source_code_json_content) + ) + finally: + shutil.rmtree(tmp_dir.name) + assert not os.path.exists(tmp_dir.name) From 053808f9929e21cd4187d3da86474a6b4fbeb9e8 Mon Sep 17 00:00:00 2001 From: Gokul Anantha Narayanan <166456257+nargokul@users.noreply.github.com> Date: Tue, 12 Nov 2024 09:54:44 -0800 Subject: [PATCH 24/79] Trainer handshake (#1535) * Base model trainer (#1521) * Base model trainer * flake8 * add testing notebook * add param validation & set defaults * Implement simple train method * feature: support script mode with local train.sh (#1523) * feature: support script mode with local train.sh * Stop tracking train.sh and add it to .gitignore * update message * make dir if not exist * fix docs * fix: docstyle * Address comments * fix hyperparams * Revert pydantic custom error * pylint * Image Spec refactoring and updates (#1525) * Image Spec refactoring and updates * Unit tests and update function for Image Spec * Fix hugging face test * Fix Tests * Add unit tests for ModelTrainer (#1527) * Add unit tests for ModelTrainer * Flake8 * format * Add example notebook (#1528) * Add testing notebook * format * use smaller data * remove large dataset * update * pylint * flake8 * ignore docstyle in directories with test * format * format * Add enviornment variable bootstrapping script (#1530) * Add enviornment variables scripts * format * fix comment * add docstrings * fix comment * feature: add utility function to capture local snapshot (#1524) * local snapshot * Update pip list command * Remove function calls * Address comments * Address comments * Change to make Model Trainer return a Model Object * Fix * Cleanup * Support intelligent parameters (#1540) * Support intelligent parameters * fix codestyle * Revert Image Spec (#1541) * Cleanup ModelTrainer (#1542) * General image builder (#1546) * General image builder * General image builder * Fix codestyle * Fix codestyle * Move location * Add warnings * Add integ tests * Fix integ test * Fix integ test * Fix region error * Add region * Latest Container Image (#1545) * Latest Container Image * Test Fixes * Parameterized tests and some logic updates * Test fixes * Move to Image URI * Fixes for unit test * Fixes for unit test * Fix codestyle error checks * Cleanup ModelTrainer code (#1552) * Updates * feat: add pre-processing and post-processing logic to inference_spec (#1560) * add pre-processing and post-processing logic to inference_spec * fix format * make accept_type and content_type optional * remove accept_type and content_type from pre/post processing * correct typo * Add Distributed Training Support Model Trainer (#1536) * Add path to set Additional Settings in ModelTrainer (#1555) * Updates * Mask Sensitive Env Logs in Container (#1568) * Cleanup PR * Codestyle fixes * Update logic to use model parameter instead of model_path * Fixes * Fixes * Tests * Codestyle Fixes * Codestyle Fixes * Codestyle Fixes * Codestyle Fixes --------- Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Co-authored-by: pintaoz-aws <167920275+pintaoz-aws@users.noreply.github.com> Co-authored-by: Pravali Uppugunduri <46845440+pravali96@users.noreply.github.com> --- .../model_builder_handshake.ipynb | 150 ++++++++++++++++++ src/sagemaker/modules/train/model_trainer.py | 14 +- src/sagemaker/serve/builder/model_builder.py | 28 ++-- .../modules/train/test_model_trainer.py | 18 ++- 4 files changed, 195 insertions(+), 15 deletions(-) create mode 100644 src/sagemaker/modules/testing_notebooks/model_builder_handshake.ipynb diff --git a/src/sagemaker/modules/testing_notebooks/model_builder_handshake.ipynb b/src/sagemaker/modules/testing_notebooks/model_builder_handshake.ipynb new file mode 100644 index 0000000000..f24448d7d9 --- /dev/null +++ b/src/sagemaker/modules/testing_notebooks/model_builder_handshake.ipynb @@ -0,0 +1,150 @@ +{ + "cells": [ + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "source": [ + "from sagemaker_core.main.shapes import TrainingJob\n", + "\n", + "from sagemaker import Session, get_execution_role\n", + "\n", + "sagemaker_session = Session()\n", + "role = get_execution_role()\n", + "region = sagemaker_session.boto_region_name\n", + "bucket = sagemaker_session.default_bucket()" + ], + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "\n", + "from sagemaker.modules.configs import SourceCode\n", + "from sagemaker.modules.train.model_trainer import ModelTrainer\n", + "\n", + "xgboost_image = \"433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest\"\n", + "\n", + "source_code = SourceCode(\n", + " command=\"echo 'Hello World' && env\",\n", + ")\n", + "model_trainer = ModelTrainer(\n", + " training_image=xgboost_image,\n", + " source_code=source_code,\n", + ")\n", + "\n", + "model_trainer.train()" + ], + "id": "4b3a4f7d1713685f", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "from sagemaker.serve.builder.schema_builder import SchemaBuilder\n", + "import pandas as pd\n", + "from xgboost import XGBClassifier\n", + "from sagemaker.serve.spec.inference_spec import InferenceSpec\n", + "from sagemaker.serve import ModelBuilder\n", + "\n", + "data = {\n", + " 'Name': ['Alice', 'Bob', 'Charlie']\n", + "}\n", + "df = pd.DataFrame(data)\n", + "schema_builder = SchemaBuilder(sample_input=df, sample_output=df)\n", + "\n", + "\n", + "class XGBoostSpec(InferenceSpec):\n", + " def load(self, model_dir: str):\n", + " print(model_dir)\n", + " model = XGBClassifier()\n", + " model.load_model(model_dir + \"/xgboost-model\")\n", + " return model\n", + "\n", + " def invoke(self, input_object: object, model: object):\n", + " prediction_probabilities = model.predict_proba(input_object)\n", + " predictions = np.argmax(prediction_probabilities, axis=1)\n", + " return predictions\n", + "\n", + "model_builder = ModelBuilder(\n", + " model=model_trainer, # ModelTrainer object passed onto ModelBuilder directly \n", + " role_arn=role,\n", + " image_uri=xgboost_image,\n", + " inference_spec=XGBoostSpec(),\n", + " schema_builder=schema_builder,\n", + " instance_type=\"ml.c6i.xlarge\"\n", + ")\n", + "model=model_builder.build()\n", + "predictor=model_builder.deploy()\n", + "\n", + "predictor\n", + "assert model.model_data == model_trainer._latest_training_job.model_artifacts.s3_model_artifacts\n", + "\n", + "print(model.model_data)" + ], + "id": "295a16ef277257a0", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "training_job: TrainingJob = model_trainer._latest_training_job\n", + "\n", + "model_builder = ModelBuilder(\n", + " model=training_job, # Sagemaker core's TrainingJob object passed onto ModelBuilder directly \n", + " role_arn=role,\n", + " image_uri=xgboost_image,\n", + " schema_builder=schema_builder,\n", + " inference_spec=XGBoostSpec(),\n", + " instance_type=\"ml.c6i.xlarge\"\n", + ")\n", + "model=model_builder.build()\n", + "\n", + "assert model.model_data == training_job.model_artifacts.s3_model_artifacts\n", + "\n", + "print(model.model_data)" + ], + "id": "935ea8486278d7b1", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "", + "id": "757180da84407a1a", + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index efd5011055..915e716637 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -19,11 +19,12 @@ from tempfile import TemporaryDirectory from typing import Optional, List, Union, Dict, Any -from pydantic import BaseModel, ConfigDict, PrivateAttr, validate_call - +from sagemaker_core.main import resources from sagemaker_core.resources import TrainingJob from sagemaker_core.shapes import AlgorithmSpecification +from pydantic import BaseModel, ConfigDict, PrivateAttr, validate_call + from sagemaker import get_execution_role, Session from sagemaker.modules.configs import ( Compute, @@ -51,6 +52,7 @@ CheckpointConfig, InputData, ) + from sagemaker.modules.distributed import ( DistributedRunner, TorchrunSMP, @@ -187,6 +189,9 @@ class ModelTrainer(BaseModel): hyperparameters: Optional[Dict[str, Any]] = None tags: Optional[List[Tag]] = None + # Created Artifacts + _latest_training_job: Optional[resources.TrainingJob] = None + # Metrics settings _enable_sage_maker_metrics_time_series: Optional[bool] = PrivateAttr(default=False) _metric_definitions: Optional[List[MetricDefinition]] = PrivateAttr(default=None) @@ -194,6 +199,7 @@ class ModelTrainer(BaseModel): # Debugger settings _debug_hook_config: Optional[DebugHookConfig] = PrivateAttr(default=None) _debug_rule_configurations: Optional[List[DebugRuleConfiguration]] = PrivateAttr(default=None) + _remote_debug_config: Optional[RemoteDebugConfig] = PrivateAttr(default=None) _profiler_config: Optional[ProfilerConfig] = PrivateAttr(default=None) _profiler_rule_configurations: Optional[List[ProfilerRuleConfiguration]] = PrivateAttr( default=None @@ -448,11 +454,9 @@ def train( infra_check_config=self._infra_check_config, session_chaining_config=self._session_chaining_config, ) - + self._latest_training_job = training_job if wait: training_job.wait(logs=logs) - if logs and not wait: - logger.warning("Not displaing the training container logs as 'wait' is set to False.") def create_input_data_channel(self, channel_name: str, data_source: DataSourceType) -> Channel: """Create an input data channel for the training job. diff --git a/src/sagemaker/serve/builder/model_builder.py b/src/sagemaker/serve/builder/model_builder.py index bdbd08ff55..ae3f2cac94 100644 --- a/src/sagemaker/serve/builder/model_builder.py +++ b/src/sagemaker/serve/builder/model_builder.py @@ -24,6 +24,9 @@ from pathlib import Path +from sagemaker_core.main.resources import TrainingJob + +from sagemaker.estimator import Estimator from sagemaker.enums import Tag from sagemaker.jumpstart.accessors import JumpStartS3PayloadAccessor from sagemaker.jumpstart.utils import get_jumpstart_content_bucket @@ -176,8 +179,9 @@ class ModelBuilder(Triton, DJL, JumpStart, TGI, Transformers, TensorflowServing, The schema builder can be omitted for HuggingFace models with task types TextGeneration, TextClassification, and QuestionAnswering. Omitting SchemaBuilder is in beta for FillMask, and AutomaticSpeechRecognition use-cases. - model (Optional[Union[object, str]): Model object (with ``predict`` method to perform - inference) or a HuggingFace/JumpStart Model ID. Either ``model`` or ``inference_spec`` + model (Optional[Union[object, str, ModelTrainer, TrainingJob, Estimator]]): + Define object from which training artifacts can be extracted. + Either ``model`` or ``inference_spec`` is required for the model builder to build the artifact. inference_spec (InferenceSpec): The inference spec file with your customized ``invoke`` and ``load`` functions. @@ -268,14 +272,9 @@ class ModelBuilder(Triton, DJL, JumpStart, TGI, Transformers, TensorflowServing, schema_builder: Optional[SchemaBuilder] = field( default=None, metadata={"help": "Defines the i/o schema of the model"} ) - model: Optional[Union[object, str]] = field( + model: Optional[Union[object, str, "ModelTrainer", TrainingJob, Estimator]] = field( default=None, - metadata={ - "help": ( - 'Model object with "predict" method to perform inference ' - "or HuggingFace/JumpStart Model ID" - ) - }, + metadata={"help": "Define object from which training artifacts can be extracted"} ) inference_spec: InferenceSpec = field( default=None, @@ -852,6 +851,7 @@ def build( # pylint: disable=R0911 Returns: Type[Model]: A deployable ``Model`` object. """ + from sagemaker.modules.train.model_trainer import ModelTrainer self.modes = dict() if mode: @@ -859,6 +859,16 @@ def build( # pylint: disable=R0911 if role_arn: self.role_arn = role_arn + if isinstance(self.model, TrainingJob): + self.model_path = self.model.model_artifacts.s3_model_artifacts + self.model = None + elif isinstance(self.model, ModelTrainer): + self.model_path = self.model._latest_training_job.model_artifacts.s3_model_artifacts + self.model = None + elif isinstance(self.model, Estimator): + self.model_path = self.model.output_path + self.model = None + self.sagemaker_session = sagemaker_session or self.sagemaker_session or Session() self.sagemaker_session.settings._local_download_dir = self.model_path diff --git a/tests/unit/sagemaker/modules/train/test_model_trainer.py b/tests/unit/sagemaker/modules/train/test_model_trainer.py index 42bab9a4b6..b9fdc181d7 100644 --- a/tests/unit/sagemaker/modules/train/test_model_trainer.py +++ b/tests/unit/sagemaker/modules/train/test_model_trainer.py @@ -20,6 +20,8 @@ import pytest from unittest.mock import patch, MagicMock +from sagemaker_core.main.resources import TrainingJob + from sagemaker.session import Session from sagemaker.modules.train.model_trainer import ModelTrainer from sagemaker.modules.constants import ( @@ -316,6 +318,7 @@ def test_debugger_settings(mock_training_job, modules_session): assert model_trainer._debug_hook_config == debug_hook_config assert model_trainer._debug_rule_configurations == debug_rule_config + assert model_trainer._profiler_config == profiler_config assert model_trainer._profiler_rule_configurations == profiler_rule_config assert model_trainer._tensor_board_output_config == tensor_board_output_config @@ -485,7 +488,12 @@ def test_train_with_distributed_runner( assert test_case["distributed_runner"].model_dump(exclude_none=True) == ( json.loads(runner_json_content) ) - + assert os.path.exists(expected_source_code_json_path) + with open(expected_source_code_json_path, "r") as f: + source_code_json_content = f.read() + assert test_case["source_code"].model_dump(exclude_none=True) == ( + json.loads(source_code_json_content) + ) assert os.path.exists(expected_source_code_json_path) with open(expected_source_code_json_path, "r") as f: source_code_json_content = f.read() @@ -495,3 +503,11 @@ def test_train_with_distributed_runner( finally: shutil.rmtree(tmp_dir.name) assert not os.path.exists(tmp_dir.name) + + +@patch("sagemaker.modules.train.model_trainer.TrainingJob") +def test_train_stores_created_training_job(mock_training_job, model_trainer): + mock_training_job.create.return_value = TrainingJob(training_job_name="Created-job") + model_trainer.train(wait=False) + assert model_trainer._latest_training_job is not None + assert model_trainer._latest_training_job == TrainingJob(training_job_name="Created-job") From debcdc2a9778021bd21dd0fbaf83d39b63e63ee7 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Tue, 12 Nov 2024 10:57:04 -0800 Subject: [PATCH 25/79] Add Support for Training Recipes (#1565) Co-authored-by: Gokul Anantha Narayanan <166456257+nargokul@users.noreply.github.com> --- MANIFEST.in | 1 + pyproject.toml | 1 + src/sagemaker/modules/distributed.py | 89 +++-- .../base_model_trainer.ipynb | 81 ++++- .../container_drivers/scripts/environment.py | 3 +- src/sagemaker/modules/train/model_trainer.py | 96 +++++- .../modules/train/sm_recipes/__init__.py | 0 .../train/sm_recipes/training_recipes.json | 11 + .../modules/train/sm_recipes/utils.py | 310 ++++++++++++++++++ src/sagemaker/modules/utils.py | 51 +++ tests/integ/sagemaker/modules/conftest.py | 41 +++ .../modules/train/test_model_trainer.py | 12 +- .../modules/train/test_model_trainer.py | 14 +- 13 files changed, 645 insertions(+), 65 deletions(-) create mode 100644 src/sagemaker/modules/train/sm_recipes/__init__.py create mode 100644 src/sagemaker/modules/train/sm_recipes/training_recipes.json create mode 100644 src/sagemaker/modules/train/sm_recipes/utils.py create mode 100644 tests/integ/sagemaker/modules/conftest.py diff --git a/MANIFEST.in b/MANIFEST.in index ab053b00aa..cb77180c22 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -3,6 +3,7 @@ recursive-include src/sagemaker *.py include src/sagemaker/image_uri_config/*.json include src/sagemaker/serve/schema/*.json include src/sagemaker/serve/requirements.txt +include src/sagemaker/modules/train/sm_recipes/training_recipes.json recursive-include requirements * include VERSION diff --git a/pyproject.toml b/pyproject.toml index 8b9e9fa92f..1b9569afc5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -39,6 +39,7 @@ dependencies = [ "importlib-metadata>=1.4.0,<7.0", "jsonschema", "numpy>=1.9.0,<2.0", + "omegaconf>=2.2,<2.3", "packaging>=20.0", "pandas", "pathos", diff --git a/src/sagemaker/modules/distributed.py b/src/sagemaker/modules/distributed.py index 32ea7e81a6..4e8334178e 100644 --- a/src/sagemaker/modules/distributed.py +++ b/src/sagemaker/modules/distributed.py @@ -15,49 +15,17 @@ from typing import Optional, Dict, Any, List from pydantic import BaseModel, PrivateAttr +from sagemaker.modules.utils import safe_serialize -class DistributedRunner(BaseModel): - """Base class for DistributedRunner Class""" - - _type: str = PrivateAttr() - - def model_dump(self, *args, **kwargs): - """Dump the model to a dictionary.""" - result = super().model_dump(*args, **kwargs) - result["_type"] = self._type - return result - - -class Torchrun(DistributedRunner): - """TorchDistributed. - - The Torchrun distributed runner uses `torchrun` or `torch.distributed.launch` in the backend to - launch distributed training. +class SMP(BaseModel): + """SMP. - Attributes: - process_count_per_node (int): - The number of processes to run on each node in the training job. - Will default to the number of GPUs available in the container. - """ - - _type: str = PrivateAttr(default="torchrun") - - process_count_per_node: Optional[int] = None - - -class TorchrunSMP(DistributedRunner): - """TorchrunSMP. - - The TorchrunSMP runner uses `torchrun` or `torch.distributed.launch` in the backend - to launch distributed training. This strategy is used for a PyTorch job using the SageMaker - Model Parallelism library v2. For more information on the model parallelism parameters, see: + This class is used for configuring the SageMaker Model Parallelism v2 parameters. + For more information on the model parallelism parameters, see: https://docs.aws.amazon.com/sagemaker/latest/dg/distributed-model-parallel-v2-reference.html#distributed-model-parallel-v2-reference-init-config Attributes: - process_count_per_node (int): - The number of processes to run on each node in the training job. - Will default to the number of GPUs available in the container. hybrid_shard_degree (Optional[int]): Specifies a sharded parallelism degree for the model. sm_activation_offloading (Optional[bool]): @@ -85,9 +53,6 @@ class TorchrunSMP(DistributedRunner): parallelism or expert parallelism. """ - _type: str = PrivateAttr(default="torchrun") - - process_count_per_node: Optional[int] = None hybrid_shard_degree: Optional[int] = None sm_activation_offloading: Optional[bool] = None activation_loading_horizon: Optional[int] = None @@ -98,13 +63,45 @@ class TorchrunSMP(DistributedRunner): expert_parallel_degree: Optional[int] = None random_seed: Optional[int] = None - def _to_mp_parameters_dict(self) -> Dict[str, Any]: - """Convert to a dictionary of MP parameters.""" + def _to_mp_hyperparameters(self) -> Dict[str, Any]: + """Converts to the hyperparameters format for the SageMaker Model Parallelism v2.""" mp_parameters = self.model_dump(exclude_none=True) - mp_parameters.pop("_type") - if mp_parameters.get("process_count_per_node") is not None: - mp_parameters.pop("process_count_per_node") - return mp_parameters + hyperparameters = { + "mp_parameters": safe_serialize(mp_parameters), + } + return hyperparameters + + +class DistributedRunner(BaseModel): + """Base class for DistributedRunner Class""" + + _type: str = PrivateAttr() + + def model_dump(self, *args, **kwargs): + """Dump the model to a dictionary.""" + result = super().model_dump(*args, **kwargs) + result["_type"] = self._type + return result + + +class Torchrun(DistributedRunner): + """TorchDistributed. + + The Torchrun runner uses `torchrun` or `torch.distributed.launch` in the backend to + launch distributed training. + + Attributes: + process_count_per_node (int): + The number of processes to run on each node in the training job. + Will default to the number of GPUs available in the container. + smp (Optional[SMP]): + The SageMaker Model Parallelism v2 parameters. + """ + + _type: str = PrivateAttr(default="torchrun") + + process_count_per_node: Optional[int] = None + smp: Optional["SMP"] = None class MPI(DistributedRunner): diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index e674a4e041..b25fa20979 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -117,11 +117,8 @@ "metadata": {}, "outputs": [], "source": [ - "from sagemaker.modules.train import ModelTrainer\n", "from sagemaker.modules.configs import SourceCode\n", "\n", - "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n", - "\n", "source_code = SourceCode(\n", " source_dir=\"basic-script-mode\",\n", " requirements=\"requirements.txt\",\n", @@ -163,7 +160,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install \"datasets[s3]\"" + "!pip install \"datasets[s3]\" \"requests<2.32.0\"" ] }, { @@ -463,6 +460,82 @@ ")\n", "model_trainer.train(input_data_config=[test_data], wait=False)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ModelTrainer Recipes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### SageMaker GPU Recipe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.modules.train import ModelTrainer\n", + "from sagemaker.modules.configs import Compute, InputData\n", + "\n", + "recipe_overrides = {\n", + " \"run\": {\n", + " \"results_dir\": \"/opt/ml/model\",\n", + " },\n", + " \"exp_manager\": {\n", + " \"exp_dir\": \"\",\n", + " \"explicit_log_dir\": \"/opt/ml/output/tensorboard\",\n", + " \"checkpoint_dir\": \"/opt/ml/checkpoints\",\n", + " \"export_full_model\": {\n", + " \"save_last\": False\n", + " }\n", + " }, \n", + " \"model\": {\n", + " \"data\": {\n", + " \"train_dir\": \"/opt/ml/input/data/train\",\n", + " \"val_dir\": \"/opt/ml/input/data/val\",\n", + " \"use_synthetic_data\": True,\n", + " },\n", + " \"train_batch_size\": 1,\n", + " \"num_hidden_layers\": 4,\n", + " \"fp8\": False,\n", + " },\n", + " \"trainer\": {\n", + " \"num_nodes\": 1\n", + " }\n", + "}\n", + "\n", + "training_image = \"059094755717.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-gpu\"\n", + "\n", + "model_trainer = ModelTrainer.from_recipe(\n", + " training_recipe=\"training/llama/hf_llama3_8b_seq8192_gpu\",\n", + " training_image=training_image,\n", + " recipe_overrides=recipe_overrides,\n", + " compute=Compute(instance_type=\"ml.p4d.24xlarge\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Successful Run - https://tiny.amazon.com/14jxjrndx/IsenLink" + ] } ], "metadata": { diff --git a/src/sagemaker/modules/train/container_drivers/scripts/environment.py b/src/sagemaker/modules/train/container_drivers/scripts/environment.py index b02833b5af..173d47599c 100644 --- a/src/sagemaker/modules/train/container_drivers/scripts/environment.py +++ b/src/sagemaker/modules/train/container_drivers/scripts/environment.py @@ -152,7 +152,8 @@ def set_env( # Hyperparameters env_vars["SM_HPS"] = hyperparameters_config for key, value in hyperparameters_config.items(): - env_vars[f"SM_HP_{key.upper()}"] = safe_serialize(value) + key_upper = key.replace("-", "_").upper() + env_vars[f"SM_HP_{key_upper}"] = safe_serialize(value) # Host Variables current_host = resource_config["current_host"] diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 915e716637..323f2242a8 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -55,7 +55,7 @@ from sagemaker.modules.distributed import ( DistributedRunner, - TorchrunSMP, + Torchrun, ) from sagemaker.modules.utils import ( _get_repo_name_from_image, @@ -85,6 +85,7 @@ EXECUTE_BASIC_SCRIPT_DRIVER, ) from sagemaker.modules import logger +from sagemaker.modules.train.sm_recipes.utils import get_args_from_recipe, _determine_device_type class ModelTrainer(BaseModel): @@ -213,6 +214,14 @@ class ModelTrainer(BaseModel): _session_chaining_config: Optional[SessionChainingConfig] = PrivateAttr(default=None) _remote_debug_config: Optional[RemoteDebugConfig] = PrivateAttr(default=None) + _temp_recipe_train_dir: Optional[TemporaryDirectory] = PrivateAttr(default=None) + + def __del__(self): + """Destructor method to clean up the temporary directory.""" + # Clean up the temporary directory if it exists + if self._temp_recipe_train_dir is not None: + self._temp_recipe_train_dir.cleanup() + def _validate_training_image_and_algorithm_name( self, training_image: Optional[str], algorithm_name: Optional[str] ): @@ -383,9 +392,9 @@ def train( distributed_runner=self.distributed_runner, ) - if isinstance(self.distributed_runner, TorchrunSMP): - mp_parameters = self.distributed_runner._to_mp_parameters_dict() - string_hyper_parameters["mp_parameters"] = safe_serialize(mp_parameters) + if isinstance(self.distributed_runner, Torchrun) and self.distributed_runner.smp: + mp_parameters = self.distributed_runner.smp._to_mp_hyperparameters() + string_hyper_parameters.update(mp_parameters) self._write_source_code_json(tmp_dir=drivers_dir, source_code=self.source_code) self._write_distributed_runner_json( @@ -455,6 +464,11 @@ def train( session_chaining_config=self._session_chaining_config, ) self._latest_training_job = training_job + + # Clean up the temporary directory if it exists + if self._temp_recipe_train_dir is not None: + self._temp_recipe_train_dir.cleanup() + if wait: training_job.wait(logs=logs) @@ -748,3 +762,77 @@ def with_additional_settings( self._session_chaining_config = session_chaining_config self._remote_debug_config = remote_debug_config return self + + @classmethod + def from_recipe( + cls, + training_recipe: str, + compute: Compute, + recipe_overrides: Optional[Dict[str, Any]] = None, + training_image: Optional[str] = None, + session: Optional[Session] = None, + role: Optional[str] = None, + base_job_name: Optional[str] = None, + **kwargs, + ) -> "ModelTrainer": + """Create a ModelTrainer from a training recipe. + + Args: + training_recipe (str): + The training recipe to use for training the model. This must be the name of + a sagemaker training recipe or a path to a local training recipe .yaml file. + compute (Compute): + The compute configuration. This is used to specify the compute resources for + the training job. If not specified, will default to 1 instance of ml.m5.xlarge. + recipe_overrides (Optional[Dict[str, Any]]): + The recipe overrides. This is used to override the default recipe parameters. + training_image (Optional[str]): + The training image URI to use for the training job container. If not specified, + the training image will be determined from the recipe. + session (Optional[Session]): + The SageMaker session. + If not specified, a new session will be created. + role (Optional[str]): + The IAM role ARN for the training job. + If not specified, the default SageMaker execution role will be used. + base_job_name (Optional[str]): + The base name for the training job. + If not specified, a default name will be generated using the algorithm name + or training image. + kwargs: + Additional keyword arguments to pass to the ModelTrainer constructor. + + """ + if compute.instance_type is None: + raise ValueError( + "Must set `instance_type` in compute_config when using training recipes." + ) + device_type = _determine_device_type(compute.instance_type) + if device_type == "cpu": + raise ValueError( + "Training recipes are not supported for CPU instances. " + + "Please provide a GPU or Tranium instance type." + ) + + if session is None: + session = Session() + logger.warning("Session not provided. Using default Session.") + if role is None: + role = get_execution_role() + logger.warning(f"Role not provided. Using default role:\n{role}") + + model_trainer_args, recipe_train_dir = get_args_from_recipe( + training_recipe=training_recipe, + recipe_overrides=recipe_overrides, + compute=compute, + session=session, + ) + if training_image is not None: + model_trainer_args["training_image"] = training_image + + model_trainer = cls( + session=session, role=role, base_job_name=base_job_name, **model_trainer_args, **kwargs + ) + + model_trainer._temp_recipe_train_dir = recipe_train_dir + return model_trainer diff --git a/src/sagemaker/modules/train/sm_recipes/__init__.py b/src/sagemaker/modules/train/sm_recipes/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/sagemaker/modules/train/sm_recipes/training_recipes.json b/src/sagemaker/modules/train/sm_recipes/training_recipes.json new file mode 100644 index 0000000000..bc571548e3 --- /dev/null +++ b/src/sagemaker/modules/train/sm_recipes/training_recipes.json @@ -0,0 +1,11 @@ +{ + "adapter_repo": "git@github.com:aws/private-sagemaker-training-adapter-for-nemo-staging.git", + "launcher_repo": "git@github.com:aws/private-sagemaker-training-launcher-staging.git", + "neuron_dist_repo": "https://github.com/aws-neuron/neuronx-distributed-training.git", + "gpu_image" : { + "framework": "pytorch-smp", + "version": "2.3.1", + "additional_args": {} + }, + "neuron_image": "855988369404.dkr.ecr.us-west-2.amazonaws.com/chinmayee-dev:neuron_sept26_v1" +} \ No newline at end of file diff --git a/src/sagemaker/modules/train/sm_recipes/utils.py b/src/sagemaker/modules/train/sm_recipes/utils.py new file mode 100644 index 0000000000..c4c0c2c68d --- /dev/null +++ b/src/sagemaker/modules/train/sm_recipes/utils.py @@ -0,0 +1,310 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Utility functions for SageMaker training recipes.""" +from __future__ import absolute_import + +import math +import os +import json +import shutil +import tempfile +from urllib.request import urlretrieve +from typing import Dict, Any, Optional, Tuple + +import omegaconf +from omegaconf import OmegaConf, dictconfig + +from sagemaker.image_uris import retrieve +from sagemaker import Session + +from sagemaker.modules import logger +from sagemaker.modules.utils import _run_clone_command_silent +from sagemaker.modules.configs import Compute, SourceCode +from sagemaker.modules.distributed import Torchrun, SMP + + +def _try_resolve_recipe(recipe, key=None): + """Try to resolve recipe and return resolved recipe.""" + if key is not None: + recipe = dictconfig.DictConfig({key: recipe}) + try: + OmegaConf.resolve(recipe) + except omegaconf.errors.OmegaConfBaseException: + return None + if key is None: + return recipe + return recipe[key] + + +def _determine_device_type(instance_type: str) -> str: + """Determine device type (gpu, cpu, trainium) based on instance type.""" + instance_family = instance_type.split(".")[1] + if instance_family.startswith(("p", "g")): + return "gpu" + if instance_family.startswith("trn"): + return "trainium" + return "cpu" + + +def _load_recipes_cfg() -> str: + """Load training recipes configuration json.""" + training_recipes_cfg_filename = os.path.join(os.path.dirname(__file__), "training_recipes.json") + with open(training_recipes_cfg_filename) as training_recipes_cfg_file: + training_recipes_cfg = json.load(training_recipes_cfg_file) + return training_recipes_cfg + + +def _load_base_recipe( + training_recipe: str, + recipe_overrides: Optional[Dict[str, Any]], + training_recipes_cfg: Optional[Dict[str, Any]] = None, +) -> Dict[str, Any]: + """Load recipe and apply overrides.""" + if recipe_overrides is None: + recipe_overrides = dict() + + temp_local_recipe = tempfile.NamedTemporaryFile(prefix="recipe_original", suffix=".yaml").name + + if training_recipe.endswith(".yaml"): + if os.path.isfile(training_recipe): + shutil.copy(training_recipe, temp_local_recipe) + else: + try: + urlretrieve(training_recipe, temp_local_recipe) + except Exception as e: + raise ValueError( + f"Could not fetch the provided recipe {training_recipe}: exception {str(e)}" + ) + else: + recipe_launcher_dir = tempfile.TemporaryDirectory(prefix="launcher_") + + launcher_repo = os.environ.get("training_launcher_git", None) or training_recipes_cfg.get( + "launcher_repo" + ) + _run_clone_command_silent(launcher_repo, recipe_launcher_dir.name) + + recipe = os.path.join( + recipe_launcher_dir.name, + "recipes_collection", + "recipes", + training_recipe + ".yaml", + ) + if os.path.isfile(recipe): + shutil.copy(recipe, temp_local_recipe) + else: + raise ValueError(f"Recipe {training_recipe} not found.") + + recipe = OmegaConf.load(temp_local_recipe) + os.unlink(temp_local_recipe) + recipe = OmegaConf.merge(recipe, recipe_overrides) + return recipe + + +def _register_custom_resolvers(): + """Register custom resolvers for OmegaConf.""" + if not OmegaConf.has_resolver("multiply"): + OmegaConf.register_new_resolver("multiply", lambda x, y: x * y, replace=True) + if not OmegaConf.has_resolver("divide_ceil"): + OmegaConf.register_new_resolver( + "divide_ceil", lambda x, y: int(math.ceil(x / y)), replace=True + ) + if not OmegaConf.has_resolver("divide_floor"): + OmegaConf.register_new_resolver( + "divide_floor", lambda x, y: int(math.floor(x / y)), replace=True + ) + if not OmegaConf.has_resolver("add"): + OmegaConf.register_new_resolver("add", lambda *numbers: sum(numbers)) + + +def _configure_gpu_args( + training_recipes_cfg: Dict[str, Any], + region_name: str, + recipe: OmegaConf, + recipe_train_dir: tempfile.TemporaryDirectory, +) -> Dict[str, Any]: + """Configure arguments specific to GPU.""" + source_code = SourceCode() + args = dict() + + adapter_repo = os.environ.get("training_adapter_git", None) or training_recipes_cfg.get( + "adapter_repo" + ) + _run_clone_command_silent(adapter_repo, recipe_train_dir.name) + + model_type_to_entry = { + "llama_v3": ("llama", "llama_pretrain.py"), + "mistral": ("mistral", "mistral_pretrain.py"), + "mixtral": ("mixtral", "mixtral_pretrain.py"), + } + + if "model" not in recipe: + raise ValueError("Supplied recipe does not contain required field model.") + if "model_type" not in recipe["model"]: + raise ValueError("Supplied recipe does not contain required field model_type.") + model_type = recipe["model"]["model_type"] + if model_type not in model_type_to_entry: + raise ValueError(f"Model type {model_type} not supported") + + source_code.source_dir = os.path.join( + recipe_train_dir.name, "examples", model_type_to_entry[model_type][0] + ) + source_code.entry_script = model_type_to_entry[model_type][1] + + gpu_image_cfg = training_recipes_cfg.get("gpu_image") + if isinstance(gpu_image_cfg, str): + training_image = gpu_image_cfg + else: + training_image = retrieve( + gpu_image_cfg.get("framework"), + region=region_name, + version=gpu_image_cfg.get("version"), + image_scope="training", + **gpu_image_cfg.get("additional_args"), + ) + + # Setting dummy parameters for now + torch_distributed = Torchrun(smp=SMP(random_seed="123456")) + args.update( + { + "source_code": source_code, + "training_image": training_image, + "distributed_runner": torch_distributed, + } + ) + return args + + +def _configure_trainium_args( + training_recipes_cfg: Dict[str, Any], + region_name: str, + recipe_train_dir: tempfile.TemporaryDirectory, +) -> Dict[str, Any]: + """Configure arguments specific to Trainium.""" + source_code = SourceCode() + args = dict() + + _run_clone_command_silent(training_recipes_cfg.get("neuron_dist_repo"), recipe_train_dir.name) + + # Set SourceCodeConfig + source_code.source_dir = os.path.join(recipe_train_dir.name, "examples") + source_code.entry_script = "training_orchestrator.py" + neuron_image_cfg = training_recipes_cfg.get("neuron_image") + if isinstance(neuron_image_cfg, str): + training_image = neuron_image_cfg + else: + training_image = retrieve( + neuron_image_cfg.get("framework"), + region=region_name, + version=neuron_image_cfg.get("version"), + image_scope="training", + **neuron_image_cfg.get("additional_args"), + ) + + args.update( + { + "source_code": source_code, + "training_image": training_image, + "distributed_runner": Torchrun(), + } + ) + return args + + +def get_args_from_recipe( + training_recipe: str, + compute: Compute, + session: Session, + recipe_overrides: Optional[Dict[str, Any]], +) -> Tuple[Dict[str, Any], tempfile.TemporaryDirectory]: + """Get arguments for ModelTrainer from a training recipe. + + Returns a dictionary of arguments to be used with ModelTrainer like: + ```python + { + "source_code": SourceCode, + "training_image": str, + "distributed_runner": Dict[str, Any], + "compute": Compute, + "hyperparameters": Dict[str, Any], + } + ``` + + Args: + training_recipe (str): + Name of the training recipe or path to the recipe file. + compute (Compute): + Compute configuration for training. + session (Session): + Session object for training. + recipe_overrides (Optional[Dict[str, Any]]): + Overrides for the training recipe. + """ + if compute.instance_type is None: + raise ValueError("Must set `instance_type` in compute when using training recipes.") + + region_name = session.boto_region_name + training_recipes_cfg = _load_recipes_cfg() + recipe = _load_base_recipe(training_recipe, recipe_overrides, training_recipes_cfg) + + if "trainer" not in recipe: + raise ValueError("Supplied recipe does not contain required field trainer.") + + # Set instance_count + if compute.instance_count and "num_nodes" in recipe["trainer"]: + logger.warning( + "Using instance_count in compute to set number " + " of nodes. Ignoring trainer -> num_nodes in recipe." + ) + if compute.instance_count is None: + if "num_nodes" not in recipe["trainer"]: + raise ValueError( + "Must set either instance_count argument for estimator or" + "set trainer -> num_nodes in recipe." + ) + compute.instance_count = recipe["trainer"]["num_nodes"] + + # Get Training Image, SourceCodeConfig, and DistributionConfig args + device_type = _determine_device_type(compute.instance_type) + recipe_train_dir = tempfile.TemporaryDirectory(prefix="training_") + if device_type == "gpu": + args = _configure_gpu_args(training_recipes_cfg, region_name, recipe, recipe_train_dir) + elif device_type == "trainium": + args = _configure_trainium_args(training_recipes_cfg, region_name, recipe_train_dir) + else: + raise ValueError(f"Devices of type {device_type} are not supported with training recipes.") + + _register_custom_resolvers() + + # Resolve Final Recipe + final_recipe = _try_resolve_recipe(recipe) + if final_recipe is None: + final_recipe = _try_resolve_recipe(recipe, "recipes") + if final_recipe is None: + final_recipe = _try_resolve_recipe(recipe, "training") + if final_recipe is None: + raise RuntimeError("Could not resolve provided recipe.") + + # Save Final Recipe to source_dir + OmegaConf.save( + config=final_recipe, f=os.path.join(args["source_code"].source_dir, "recipe.yaml") + ) + + # Update args with compute_config and hyperparameters + args.update( + { + "compute": compute, + "hyperparameters": {"config-path": ".", "config-name": "recipe.yaml"}, + } + ) + + return args, recipe_train_dir diff --git a/src/sagemaker/modules/utils.py b/src/sagemaker/modules/utils.py index 133685db63..502f1bbc74 100644 --- a/src/sagemaker/modules/utils.py +++ b/src/sagemaker/modules/utils.py @@ -15,10 +15,15 @@ import os import json +import subprocess +import tempfile +from pathlib import Path + from datetime import datetime from typing import Literal, Any from sagemaker_core.shapes import Unassigned +from sagemaker.modules import logger def _is_valid_s3_uri(path: str, path_type: Literal["File", "Directory", "Any"] = "Any") -> bool: @@ -141,3 +146,49 @@ def safe_serialize(data): return json.dumps(data) except TypeError: return str(data) + + +def _run_clone_command_silent(repo_url, dest_dir): + """Run the 'git clone' command with the repo url and the directory to clone the repo into. + + Args: + repo_url (str): Git repo url to be cloned. + dest_dir: (str): Local path where the repo should be cloned into. + + Raises: + CalledProcessError: If failed to clone git repo. + """ + my_env = os.environ.copy() + if repo_url.startswith("https://"): + try: + my_env["GIT_TERMINAL_PROMPT"] = "0" + subprocess.check_call( + ["git", "clone", repo_url, dest_dir], + env=my_env, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + except subprocess.CalledProcessError as e: + logger.error(f"Failed to clone repository: {repo_url}") + logger.error(f"Error output:\n{e}") + raise + elif repo_url.startswith("git@") or repo_url.startswith("ssh://"): + try: + with tempfile.TemporaryDirectory() as tmp_dir: + custom_ssh_executable = Path(tmp_dir) / "ssh_batch" + with open(custom_ssh_executable, "w") as pipe: + print("#!/bin/sh", file=pipe) + print("ssh -oBatchMode=yes $@", file=pipe) + os.chmod(custom_ssh_executable, 0o511) + my_env["GIT_SSH"] = str(custom_ssh_executable) + subprocess.check_call( + ["git", "clone", repo_url, dest_dir], + env=my_env, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + ) + except subprocess.CalledProcessError as e: + del my_env["GIT_SSH"] + logger.error(f"Failed to clone repository: {repo_url}") + logger.error(f"Error output:\n{e}") + raise diff --git a/tests/integ/sagemaker/modules/conftest.py b/tests/integ/sagemaker/modules/conftest.py new file mode 100644 index 0000000000..270fe743b6 --- /dev/null +++ b/tests/integ/sagemaker/modules/conftest.py @@ -0,0 +1,41 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""This module contains code to test image builder""" +from __future__ import absolute_import + +import pytest +import json + +import boto3 +from botocore.config import Config +from sagemaker import Session + +DEFAULT_REGION = "us-west-2" + +@pytest.fixture(scope="module") +def modules_boto_session(request): + config = request.config.getoption("--boto-config") + if config: + return boto3.Session(**json.loads(config)) + else: + return boto3.Session(region_name=DEFAULT_REGION) + +@pytest.fixture(scope="module") +def modules_sagemaker_session(request, modules_boto_session): + sagemaker_client = ( + modules_boto_session.client( + "sagemaker", + config=Config(retries={"max_attempts": 10, "mode": "standard"}) + ) + ) + return Session(boto_session=modules_boto_session) diff --git a/tests/integ/sagemaker/modules/train/test_model_trainer.py b/tests/integ/sagemaker/modules/train/test_model_trainer.py index a4f02cd77c..8edfcbc97d 100644 --- a/tests/integ/sagemaker/modules/train/test_model_trainer.py +++ b/tests/integ/sagemaker/modules/train/test_model_trainer.py @@ -37,13 +37,14 @@ DEFAULT_CPU_IMAGE = "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310" -def test_hp_contract_basic_py_script(): +def test_hp_contract_basic_py_script(modules_sagemaker_session): source_code = SourceCode( source_dir=f"{DATA_DIR}/modules/params-script", entry_script="train.py", ) model_trainer = ModelTrainer( + session=modules_sagemaker_session, training_image=DEFAULT_CPU_IMAGE, hyperparameters=EXPECTED_HYPERPARAMETERS, source_code=source_code, @@ -53,12 +54,13 @@ def test_hp_contract_basic_py_script(): model_trainer.train() -def test_hp_contract_basic_sh_script(): +def test_hp_contract_basic_sh_script(modules_sagemaker_session): source_code = SourceCode( source_dir=f"{DATA_DIR}/modules/params-script", entry_script="train.sh", ) model_trainer = ModelTrainer( + session=modules_sagemaker_session, training_image=DEFAULT_CPU_IMAGE, hyperparameters=EXPECTED_HYPERPARAMETERS, source_code=source_code, @@ -68,13 +70,14 @@ def test_hp_contract_basic_sh_script(): model_trainer.train() -def test_hp_contract_mpi_script(): +def test_hp_contract_mpi_script(modules_sagemaker_session): source_code = SourceCode( source_dir=f"{DATA_DIR}/modules/params-script", entry_script="train.py", ) compute = Compute(instance_type="ml.m5.xlarge", instance_count=2) model_trainer = ModelTrainer( + session=modules_sagemaker_session, training_image=DEFAULT_CPU_IMAGE, compute=compute, hyperparameters=EXPECTED_HYPERPARAMETERS, @@ -86,13 +89,14 @@ def test_hp_contract_mpi_script(): model_trainer.train() -def test_hp_contract_torchrun_script(): +def test_hp_contract_torchrun_script(modules_sagemaker_session): source_code = SourceCode( source_dir=f"{DATA_DIR}/modules/params-script", entry_script="train.py", ) compute = Compute(instance_type="ml.m5.xlarge", instance_count=2) model_trainer = ModelTrainer( + session=modules_sagemaker_session, training_image=DEFAULT_CPU_IMAGE, compute=compute, hyperparameters=EXPECTED_HYPERPARAMETERS, diff --git a/tests/unit/sagemaker/modules/train/test_model_trainer.py b/tests/unit/sagemaker/modules/train/test_model_trainer.py index b9fdc181d7..dd46e568de 100644 --- a/tests/unit/sagemaker/modules/train/test_model_trainer.py +++ b/tests/unit/sagemaker/modules/train/test_model_trainer.py @@ -50,7 +50,7 @@ SessionChainingConfig, InputData, ) -from sagemaker.modules.distributed import Torchrun, TorchrunSMP, MPI +from sagemaker.modules.distributed import Torchrun, SMP, MPI from sagemaker.modules.templates import EXEUCTE_TORCHRUN_DRIVER, EXECUTE_MPI_DRIVER from tests.unit import DATA_DIR @@ -412,11 +412,13 @@ def test_additional_settings(mock_training_job, modules_session): }, { "source_code": DEFAULT_SOURCE_CODE, - "distributed_runner": TorchrunSMP( - hybrid_shard_degree=3, - sm_activation_offloading=True, - allow_empty_shards=True, - tensor_parallel_degree=5, + "distributed_runner": Torchrun( + smp=SMP( + hybrid_shard_degree=3, + sm_activation_offloading=True, + allow_empty_shards=True, + tensor_parallel_degree=5, + ) ), "expected_template": EXEUCTE_TORCHRUN_DRIVER, "expected_hyperparameters": { From 8cf96310716430ccf6f863d5e85c19e7d268fd2e Mon Sep 17 00:00:00 2001 From: pintaoz-aws <167920275+pintaoz-aws@users.noreply.github.com> Date: Tue, 12 Nov 2024 13:42:46 -0800 Subject: [PATCH 26/79] Support building image from Dockerfile (#1571) * Base model trainer (#1521) * Base model trainer * flake8 * add testing notebook * add param validation & set defaults * Implement simple train method * feature: support script mode with local train.sh (#1523) * feature: support script mode with local train.sh * Stop tracking train.sh and add it to .gitignore * update message * make dir if not exist * fix docs * fix: docstyle * Address comments * fix hyperparams * Revert pydantic custom error * pylint * Image Spec refactoring and updates (#1525) * Image Spec refactoring and updates * Unit tests and update function for Image Spec * Fix hugging face test * Fix Tests * Add unit tests for ModelTrainer (#1527) * Add unit tests for ModelTrainer * Flake8 * format * Add example notebook (#1528) * Add testing notebook * format * use smaller data * remove large dataset * update * pylint * flake8 * ignore docstyle in directories with test * format * format * Add enviornment variable bootstrapping script (#1530) * Add enviornment variables scripts * format * fix comment * add docstrings * fix comment * feature: add utility function to capture local snapshot (#1524) * local snapshot * Update pip list command * Remove function calls * Address comments * Address comments * Support intelligent parameters (#1540) * Support intelligent parameters * fix codestyle * Revert Image Spec (#1541) * Cleanup ModelTrainer (#1542) * General image builder (#1546) * General image builder * General image builder * Fix codestyle * Fix codestyle * Move location * Add warnings * Add integ tests * Fix integ test * Fix integ test * Fix region error * Add region * Latest Container Image (#1545) * Latest Container Image * Test Fixes * Parameterized tests and some logic updates * Test fixes * Move to Image URI * Fixes for unit test * Fixes for unit test * Fix codestyle error checks * Cleanup ModelTrainer code (#1552) * feat: add pre-processing and post-processing logic to inference_spec (#1560) * add pre-processing and post-processing logic to inference_spec * fix format * make accept_type and content_type optional * remove accept_type and content_type from pre/post processing * correct typo * Add Distributed Training Support Model Trainer (#1536) * Add path to set Additional Settings in ModelTrainer (#1555) * Support building image from Dockerfile * Fix test * Fix test * Rename functions --------- Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Co-authored-by: Gokul Anantha Narayanan <166456257+nargokul@users.noreply.github.com> Co-authored-by: Pravali Uppugunduri <46845440+pravali96@users.noreply.github.com> --- src/sagemaker/modules/image_builder.py | 67 ++++++++++++++++--- tests/integ/sagemaker/modules/Dockerfile | 5 ++ .../sagemaker/modules/test_image_builder.py | 17 +++-- 3 files changed, 76 insertions(+), 13 deletions(-) create mode 100644 tests/integ/sagemaker/modules/Dockerfile diff --git a/src/sagemaker/modules/image_builder.py b/src/sagemaker/modules/image_builder.py index e27bc7fba0..e7ded22184 100644 --- a/src/sagemaker/modules/image_builder.py +++ b/src/sagemaker/modules/image_builder.py @@ -81,7 +81,7 @@ """ -def build_image( +def build_image_from_base( image_name: str = "sm-custom-image", env_name: str = "sm_custom_env", deploy_to_ecr: bool = False, @@ -162,7 +162,7 @@ def build_image( return None -def capture_local_environment( +def build_image_from_local( image_name: str = "sm-local-capture", env_name: str = "saved_local_env", package_manager: str = "pip", @@ -285,13 +285,13 @@ def capture_local_environment( f.write(additional_requirements) logger.info("Merged requirements file saved to %s", requirement_txt_path) - if not base_image: - version = sys.version_info - base_image = f"python:{version.major}.{version.minor}.{version.micro}" - dockerfile_contents = PIP_DOCKERFILE_TEMPLATE.format( - base_image=base_image, - env_name=env_name, - ) + if not base_image: + version = sys.version_info + base_image = f"python:{version.major}.{version.minor}.{version.micro}" + dockerfile_contents = PIP_DOCKERFILE_TEMPLATE.format( + base_image=base_image, + env_name=env_name, + ) else: raise ValueError( @@ -306,6 +306,55 @@ def capture_local_environment( return None +def build_image_from_dockerfile( + image_name: str, + dockerfile: str, + deploy_to_ecr: bool = False, + ecr_repo_name: Optional[str] = None, + boto_session: Optional[boto3.Session] = None, + region: Optional[str] = None, +) -> Optional[str]: + """Build a Docker image with Dockerfile. + + Args: + image_name (str): The name of the docker image. + dockerfile (str): The file path to the Dockerfile. + deploy_to_ecr (bool): Whether to deploy the docker image to AWS ECR, defaults to False. + If set to True, the AWS credentials must be configured in the environment. + ecr_repo_name (Optional[str]): The AWS ECR repo to push the docker image. If not specified, + it will use image_name as the ECR repo name. This parameter is only valid when + deploy_to_ecr is True. + boto_session (Optional[boto3.Session]): The boto3 session with AWS account info. If not + provided, a new boto session will be created. + region (Optional[str]): The AWS region. + + Exceptions: + docker.errors.DockerException: Error while fetching server API version: + The docker engine is not running in your environment. + docker.errors.BuildError: The docker failed to build the image. The most likely reason is: + 1) Some packages are not supported in the base image. 2) There are dependency conflicts + between your local environment and additional dependencies. + botocore.exceptions.ClientError: AWS credentials are not configured. + """ + absolute_path = os.path.abspath(dockerfile) + directory = os.path.dirname(absolute_path) + + client = docker.from_env() + _, logs = client.images.build( + path=directory, + dockerfile=absolute_path, + rm=True, + tag=image_name, + ) + for log in logs: + logger.info(log.get("stream", "").strip()) + logger.info("Docker image %s built successfully", image_name) + + if deploy_to_ecr: + return _push_image_to_ecr(image_name, ecr_repo_name, boto_session, region) + return None + + def _merge_environment_ymls(env_name: str, env_file1: str, env_file2: str, output_file: str): """Merge two environment.yml files and save to a new environment.yml file. diff --git a/tests/integ/sagemaker/modules/Dockerfile b/tests/integ/sagemaker/modules/Dockerfile new file mode 100644 index 0000000000..4c978522df --- /dev/null +++ b/tests/integ/sagemaker/modules/Dockerfile @@ -0,0 +1,5 @@ +FROM python:3.12.4 + +WORKDIR /app + +RUN pip install --no-cache-dir numpy scipy sagemaker diff --git a/tests/integ/sagemaker/modules/test_image_builder.py b/tests/integ/sagemaker/modules/test_image_builder.py index 7e4b5de8fa..ac45f99569 100644 --- a/tests/integ/sagemaker/modules/test_image_builder.py +++ b/tests/integ/sagemaker/modules/test_image_builder.py @@ -15,24 +15,33 @@ import os -from sagemaker.modules.image_builder import build_image +from sagemaker.modules.image_builder import build_image_from_base, build_image_from_dockerfile def test_build_public_image_locally(): - build_image(image_name="python_310", base_image="python:3.10") + build_image_from_base(image_name="python_310", base_image="python:3.10") def test_build_with_dependency_file(): dependency_file_path = os.getcwd() + "/tests/integ/sagemaker/modules/requirements.txt" - build_image(image_name="ubuntu_with_dependencies", dependency_file=dependency_file_path) + build_image_from_base( + image_name="ubuntu_with_dependencies", dependency_file=dependency_file_path + ) def test_build_image_and_push_to_ecr(): dependency_file_path = os.getcwd() + "/tests/integ/sagemaker/modules/environment.yml" - build_image( + build_image_from_base( image_name="ecr_test_image", dependency_file=dependency_file_path, base_image="debian", deploy_to_ecr=True, ecr_repo_name="image_builder_integ_test", ) + + +def test_build_image_from_dockerfile(): + dockerfile_path = os.getcwd() + "/tests/integ/sagemaker/modules/Dockerfile" + build_image_from_dockerfile( + image_name="image_from_dockerfile", dockerfile=dockerfile_path, deploy_to_ecr=True + ) From 51fb427016907498342d7cbb49ac7dfaf3c476f9 Mon Sep 17 00:00:00 2001 From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Date: Wed, 13 Nov 2024 16:22:47 -0800 Subject: [PATCH 27/79] Use exact python path in trainer template (#1584) --- src/sagemaker/modules/templates.py | 18 +++++------ .../base_model_trainer.ipynb | 6 ++-- src/sagemaker/modules/train/model_trainer.py | 2 +- src/sagemaker/serve/builder/model_builder.py | 7 +++-- tests/integ/sagemaker/modules/conftest.py | 31 +++++++++---------- 5 files changed, 31 insertions(+), 33 deletions(-) diff --git a/src/sagemaker/modules/templates.py b/src/sagemaker/modules/templates.py index 0a8f6d1f12..7500b0fd30 100644 --- a/src/sagemaker/modules/templates.py +++ b/src/sagemaker/modules/templates.py @@ -50,18 +50,16 @@ }} check_python() {{ - if command -v python3 &>/dev/null; then - SM_PYTHON_CMD="python3" - SM_PIP_CMD="pip3" - echo "Found python3" - elif command -v python &>/dev/null; then - SM_PYTHON_CMD="python" - SM_PIP_CMD="pip" - echo "Found python" - else - echo "Python may not be installed" + SM_PYTHON_CMD=$(command -v python3 || command -v python) + SM_PIP_CMD=$(command -v pip3 || command -v pip) + + # Check if Python is found + if [[ -z "$SM_PYTHON_CMD" || -z "$SM_PIP_CMD" ]]; then + echo "Error: The Python executable was not found in the system path." return 1 fi + + return 0 }} trap 'handle_error' ERR diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb index b25fa20979..03cb75b5c9 100644 --- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb +++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb @@ -46,7 +46,7 @@ "metadata": {}, "outputs": [], "source": [ - "model_trainer.train()" + "model_trainer.train(wait=False)" ] }, { @@ -94,7 +94,7 @@ " environment=env_vars,\n", ")\n", "\n", - "model_trainer.train()" + "model_trainer.train(wait=False)" ] }, { @@ -137,7 +137,7 @@ "metadata": {}, "outputs": [], "source": [ - "model_trainer.train()" + "model_trainer.train(wait=False)" ] }, { diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py index 323f2242a8..0868b8c59b 100644 --- a/src/sagemaker/modules/train/model_trainer.py +++ b/src/sagemaker/modules/train/model_trainer.py @@ -293,7 +293,7 @@ def model_post_init(self, __context: Any): logger.warning("Session not provided. Using default Session.") if self.role is None: - self.role = get_execution_role() + self.role = get_execution_role(sagemaker_session=self.session) logger.warning(f"Role not provided. Using default role:\n{self.role}") if self.base_job_name is None: diff --git a/src/sagemaker/serve/builder/model_builder.py b/src/sagemaker/serve/builder/model_builder.py index ae3f2cac94..ce94648852 100644 --- a/src/sagemaker/serve/builder/model_builder.py +++ b/src/sagemaker/serve/builder/model_builder.py @@ -112,6 +112,7 @@ download_huggingface_model_metadata, ) from sagemaker.serve.validations.optimization import _validate_optimization_configuration +from sagemaker.modules.train import ModelTrainer logger = logging.getLogger(__name__) @@ -272,9 +273,9 @@ class ModelBuilder(Triton, DJL, JumpStart, TGI, Transformers, TensorflowServing, schema_builder: Optional[SchemaBuilder] = field( default=None, metadata={"help": "Defines the i/o schema of the model"} ) - model: Optional[Union[object, str, "ModelTrainer", TrainingJob, Estimator]] = field( + model: Optional[Union[object, str, ModelTrainer, TrainingJob, Estimator]] = field( default=None, - metadata={"help": "Define object from which training artifacts can be extracted"} + metadata={"help": "Define object from which training artifacts can be extracted"}, ) inference_spec: InferenceSpec = field( default=None, @@ -851,7 +852,7 @@ def build( # pylint: disable=R0911 Returns: Type[Model]: A deployable ``Model`` object. """ - from sagemaker.modules.train.model_trainer import ModelTrainer + self.modes = dict() if mode: diff --git a/tests/integ/sagemaker/modules/conftest.py b/tests/integ/sagemaker/modules/conftest.py index 270fe743b6..189d79167c 100644 --- a/tests/integ/sagemaker/modules/conftest.py +++ b/tests/integ/sagemaker/modules/conftest.py @@ -14,28 +14,27 @@ from __future__ import absolute_import import pytest -import json +import os import boto3 -from botocore.config import Config from sagemaker import Session DEFAULT_REGION = "us-west-2" + @pytest.fixture(scope="module") -def modules_boto_session(request): - config = request.config.getoption("--boto-config") - if config: - return boto3.Session(**json.loads(config)) +def modules_sagemaker_session(): + region = os.environ.get("AWS_DEFAULT_REGION") + if not region: + os.environ["AWS_DEFAULT_REGION"] = DEFAULT_REGION + region_manual_set = True else: - return boto3.Session(region_name=DEFAULT_REGION) + region_manual_set = True -@pytest.fixture(scope="module") -def modules_sagemaker_session(request, modules_boto_session): - sagemaker_client = ( - modules_boto_session.client( - "sagemaker", - config=Config(retries={"max_attempts": 10, "mode": "standard"}) - ) - ) - return Session(boto_session=modules_boto_session) + boto_session = boto3.Session(region_name=os.environ["AWS_DEFAULT_REGION"]) + sagemaker_session = Session(boto_session=boto_session) + + yield sagemaker_session + + if region_manual_set: + del os.environ["AWS_DEFAULT_REGION"] From 70ae24f6a2e393a5bea3936dded2fb28e1edbcec Mon Sep 17 00:00:00 2001 From: Gokul Anantha Narayanan <166456257+nargokul@users.noreply.github.com> Date: Thu, 14 Nov 2024 09:57:29 -0800 Subject: [PATCH 28/79] Unified Deployment interface in Model Builder (#1549) * Base model trainer (#1521) * Base model trainer * flake8 * add testing notebook * add param validation & set defaults * Implement simple train method * feature: support script mode with local train.sh (#1523) * feature: support script mode with local train.sh * Stop tracking train.sh and add it to .gitignore * update message * make dir if not exist * fix docs * fix: docstyle * Address comments * fix hyperparams * Revert pydantic custom error * pylint * Image Spec refactoring and updates (#1525) * Image Spec refactoring and updates * Unit tests and update function for Image Spec * Fix hugging face test * Fix Tests * Add unit tests for ModelTrainer (#1527) * Add unit tests for ModelTrainer * Flake8 * format * Add example notebook (#1528) * Add testing notebook * format * use smaller data * remove large dataset * update * pylint * flake8 * ignore docstyle in directories with test * format * format * Add enviornment variable bootstrapping script (#1530) * Add enviornment variables scripts * format * fix comment * add docstrings * fix comment * feature: add utility function to capture local snapshot (#1524) * local snapshot * Update pip list command * Remove function calls * Address comments * Address comments * Support intelligent parameters (#1540) * Support intelligent parameters * fix codestyle * Revert Image Spec (#1541) * Cleanup ModelTrainer (#1542) * Initial Prototype * General image builder (#1546) * General image builder * General image builder * Fix codestyle * Fix codestyle * Move location * Add warnings * Add integ tests * Fix integ test * Fix integ test * Fix region error * Add region * Unified deploying in ModelBuilder * Latest Container Image (#1545) * Latest Container Image * Test Fixes * Parameterized tests and some logic updates * Test fixes * Move to Image URI * Fixes for unit test * Fixes for unit test * Fix codestyle error checks * Address PR comments * Address Codestyle errors * Cleanup ModelTrainer code (#1552) * Black format * Codestyle changes * Codestyle changes * from __future__ import absolute_import * DocString formatting * Black formatting * Address PR comments * Noteboook changes and fixes * feat: add pre-processing and post-processing logic to inference_spec (#1560) * add pre-processing and post-processing logic to inference_spec * fix format * make accept_type and content_type optional * remove accept_type and content_type from pre/post processing * correct typo * Add Distributed Training Support Model Trainer (#1536) * Add path to set Additional Settings in ModelTrainer (#1555) * Checkstyle Fixes * Address PR comments * Fixes * Merge Fixes * Codestyle Fixes * Codestyle Fixes * Codestyle Fixes * Codestyle Fixes * Codestyle Fixes * Update Docstring --------- Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Co-authored-by: pintaoz-aws <167920275+pintaoz-aws@users.noreply.github.com> Co-authored-by: Pravali Uppugunduri <46845440+pravali96@users.noreply.github.com> --- src/sagemaker/batch_inference/__init__.py | 0 .../batch_transform_inference_config.py | 27 + .../base_model_builder_deploy.ipynb | 1585 +++++++++++++++++ src/sagemaker/serve/builder/model_builder.py | 118 +- 4 files changed, 1712 insertions(+), 18 deletions(-) create mode 100644 src/sagemaker/batch_inference/__init__.py create mode 100644 src/sagemaker/batch_inference/batch_transform_inference_config.py create mode 100644 src/sagemaker/modules/testing_notebooks/base_model_builder_deploy.ipynb diff --git a/src/sagemaker/batch_inference/__init__.py b/src/sagemaker/batch_inference/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/sagemaker/batch_inference/batch_transform_inference_config.py b/src/sagemaker/batch_inference/batch_transform_inference_config.py new file mode 100644 index 0000000000..3d3618d7fb --- /dev/null +++ b/src/sagemaker/batch_inference/batch_transform_inference_config.py @@ -0,0 +1,27 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at +# +# http://aws.amazon.com/apache2.0/ +# +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +"""Config Classes for taking in parameters for Batch Inference""" + +from __future__ import absolute_import +from pydantic import BaseModel + + +class BatchTransformInferenceConfig(BaseModel): + """Config class for Batch Transform Inference + + * Can be used to deploy from ModelBuilder + """ + + instance_count: int + instance_type: str + output_path: str diff --git a/src/sagemaker/modules/testing_notebooks/base_model_builder_deploy.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_builder_deploy.ipynb new file mode 100644 index 0000000000..7dd93b7b85 --- /dev/null +++ b/src/sagemaker/modules/testing_notebooks/base_model_builder_deploy.ipynb @@ -0,0 +1,1585 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## This Notebook is to showcase the deploy function of ModelBuilder. \n", + "\n", + "As ModelBuilder becomes the main interface for all inference related changes, this deploy function is one step further to provide an enhanced experience over Model.deploy(). \n", + "The deploy function mainly provides 2 advantages:\n", + "1. Simplification of parameters - Reducing the total parameters required from 22 to 7. \n", + "2. Validation/Error Handling with enum to make sure the deployment mode related parameters are provided in the call" + ], + "id": "270525e009204918" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Basic Setup", + "id": "bb8a9608ba668104" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-30T20:10:08.460832Z", + "start_time": "2024-10-30T20:10:03.204079Z" + } + }, + "cell_type": "code", + "source": [ + "from sagemaker import Session, get_execution_role\n", + "\n", + "sagemaker_session = Session()\n", + "role = get_execution_role()\n", + "region = sagemaker_session.boto_region_name\n", + "bucket = sagemaker_session.default_bucket()\n" + ], + "id": "a8fdb05f0164a7b6", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /Users/nargokul/Library/Application Support/sagemaker/config.yaml\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Create Training Job and Model Builder", + "id": "75baff9492b9c9a6" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-30T20:10:09.506486Z", + "start_time": "2024-10-30T20:10:08.468687Z" + } + }, + "cell_type": "code", + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import pandas as pd\n", + "\n", + "# Get IRIS Data\n", + "\n", + "iris = load_iris()\n", + "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)\n", + "iris_df['target'] = iris.target\n", + "\n", + "import os\n", + "\n", + "# Prepare Data\n", + "\n", + "os.makedirs('./data', exist_ok=True)\n", + "\n", + "iris_df = iris_df[['target'] + [col for col in iris_df.columns if col != 'target']]\n", + "\n", + "train_data, test_data = train_test_split(iris_df, test_size=0.2, random_state=42)\n", + "\n", + "train_data.to_csv('./data/train.csv', index=False, header=False)\n", + "test_data.to_csv('./data/test.csv', index=False, header=False)\n", + "\n", + "# Remove the target column from the testing data. We will use this to call invoke_endpoint later\n", + "test_data_no_target = test_data.drop('target', axis=1)\n", + "\n", + "prefix = \"DEMO-scikit-iris\"\n", + "TRAIN_DATA = \"train.csv\"\n", + "DATA_DIRECTORY = \"data\"\n", + "\n", + "train_input = sagemaker_session.upload_data(\n", + " DATA_DIRECTORY, bucket=bucket, key_prefix=\"{}/{}\".format(prefix, DATA_DIRECTORY)\n", + ")\n", + "\n", + "s3_input_path = \"s3://{}/{}/data/{}\".format(bucket, prefix, TRAIN_DATA)\n", + "s3_output_path = \"s3://{}/{}/output\".format(bucket, prefix)\n", + "\n", + "print(s3_input_path)\n", + "print(s3_output_path)" + ], + "id": "2d4ade0d9d11b649", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "s3://sagemaker-us-west-2-211125564141/DEMO-scikit-iris/data/train.csv\n", + "s3://sagemaker-us-west-2-211125564141/DEMO-scikit-iris/output\n" + ] + } + ], + "execution_count": 2 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-10-30T20:12:53.241670Z", + "start_time": "2024-10-30T20:10:09.640599Z" + } + }, + "cell_type": "code", + "source": [ + "from sagemaker_core.main.shapes import AlgorithmSpecification, Channel, DataSource, S3DataSource,OutputDataConfig, ResourceConfig, StoppingCondition\n", + "import uuid\n", + "from sagemaker.serve.builder.model_builder import ModelBuilder, BatchTransformInferenceConfig\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sagemaker.serve import InferenceSpec, SchemaBuilder\n", + "from sagemaker_core.main.resources import TrainingJob\n", + "from xgboost import XGBClassifier\n", + "\n", + "image = \"433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest\"\n", + "\n", + "class XGBoostSpec(InferenceSpec):\n", + " def load(self, model_dir: str):\n", + " print(model_dir)\n", + " model = XGBClassifier()\n", + " model.load_model(model_dir + \"/xgboost-model\")\n", + " return model\n", + "\n", + " def invoke(self, input_object: object, model: object):\n", + " prediction_probabilities = model.predict_proba(input_object)\n", + " predictions = np.argmax(prediction_probabilities, axis=1)\n", + " return predictions\n", + " \n", + "data = {\n", + " 'Name': ['Alice', 'Bob', 'Charlie']\n", + "}\n", + "df = pd.DataFrame(data)\n", + "training_job_name = str(uuid.uuid4())\n", + "schema_builder = SchemaBuilder(sample_input=df, sample_output=df)\n", + "\n", + "training_job = TrainingJob.create(\n", + " training_job_name=training_job_name,\n", + " hyper_parameters={\n", + " 'objective': 'multi:softmax',\n", + " 'num_class': '3',\n", + " 'num_round': '10',\n", + " 'eval_metric': 'merror'\n", + " },\n", + " algorithm_specification=AlgorithmSpecification(\n", + " training_image=image,\n", + " training_input_mode='File'\n", + " ),\n", + " role_arn=role,\n", + " input_data_config=[\n", + " Channel(\n", + " channel_name='train',\n", + " content_type='csv',\n", + " compression_type='None',\n", + " record_wrapper_type='None',\n", + " data_source=DataSource(\n", + " s3_data_source=S3DataSource(\n", + " s3_data_type='S3Prefix',\n", + " s3_uri=s3_input_path,\n", + " s3_data_distribution_type='FullyReplicated'\n", + " )\n", + " )\n", + " )\n", + " ],\n", + " output_data_config=OutputDataConfig(\n", + " s3_output_path=s3_output_path\n", + " ),\n", + " resource_config=ResourceConfig(\n", + " instance_type='ml.m4.xlarge',\n", + " instance_count=1,\n", + " volume_size_in_gb=30\n", + " ),\n", + " stopping_condition=StoppingCondition(\n", + " max_runtime_in_seconds=600\n", + " )\n", + ")\n", + "training_job.wait()\n", + "\n", + "model_builder = ModelBuilder(\n", + " model_path=training_job.model_artifacts.s3_model_artifacts,\n", + " role_arn=role,\n", + " inference_spec=XGBoostSpec(),\n", + " image_uri=image,\n", + " schema_builder=schema_builder,\n", + " instance_type=\"ml.c6i.xlarge\"\n", + ")\n", + "model_builder.build()\n" + ], + "id": "cdda06c2b848ce94", + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Library/Python/3.9/site-packages/pydantic/_internal/_fields.py:200: UserWarning: Field name \"json\" in \"MonitoringDatasetFormat\" shadows an attribute in parent \"Base\"\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "\u001B[2;36m[10/30/24 13:10:11]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO \u001B[0m Creating training_job resource. \u001B]8;id=847076;file:///Library/Python/3.9/site-packages/sagemaker_core/main/resources.py\u001B\\\u001B[2mresources.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=443564;file:///Library/Python/3.9/site-packages/sagemaker_core/main/resources.py#26309\u001B\\\u001B[2m26309\u001B[0m\u001B]8;;\u001B\\\n" + ], + "text/html": [ + "

[10/30/24 13:10:11] INFO     Creating training_job resource.                                     resources.py:26309\n",
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+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ModelBuilder: INFO:     Either inference spec or model is provided. ModelBuilder is not handling MLflow model input\n",
+      "ModelBuilder: INFO:     Skipping auto detection as the image uri is provided 433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### Real Time Deployment",
+   "id": "b0d045e8f64e3787"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-30T20:16:00.777140Z",
+     "start_time": "2024-10-30T20:12:53.258703Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "real_time_predictor = model_builder.deploy(\n",
+    "    initial_instance_count=1)"
+   ],
+   "id": "96bb41b4146549dd",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ModelBuilder: INFO:     ModelBuilder will collect telemetry to help us better understand our user's needs, diagnose issues, and deliver additional features. To opt out of telemetry, please disable via TelemetryOptOut in intelligent defaults. See https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk for more info.\n"
+     ]
+    },
+    {
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+       "\u001B[2;36m                    \u001B[0m         : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been   \u001B[2m               \u001B[0m\n",
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+       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[4;38;2;0;105;255mhttps://github.com/gevent/gevent/issues/1016.\u001B[0m Modules that had direct  \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         imports \u001B[1m(\u001B[0mNOT patched\u001B[1m)\u001B[0m: \u001B[1m[\u001B[0m\u001B[38;2;0;135;0m'urllib3.util \u001B[0m                                 \u001B[2m               \u001B[0m\n",
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+       "\u001B[2;36m                    \u001B[0m         \u001B[38;2;0;135;0m'urllib3.util.ssl_ \u001B[0m                                                    \u001B[2m               \u001B[0m\n",
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+       "\u001B[2;36m                    \u001B[0m           \u001B[1;38;2;225;0;225mmonkey.patch_all\u001B[0m\u001B[1m(\u001B[0m\u001B[38;2;215;175;0msubprocess\u001B[0m=\u001B[3;38;2;0;135;0mTrue\u001B[0m\u001B[1m)\u001B[0m                                    \u001B[2m               \u001B[0m\n"
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+      "text/html": [
+       "
                    INFO     /opt/amazon/lib/python3.7/site-packages/gunicorn/workers/ggevent.py:65 session.py:8308\n",
+       "                             : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been                  \n",
+       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
+       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
+       "                             monkey-patch earlier. See                                                             \n",
+       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
+       "                             imports (NOT patched): ['urllib3.util                                                 \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py)',                  \n",
+       "                             'urllib3.util.ssl_                                                                    \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py)'].                     \n",
+       "                               monkey.patch_all(subprocess=True)                                                   \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+      "text/html": [
+       "
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+       "
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+       "\u001B[2;36m                    \u001B[0m         : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been   \u001B[2m               \u001B[0m\n",
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+       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[4;38;2;0;105;255mhttps://github.com/gevent/gevent/issues/1016.\u001B[0m Modules that had direct  \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         imports \u001B[1m(\u001B[0mNOT patched\u001B[1m)\u001B[0m: \u001B[1m[\u001B[0m\u001B[38;2;0;135;0m'urllib3.util \u001B[0m                                 \u001B[2m               \u001B[0m\n",
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+       "\u001B[2;36m                    \u001B[0m           \u001B[1;38;2;225;0;225mmonkey.patch_all\u001B[0m\u001B[1m(\u001B[0m\u001B[38;2;215;175;0msubprocess\u001B[0m=\u001B[3;38;2;0;135;0mTrue\u001B[0m\u001B[1m)\u001B[0m                                    \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
                    INFO     /opt/amazon/lib/python3.7/site-packages/gunicorn/workers/ggevent.py:65 session.py:8308\n",
+       "                             : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been                  \n",
+       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
+       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
+       "                             monkey-patch earlier. See                                                             \n",
+       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
+       "                             imports (NOT patched): ['urllib3.util                                                 \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py)',                  \n",
+       "                             'urllib3.util.ssl_                                                                    \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py)'].                     \n",
+       "                               monkey.patch_all(subprocess=True)                                                   \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-30:20:15:17:INFO\u001B[1m]\u001B[0m Model loaded successfully for worker : \u001B[1;36m23\u001B[0m   \u001B]8;id=693683;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=319507;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
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+      "text/html": [
+       "
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+       "
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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-\u001B[1;36m30\u001B[0m 20:15:17 +\u001B[1;36m0000\u001B[0m\u001B[1m]\u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m24\u001B[0m\u001B[1m]\u001B[0m \u001B[1m[\u001B[0mINFO\u001B[1m]\u001B[0m Booting worker with pid: \u001B[1;36m24\u001B[0m    \u001B]8;id=6848;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=288255;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
                    INFO     [2024-10-30 20:15:17 +0000] [24] [INFO] Booting worker with pid: 24    session.py:8308\n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "\u001B[2;36m                    \u001B[0m         : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         imported may lead to errors, including RecursionError on Python \u001B[1;36m3.6\u001B[0m.   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         It may also silently lead to incorrect behaviour on Python \u001B[1;36m3.7\u001B[0m. Please \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[4;38;2;0;105;255mhttps://github.com/gevent/gevent/issues/1016.\u001B[0m Modules that had direct  \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         imports \u001B[1m(\u001B[0mNOT patched\u001B[1m)\u001B[0m: \u001B[1m[\u001B[0m\u001B[38;2;0;135;0m'urllib3.util \u001B[0m                                 \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[1;38;2;0;135;0m(\u001B[0m\u001B[38;2;0;135;0m/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py\u001B[0m\u001B[1;38;2;0;135;0m)\u001B[0m\u001B[38;2;0;135;0m'\u001B[0m,   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[38;2;0;135;0m'urllib3.util.ssl_ \u001B[0m                                                    \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[1;38;2;0;135;0m(\u001B[0m\u001B[38;2;0;135;0m/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py\u001B[0m\u001B[1;38;2;0;135;0m)\u001B[0m\u001B[38;2;0;135;0m'\u001B[0m\u001B[1m]\u001B[0m.      \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m           \u001B[1;38;2;225;0;225mmonkey.patch_all\u001B[0m\u001B[1m(\u001B[0m\u001B[38;2;215;175;0msubprocess\u001B[0m=\u001B[3;38;2;0;135;0mTrue\u001B[0m\u001B[1m)\u001B[0m                                    \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
                    INFO     /opt/amazon/lib/python3.7/site-packages/gunicorn/workers/ggevent.py:65 session.py:8308\n",
+       "                             : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been                  \n",
+       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
+       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
+       "                             monkey-patch earlier. See                                                             \n",
+       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
+       "                             imports (NOT patched): ['urllib3.util                                                 \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py)',                  \n",
+       "                             'urllib3.util.ssl_                                                                    \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py)'].                     \n",
+       "                               monkey.patch_all(subprocess=True)                                                   \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-30:20:15:17:INFO\u001B[1m]\u001B[0m Model loaded successfully for worker : \u001B[1;36m24\u001B[0m   \u001B]8;id=70291;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=909473;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
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+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:15:59]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Created endpoint with name                                             \u001B]8;id=778192;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=467032;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8294\u001B\\\u001B[2m8294\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-12-54-372        \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
[10/30/24 13:15:59] INFO     Created endpoint with name                                             session.py:8294\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-12-54-372                       \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ModelBuilder: DEBUG:     ModelBuilder metrics emitted.\n"
+     ]
+    }
+   ],
+   "execution_count": 4
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### Serverless Deployment",
+   "id": "5104c00db40527fd"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-30T20:18:15.929714Z",
+     "start_time": "2024-10-30T20:16:00.806356Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig\n",
+    "\n",
+    "serverless_predictor = model_builder.deploy(\n",
+    "    inference_config=ServerlessInferenceConfig())"
+   ],
+   "id": "9a5de6c58da465f9",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ModelBuilder: INFO:     ModelBuilder will collect telemetry to help us better understand our user's needs, diagnose issues, and deliver additional features. To opt out of telemetry, please disable via TelemetryOptOut in intelligent defaults. See https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk for more info.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:16:00]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating model with name: model-name-f76fb1fa96fa11efb5372ad633a99cdc  \u001B]8;id=245422;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=544458;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#4025\u001B\\\u001B[2m4025\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
[10/30/24 13:16:00] INFO     Creating model with name: model-name-f76fb1fa96fa11efb5372ad633a99cdc  session.py:4025\n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:16:01]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;215;175;0mWARNING \u001B[0m Using already existing model:                                          \u001B]8;id=30512;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=969586;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#4036\u001B\\\u001B[2m4036\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a99cdc                            \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
[10/30/24 13:16:01] WARNING  Using already existing model:                                          session.py:4036\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a99cdc                                           \n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+     "data": {
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+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating endpoint-config with name                                     \u001B]8;id=503479;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=809762;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#5817\u001B\\\u001B[2m5817\u001B[0m\u001B]8;;\u001B\\\n",
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+      ],
+      "text/html": [
+       "
                    INFO     Creating endpoint-config with name                                     session.py:5817\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-16-01-492                       \n",
+       "
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+       "
[10/30/24 13:16:02] INFO     Creating endpoint with name                                            session.py:4639\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-16-01-492                       \n",
+       "
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+      ],
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+       "
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+       "
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+     "output_type": "display_data"
+    },
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+      ],
+      "text/html": [
+       "
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+       "                             http://0.0.0.0:8080 (15)                                                              \n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
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+      ],
+      "text/html": [
+       "
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
+     "data": {
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+      ],
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+       "
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+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      ],
+      "text/html": [
+       "
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+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "\u001B[2;36m                    \u001B[0m         : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         imported may lead to errors, including RecursionError on Python \u001B[1;36m3.6\u001B[0m.   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         It may also silently lead to incorrect behaviour on Python \u001B[1;36m3.7\u001B[0m. Please \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[4;38;2;0;105;255mhttps://github.com/gevent/gevent/issues/1016.\u001B[0m Modules that had direct  \u001B[2m               \u001B[0m\n",
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+       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
+       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
+       "                             monkey-patch earlier. See                                                             \n",
+       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
+       "                             imports (NOT patched): ['urllib3.util                                                 \n",
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+       "                             'urllib3.util.ssl_                                                                    \n",
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+      ],
+      "text/html": [
+       "
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+       "\u001B[2;36m                    \u001B[0m         : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         imported may lead to errors, including RecursionError on Python \u001B[1;36m3.6\u001B[0m.   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         It may also silently lead to incorrect behaviour on Python \u001B[1;36m3.7\u001B[0m. Please \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[4;38;2;0;105;255mhttps://github.com/gevent/gevent/issues/1016.\u001B[0m Modules that had direct  \u001B[2m               \u001B[0m\n",
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                    INFO     /opt/amazon/lib/python3.7/site-packages/gunicorn/workers/ggevent.py:65 session.py:8308\n",
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+       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
+       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
+       "                             monkey-patch earlier. See                                                             \n",
+       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
+       "                             imports (NOT patched): ['urllib3.util                                                 \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py)',                  \n",
+       "                             'urllib3.util.ssl_                                                                    \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py)'].                     \n",
+       "
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+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m   \u001B[1;38;2;225;0;225mmonkey.patch_all\u001B[0m\u001B[1m(\u001B[0m\u001B[38;2;215;175;0msubprocess\u001B[0m=\u001B[3;38;2;0;135;0mTrue\u001B[0m\u001B[1m)\u001B[0m                                    \u001B]8;id=382247;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=161965;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
                    INFO       monkey.patch_all(subprocess=True)                                    session.py:8308\n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-30:20:17:57:INFO\u001B[1m]\u001B[0m Model loaded successfully for worker : \u001B[1;36m31\u001B[0m   \u001B]8;id=415016;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=24361;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
                    INFO     [2024-10-30:20:17:57:INFO] Model loaded successfully for worker : 31   session.py:8308\n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-30:20:17:57:INFO\u001B[1m]\u001B[0m Model loaded successfully for worker : \u001B[1;36m30\u001B[0m   \u001B]8;id=927407;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=387335;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
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+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:18:14]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Created endpoint with name                                             \u001B]8;id=549964;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=34785;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8294\u001B\\\u001B[2m8294\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-16-01-492        \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
[10/30/24 13:18:14] INFO     Created endpoint with name                                             session.py:8294\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-16-01-492                       \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ModelBuilder: DEBUG:     ModelBuilder metrics emitted.\n"
+     ]
+    }
+   ],
+   "execution_count": 5
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### Async Deployment",
+   "id": "6462fe2d8f85dba7"
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-10-30T20:21:27.809852Z",
+     "start_time": "2024-10-30T20:18:15.955152Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from sagemaker.s3_utils import s3_path_join\n",
+    "from sagemaker.async_inference import AsyncInferenceConfig\n",
+    "\n",
+    "async_predictor = model_builder.deploy(\n",
+    "    inference_config=AsyncInferenceConfig(\n",
+    "        output_path=s3_path_join(\"s3://\", bucket, \"async_inference/output\")),\n",
+    ")"
+   ],
+   "id": "271e8dcfb015d69a",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ModelBuilder: INFO:     ModelBuilder will collect telemetry to help us better understand our user's needs, diagnose issues, and deliver additional features. To opt out of telemetry, please disable via TelemetryOptOut in intelligent defaults. See https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk for more info.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:18:15]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating model with name: model-name-f76fb1fa96fa11efb5372ad633a99cdc  \u001B]8;id=96650;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=60711;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#4025\u001B\\\u001B[2m4025\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
[10/30/24 13:18:15] INFO     Creating model with name: model-name-f76fb1fa96fa11efb5372ad633a99cdc  session.py:4025\n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:18:16]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;215;175;0mWARNING \u001B[0m Using already existing model:                                          \u001B]8;id=492060;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=45096;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#4036\u001B\\\u001B[2m4036\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a99cdc                            \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
[10/30/24 13:18:16] WARNING  Using already existing model:                                          session.py:4036\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a99cdc                                           \n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:18:19]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating endpoint-config with name                                     \u001B]8;id=21634;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=59349;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#5817\u001B\\\u001B[2m5817\u001B[0m\u001B]8;;\u001B\\\n",
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+      ],
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+       "
[10/30/24 13:18:19] INFO     Creating endpoint-config with name                                     session.py:5817\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-18-16-602                       \n",
+       "
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+      ]
+     },
+     "metadata": {},
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+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:18:20]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating endpoint with name                                            \u001B]8;id=747862;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=622744;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#4639\u001B\\\u001B[2m4639\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-18-16-602        \u001B[2m               \u001B[0m\n"
+      ],
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+       "
[10/30/24 13:18:20] INFO     Creating endpoint with name                                            session.py:4639\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-18-16-602                       \n",
+       "
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+       "\u001B[2;36m[10/30/24 13:21:04]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Arguments: serve                                                       \u001B]8;id=543297;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=668264;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
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+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-\u001B[1;36m30\u001B[0m 20:20:51 +\u001B[1;36m0000\u001B[0m\u001B[1m]\u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1m]\u001B[0m \u001B[1m[\u001B[0mINFO\u001B[1m]\u001B[0m Starting gunicorn \u001B[1;36m19.9\u001B[0m.\u001B[1;36m0\u001B[0m        \u001B]8;id=652057;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=107097;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
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+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
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+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-\u001B[1;36m30\u001B[0m 20:20:51 +\u001B[1;36m0000\u001B[0m\u001B[1m]\u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1m]\u001B[0m \u001B[1m[\u001B[0mINFO\u001B[1m]\u001B[0m Listening at:                   \u001B]8;id=775956;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=797582;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[4;38;2;0;105;255mhttp://0.0.0.0:8080\u001B[0m \u001B[1m(\u001B[0m\u001B[1;36m1\u001B[0m\u001B[1m)\u001B[0m                                                \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
                    INFO     [2024-10-30 20:20:51 +0000] [1] [INFO] Listening at:                   session.py:8308\n",
+       "                             http://0.0.0.0:8080 (1)                                                               \n",
+       "
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
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+       "                             monkey-patch earlier. See                                                             \n",
+       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
+       "                             imports (NOT patched): ['urllib3.util                                                 \n",
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+       "                             'urllib3.util.ssl_                                                                    \n",
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+       "\u001B[2;36m                    \u001B[0m           \u001B[1;38;2;225;0;225mmonkey.patch_all\u001B[0m\u001B[1m(\u001B[0m\u001B[38;2;215;175;0msubprocess\u001B[0m=\u001B[3;38;2;0;135;0mTrue\u001B[0m\u001B[1m)\u001B[0m                                    \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
                    INFO     /opt/amazon/lib/python3.7/site-packages/gunicorn/workers/ggevent.py:65 session.py:8308\n",
+       "                             : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been                  \n",
+       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
+       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
+       "                             monkey-patch earlier. See                                                             \n",
+       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
+       "                             imports (NOT patched): ['urllib3.util                                                 \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py)',                  \n",
+       "                             'urllib3.util.ssl_                                                                    \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py)'].                     \n",
+       "                               monkey.patch_all(subprocess=True)                                                   \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-30:20:20:51:INFO\u001B[1m]\u001B[0m Model loaded successfully for worker : \u001B[1;36m23\u001B[0m   \u001B]8;id=892101;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=519664;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
                    INFO     [2024-10-30:20:20:51:INFO] Model loaded successfully for worker : 23   session.py:8308\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-\u001B[1;36m30\u001B[0m 20:20:51 +\u001B[1;36m0000\u001B[0m\u001B[1m]\u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m24\u001B[0m\u001B[1m]\u001B[0m \u001B[1m[\u001B[0mINFO\u001B[1m]\u001B[0m Booting worker with pid: \u001B[1;36m24\u001B[0m    \u001B]8;id=564172;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=217009;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
                    INFO     [2024-10-30 20:20:51 +0000] [24] [INFO] Booting worker with pid: 24    session.py:8308\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[38;2;225;0;225m/opt/amazon/lib/python3.7/site-packages/gunicorn/workers/\u001B[0m\u001B[38;2;225;0;225mggevent.py\u001B[0m:\u001B[1;36m65\u001B[0m \u001B]8;id=669354;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=873117;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         imported may lead to errors, including RecursionError on Python \u001B[1;36m3.6\u001B[0m.   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         It may also silently lead to incorrect behaviour on Python \u001B[1;36m3.7\u001B[0m. Please \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[4;38;2;0;105;255mhttps://github.com/gevent/gevent/issues/1016.\u001B[0m Modules that had direct  \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         imports \u001B[1m(\u001B[0mNOT patched\u001B[1m)\u001B[0m: \u001B[1m[\u001B[0m\u001B[38;2;0;135;0m'urllib3.util \u001B[0m                                 \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[1;38;2;0;135;0m(\u001B[0m\u001B[38;2;0;135;0m/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py\u001B[0m\u001B[1;38;2;0;135;0m)\u001B[0m\u001B[38;2;0;135;0m'\u001B[0m,   \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[38;2;0;135;0m'urllib3.util.ssl_ \u001B[0m                                                    \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m         \u001B[1;38;2;0;135;0m(\u001B[0m\u001B[38;2;0;135;0m/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py\u001B[0m\u001B[1;38;2;0;135;0m)\u001B[0m\u001B[38;2;0;135;0m'\u001B[0m\u001B[1m]\u001B[0m.      \u001B[2m               \u001B[0m\n",
+       "\u001B[2;36m                    \u001B[0m           \u001B[1;38;2;225;0;225mmonkey.patch_all\u001B[0m\u001B[1m(\u001B[0m\u001B[38;2;215;175;0msubprocess\u001B[0m=\u001B[3;38;2;0;135;0mTrue\u001B[0m\u001B[1m)\u001B[0m                                    \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
                    INFO     /opt/amazon/lib/python3.7/site-packages/gunicorn/workers/ggevent.py:65 session.py:8308\n",
+       "                             : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been                  \n",
+       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
+       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
+       "                             monkey-patch earlier. See                                                             \n",
+       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
+       "                             imports (NOT patched): ['urllib3.util                                                 \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py)',                  \n",
+       "                             'urllib3.util.ssl_                                                                    \n",
+       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py)'].                     \n",
+       "                               monkey.patch_all(subprocess=True)                                                   \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m \u001B[1m[\u001B[0m\u001B[1;36m2024\u001B[0m-\u001B[1;36m10\u001B[0m-30:20:20:51:INFO\u001B[1m]\u001B[0m Model loaded successfully for worker : \u001B[1;36m24\u001B[0m   \u001B]8;id=901867;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=827841;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8308\u001B\\\u001B[2m8308\u001B[0m\u001B]8;;\u001B\\\n"
+      ],
+      "text/html": [
+       "
                    INFO     [2024-10-30:20:20:51:INFO] Model loaded successfully for worker : 24   session.py:8308\n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:21:26]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Created endpoint with name                                             \u001B]8;id=56717;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=391476;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#8294\u001B\\\u001B[2m8294\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-18-16-602        \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
[10/30/24 13:21:26] INFO     Created endpoint with name                                             session.py:8294\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-18-16-602                       \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ModelBuilder: DEBUG:     ModelBuilder metrics emitted.\n"
+     ]
+    }
+   ],
+   "execution_count": 6
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": "#### Batch Deployment",
+   "id": "f8458db5bfb1a0c5"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "from sagemaker.s3_utils import s3_path_join\n",
+    "\n",
+    "\n",
+    "batch_predictor = model_builder.deploy(\n",
+    "    initial_instance_count=1,\n",
+    "    inference_config=BatchTransformInferenceConfig(\n",
+    "        instance_count=1,\n",
+    "        instance_type='ml.m5.large',\n",
+    "        output_path=s3_path_join(\"s3://\", bucket, \"async_inference/output\"),\n",
+    "    )\n",
+    ")"
+   ],
+   "id": "5a80ccf640a5dca9",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "#### MultiModel Deployment\n",
+    "\n",
+    "Similar syntax applies for Multi-Container Endpoint as well"
+   ],
+   "id": "d97c14830180dd4"
+  },
+  {
+   "metadata": {
+    "jupyter": {
+     "is_executing": true
+    },
+    "ExecuteTime": {
+     "start_time": "2024-10-30T20:30:57.436543Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "from sagemaker.compute_resource_requirements import ResourceRequirements\n",
+    "\n",
+    "multi_model_predictor = model_builder.deploy(\n",
+    "    inference_config = ResourceRequirements(\n",
+    "        requests={\n",
+    "            \"num_cpus\": 0.5,\n",
+    "            \"memory\": 512,\n",
+    "            \"copies\": 2,\n",
+    "        },\n",
+    "        limits={},\n",
+    "    ),\n",
+    "    initial_instance_count=1)"
+   ],
+   "id": "9788d4f7fab3b7ca",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ModelBuilder: INFO:     ModelBuilder will collect telemetry to help us better understand our user's needs, diagnose issues, and deliver additional features. To opt out of telemetry, please disable via TelemetryOptOut in intelligent defaults. See https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk for more info.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m[10/30/24 13:30:58]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating endpoint-config with name                                     \u001B]8;id=987242;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=233313;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#5817\u001B\\\u001B[2m5817\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-30-57-438        \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
[10/30/24 13:30:58] INFO     Creating endpoint-config with name                                     session.py:5817\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-30-57-438                       \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating endpoint with name                                            \u001B]8;id=10549;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=757746;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#4639\u001B\\\u001B[2m4639\u001B[0m\u001B]8;;\u001B\\\n",
+       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-30-57-438        \u001B[2m               \u001B[0m\n"
+      ],
+      "text/html": [
+       "
                    INFO     Creating endpoint with name                                            session.py:4639\n",
+       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-30-57-438                       \n",
+       "
\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "724aec7045fd80f1"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/sagemaker/serve/builder/model_builder.py b/src/sagemaker/serve/builder/model_builder.py
index ce94648852..14b140cb6d 100644
--- a/src/sagemaker/serve/builder/model_builder.py
+++ b/src/sagemaker/serve/builder/model_builder.py
@@ -26,13 +26,16 @@
 
 from sagemaker_core.main.resources import TrainingJob
 
+from sagemaker.transformer import Transformer
+from sagemaker.async_inference import AsyncInferenceConfig
+from sagemaker.batch_inference.batch_transform_inference_config import BatchTransformInferenceConfig
+from sagemaker.compute_resource_requirements import ResourceRequirements
+from sagemaker.enums import Tag, EndpointType
 from sagemaker.estimator import Estimator
-from sagemaker.enums import Tag
 from sagemaker.jumpstart.accessors import JumpStartS3PayloadAccessor
 from sagemaker.jumpstart.utils import get_jumpstart_content_bucket
 from sagemaker.s3 import S3Downloader
-
-from sagemaker import Session
+from sagemaker import Session, utils
 from sagemaker.model import Model
 from sagemaker.base_predictor import PredictorBase
 from sagemaker.serializers import NumpySerializer, TorchTensorSerializer
@@ -105,6 +108,7 @@
 from sagemaker.serve.validations.check_image_and_hardware_type import (
     validate_image_uri_and_hardware,
 )
+from sagemaker.serverless import ServerlessInferenceConfig
 from sagemaker.utils import Tags
 from sagemaker.workflow.entities import PipelineVariable
 from sagemaker.huggingface.llm_utils import (
@@ -593,12 +597,8 @@ def _model_builder_deploy_wrapper(
             return predictor
         if self.mode == Mode.SAGEMAKER_ENDPOINT:
             # Validate parameters
-            if not instance_type:
-                raise ValueError("Missing required parameter `instance_type`")
-
-            if not initial_instance_count:
-                raise ValueError("Missing required parameter `initial_instance_count`")
-
+            # Instance type and instance count parameter validation is done based on deployment type
+            # and will be done inside Model.deploy()
             if is_1p_image_uri(image_uri=self.image_uri):
                 validate_image_uri_and_hardware(
                     image_uri=self.image_uri,
@@ -660,8 +660,8 @@ def _build_for_torchserve(self) -> Type[Model]:
         )
 
         self._prepare_for_mode()
-
-        return self._create_model()
+        self.model = self._create_model()
+        return self.model
 
     def _user_agent_decorator(self, func):
         """Placeholder docstring"""
@@ -901,13 +901,15 @@ def build(  # pylint: disable=R0911
         if (
             not (isinstance(self.model, str) and self._is_jumpstart_model_id())
         ) and self.model_server:
-            return self._build_for_model_server()
+            self.built_model = self._build_for_model_server()
+            return self.built_model
 
         if isinstance(self.model, str):
             model_task = None
             if self._is_jumpstart_model_id() or self._use_jumpstart_equivalent():
                 self.model_hub = ModelHub.JUMPSTART
-                return self._build_for_jumpstart()
+                self.built_model = self._build_for_jumpstart()
+                return self.built_model
             self.model_hub = ModelHub.HUGGINGFACE
 
             if self.model_metadata:
@@ -925,18 +927,23 @@ def build(  # pylint: disable=R0911
                 if self.schema_builder is None and model_task is not None:
                     self._hf_schema_builder_init(model_task)
                 if model_task == "text-generation":
-                    return self._build_for_tgi()
+                    self.built_model = self._build_for_tgi()
+                    return self.built_model
                 if model_task == "sentence-similarity":
-                    return self._build_for_tei()
+                    self.built_model = self._build_for_tei()
+                    return self.built_model
                 elif self._can_fit_on_single_gpu():
-                    return self._build_for_transformers()
+                    self.built_model = self._build_for_transformers()
+                    return self.built_model
                 else:
-                    return self._build_for_transformers()
+                    self.built_model = self._build_for_transformers()
+                    return self.built_model
 
         # Set TorchServe as default model server
         if not self.model_server:
             self.model_server = ModelServer.TORCHSERVE
-            return self._build_for_torchserve()
+            self.built_model = self._build_for_torchserve()
+            return self.built_model
 
         raise ValueError("%s model server is not supported" % self.model_server)
 
@@ -1546,6 +1553,81 @@ def _optimize_prepare_for_hf(self):
         )
         self.pysdk_model.env.update(env)
 
+    def deploy(
+        self,
+        endpoint_name: str = None,
+        initial_instance_count: Optional[int] = 1,
+        inference_config: Optional[
+            Union[
+                ServerlessInferenceConfig,
+                AsyncInferenceConfig,
+                BatchTransformInferenceConfig,
+                ResourceRequirements,
+            ]
+        ] = None,
+    ) -> Union[Predictor, Transformer]:
+        """Deploys the built Model.
+
+        Depending on the type of config provided, this function will call deployment accordingly.
+        Args:
+            endpoint_name (str): Name of the endpoint to deploy.
+             The supplied base name is used as a prefix and
+             a unique ID is appended to guarantee uniqueness.
+            initial_instance_count (int): Number of instances to deploy.
+            inference_config (Optional[Union[ServerlessInferenceConfig,
+               AsyncInferenceConfig, BatchTransformInferenceConfig, ResourceRequirements]]) :
+                Additional Config for different deployment types such as
+                serverless, async, batch and multi-model/container
+        Returns:
+            Transformer for Batch Deployments
+            Predictors for all others
+        """
+        if not hasattr(self, "built_model"):
+            raise ValueError("Model Needs to be built before deploying")
+        endpoint_name = utils.unique_name_from_base(endpoint_name)
+        if not inference_config:  # Real-time Deployment
+            return self.built_model.deploy(
+                instance_type=self.instance_type,
+                initial_instance_count=initial_instance_count,
+                endpoint_name=endpoint_name,
+            )
+
+        if isinstance(inference_config, ServerlessInferenceConfig):
+            return self.built_model.deploy(
+                serverless_inference_config=inference_config,
+                endpoint_name=endpoint_name,
+            )
+
+        if isinstance(inference_config, AsyncInferenceConfig):
+            return self.built_model.deploy(
+                instance_type=self.instance_type,
+                initial_instance_count=initial_instance_count,
+                async_inference_config=inference_config,
+                endpoint_name=endpoint_name,
+            )
+
+        if isinstance(inference_config, BatchTransformInferenceConfig):
+            transformer = self.built_model.transformer(
+                instance_type=inference_config.instance_type,
+                output_path=inference_config.output_path,
+                instance_count=inference_config.instance_count,
+            )
+            transformer.wait()
+            return transformer
+
+        if isinstance(inference_config, ResourceRequirements):
+            # Multi Model and MultiContainer endpoints with Inference Component
+            return self.built_model.deploy(
+                instance_type=self.instance_type,
+                mode=Mode.SAGEMAKER_ENDPOINT,
+                endpoint_type=EndpointType.INFERENCE_COMPONENT_BASED,
+                resources=inference_config,
+                initial_instance_count=initial_instance_count,
+                role=self.role_arn,
+            )
+
+        raise ValueError("Deployment Options not supported")
+
     def display_benchmark_metrics(self, **kwargs):
         """Display Markdown Benchmark Metrics for deployment configs."""
         if not isinstance(self.model, str):

From 6b90f89b689ccc696a8976b380a2137ff179bd6e Mon Sep 17 00:00:00 2001
From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com>
Date: Thu, 14 Nov 2024 10:14:12 -0800
Subject: [PATCH 29/79] Add recipes examples (#1582)

---
 .../base_model_trainer.ipynb                  | 155 ++++++++++++++++--
 .../recipes/custom-recipe.yaml                | 110 +++++++++++++
 src/sagemaker/modules/train/model_trainer.py  |   3 +-
 3 files changed, 251 insertions(+), 17 deletions(-)
 create mode 100644 src/sagemaker/modules/testing_notebooks/recipes/custom-recipe.yaml

diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
index 03cb75b5c9..6c1025c152 100644
--- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
+++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
@@ -79,19 +79,9 @@
     "    command=\"python custom_script.py\",\n",
     ")\n",
     "\n",
-    "hyperparameters = {\n",
-    "    \"secret_token\": \"123456\",\n",
-    "}\n",
-    "\n",
-    "env_vars = {\n",
-    "    \"PASSWORD\": \"123456\"\n",
-    "}\n",
-    "\n",
     "model_trainer = ModelTrainer(\n",
     "    training_image=pytorch_image,\n",
     "    source_code=source_code,\n",
-    "    hyperparameters=hyperparameters,\n",
-    "    environment=env_vars,\n",
     ")\n",
     "\n",
     "model_trainer.train(wait=False)"
@@ -386,10 +376,6 @@
     "from sagemaker.modules.configs import (\n",
     "    Compute, SourceCode, InputData\n",
     ")\n",
-    "from sagemaker.modules.distributed import (\n",
-    "    Torchrun,\n",
-    "    MPI\n",
-    ")\n",
     "\n",
     "compute = Compute(\n",
     "    instance_count=2,\n",
@@ -420,6 +406,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from sagemaker.modules.distributed import (\n",
+    "    Torchrun,\n",
+    "    MPI,\n",
+    "    SMP\n",
+    ")\n",
+    "\n",
     "source_code = SourceCode(\n",
     "    source_dir=\"distributed-training/scripts\",\n",
     "    requirements=\"requirements.txt\",\n",
@@ -429,6 +421,14 @@
     "# Run using Torchrun\n",
     "torchrun = Torchrun()\n",
     "\n",
+    "# Run using Torchrun with SMP\n",
+    "torchrun_smp = Torchrun(\n",
+    "    smp=SMP(\n",
+    "        sm_activation_offloading=True,\n",
+    "        activation_loading_horizon=2,\n",
+    "    )\n",
+    ")\n",
+    "\n",
     "# Run using MPI\n",
     "mpi = MPI(\n",
     "    mpi_additional_options=[\n",
@@ -482,7 +482,7 @@
    "outputs": [],
    "source": [
     "from sagemaker.modules.train import ModelTrainer\n",
-    "from sagemaker.modules.configs import Compute, InputData\n",
+    "from sagemaker.modules.configs import Compute\n",
     "\n",
     "recipe_overrides = {\n",
     "    \"run\": {\n",
@@ -536,6 +536,131 @@
    "source": [
     "Successful Run - https://tiny.amazon.com/14jxjrndx/IsenLink"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Custom Recipe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sagemaker.modules.train import ModelTrainer\n",
+    "from sagemaker.modules.configs import Compute\n",
+    "\n",
+    "training_image = \"059094755717.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-gpu\"\n",
+    "\n",
+    "model_trainer = ModelTrainer.from_recipe(\n",
+    "    training_recipe=\"recipes/custom-recipe.yaml\",\n",
+    "    training_image=training_image,\n",
+    "    compute=Compute(instance_type=\"ml.p4d.24xlarge\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Successful Run - https://tiny.amazon.com/dimbimx1/IsenLink"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trainium Recipe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sagemaker import session\n",
+    "\n",
+    "session = session.Session()\n",
+    "base_job_name = \"trn-llama\"\n",
+    "compiler_cache_bucket = f\"s3://{session.default_bucket()}/{base_job_name}/compiler-cache\"\n",
+    "print(f\"Compiler cache: {compiler_cache_bucket}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sagemaker.modules.train import ModelTrainer\n",
+    "from sagemaker.modules.configs import Compute, InputData, StoppingCondition\n",
+    "\n",
+    "recipe_overrides = {\n",
+    "    \"data\": {\n",
+    "        \"train_dir\": \"/opt/ml/input/data/train\",\n",
+    "    },\n",
+    "    \"model\": {\n",
+    "        \"model_config\": \"/opt/ml/input/data/train/config.json\",\n",
+    "    },\n",
+    "    \"trainer\": {\n",
+    "        \"max_epochs\": 1,\n",
+    "    },\n",
+    "    \"compiler_cache_url\": compiler_cache_bucket,\n",
+    "}\n",
+    "env = {\n",
+    "    \"FI_EFA_FORK_SAFE\": \"1\"\n",
+    "}\n",
+    "\n",
+    "training_image = \"059094755717.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-neuron\"\n",
+    "\n",
+    "model_trainer = ModelTrainer.from_recipe(\n",
+    "    training_recipe=\"https://raw.githubusercontent.com/aws-neuron/neuronx-distributed-training/refs/heads/main/examples/conf/hf_llama3_8B_config.yaml\",\n",
+    "    recipe_overrides=recipe_overrides,\n",
+    "    training_image=training_image,\n",
+    "    compute=Compute(\n",
+    "        instance_type=\"ml.trn1.32xlarge\",\n",
+    "        instance_count=2,\n",
+    "    ),\n",
+    "    stopping_condition=StoppingCondition(\n",
+    "        max_runtime_in_seconds=86400\n",
+    "    ),\n",
+    "    environment=env\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = InputData(\n",
+    "    channel_name=\"train\",\n",
+    "    data_source=\"s3://sagemaker-recipes-059094755717-data/data_llama3/\",\n",
+    ")\n",
+    "\n",
+    "model_trainer.train(input_data_config=[train], wait=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Successful Run - https://tiny.amazon.com/125zldym8/IsenLink"
+   ]
   }
  ],
  "metadata": {
diff --git a/src/sagemaker/modules/testing_notebooks/recipes/custom-recipe.yaml b/src/sagemaker/modules/testing_notebooks/recipes/custom-recipe.yaml
new file mode 100644
index 0000000000..bb671cb331
--- /dev/null
+++ b/src/sagemaker/modules/testing_notebooks/recipes/custom-recipe.yaml
@@ -0,0 +1,110 @@
+run:
+  name: llama-8b
+  results_dir: /opt/ml/model
+  time_limit: 6-00:00:00
+  model_type: hf
+trainer:
+  devices: 8
+  num_nodes: 1
+  accelerator: gpu
+  precision: bf16
+  max_steps: 50
+  log_every_n_steps: 1
+  val_check_interval: 1
+  limit_val_batches: 0
+exp_manager:
+  exp_dir: ''
+  name: experiment
+  create_tensorboard_logger: true
+  create_checkpoint_callback: true
+  checkpoint_callback_params:
+    save_top_k: 0
+    every_n_train_steps: 10
+    monitor: step
+    mode: max
+    save_last: true
+  checkpoint_dir: /opt/ml/checkpoints
+  resume_from_checkpoint: null
+  auto_checkpoint:
+    enabled: false
+  export_full_model:
+    every_n_train_steps: 0
+    save_last: false
+  explicit_log_dir: /opt/ml/output/tensorboard
+use_smp_model: false
+distributed_backend: nccl
+model:
+  model_type: llama_v3
+  train_batch_size: 1
+  seed: 12345
+  grad_clip: 1.0
+  log_reduced_training_loss: true
+  context_parallel_degree: 1
+  moe: false
+  activation_checkpointing: true
+  activation_loading_horizon: 2
+  delayed_param: false
+  offload_activations: false
+  fsdp: true
+  sharding_strategy: hybrid_shard
+  forward_prefetch: true
+  shard_degree: 8
+  backward_fetch_policy: backward_pre
+  auto_wrap_policy: transformer_auto_wrap_policy
+  limit_all_gathers: false
+  use_orig_param: false
+  fp8: false
+  max_context_width: 8192
+  max_position_embeddings: 8192
+  num_hidden_layers: 32
+  hidden_size: 4096
+  num_attention_heads: 32
+  intermediate_size: 14336
+  initializer_range: 0.02
+  layernorm_epsilon: 1.0e-05
+  vocab_size: 128256
+  num_key_value_heads: null
+  use_flash_attention: true
+  rope_theta: 500000.0
+  rope_scaling:
+    rope_type: llama3
+    factor: 8.0
+    high_freq_factor: 4.0
+    low_freq_factor: 1.0
+    original_max_position_embeddings: 8192
+  do_finetune: true
+  hf_model_name_or_path: meta-llama/Llama-3.1-8B
+  hf_access_token: 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
+  peft:
+    peft_type: lora
+    rank: 32
+    alpha: 16
+    dropout: 0.1
+  precision: bf16
+  lr_decay_iters: 50
+  optim:
+    name: adamw
+    lr: 0.0001
+    weight_decay: 0.01
+    betas:
+    - 0.9
+    - 0.95
+    sched:
+      name: CosineAnnealing
+      warmup_steps: 0
+      constant_steps: 0
+      min_lr: 1.0e-06
+  data:
+    train_dir: /opt/ml/input/data/train
+    val_dir: /opt/ml/input/data/val
+    dataset_type: hf
+    use_synthetic_data: true
+  nsys_profile:
+    enabled: false
+    start_step: 10
+    end_step: 10
+    ranks:
+    - 0
+    gen_shape: false
+  viztracer:
+    enabled: false
diff --git a/src/sagemaker/modules/train/model_trainer.py b/src/sagemaker/modules/train/model_trainer.py
index 0868b8c59b..3b89e55420 100644
--- a/src/sagemaker/modules/train/model_trainer.py
+++ b/src/sagemaker/modules/train/model_trainer.py
@@ -191,7 +191,7 @@ class ModelTrainer(BaseModel):
     tags: Optional[List[Tag]] = None
 
     # Created Artifacts
-    _latest_training_job: Optional[resources.TrainingJob] = None
+    _latest_training_job: Optional[resources.TrainingJob] = PrivateAttr(default=None)
 
     # Metrics settings
     _enable_sage_maker_metrics_time_series: Optional[bool] = PrivateAttr(default=False)
@@ -200,7 +200,6 @@ class ModelTrainer(BaseModel):
     # Debugger settings
     _debug_hook_config: Optional[DebugHookConfig] = PrivateAttr(default=None)
     _debug_rule_configurations: Optional[List[DebugRuleConfiguration]] = PrivateAttr(default=None)
-    _remote_debug_config: Optional[RemoteDebugConfig] = PrivateAttr(default=None)
     _profiler_config: Optional[ProfilerConfig] = PrivateAttr(default=None)
     _profiler_rule_configurations: Optional[List[ProfilerRuleConfiguration]] = PrivateAttr(
         default=None

From 67f535d5b8a57a9d8e6fd15d69d1f27916731ced Mon Sep 17 00:00:00 2001
From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com>
Date: Thu, 14 Nov 2024 11:29:15 -0800
Subject: [PATCH 30/79] update notebooks (#1588)

---
 .../base_model_trainer.ipynb                  | 115 ++++++++----------
 .../model_builder_handshake.ipynb             |  62 ++++++----
 2 files changed, 90 insertions(+), 87 deletions(-)

diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
index 6c1025c152..c738093772 100644
--- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
+++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
@@ -6,11 +6,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys, os\n",
-    "\n",
-    "# Get the absolute path of the root directory\n",
-    "root_dir = os.path.abspath(os.path.join(os.getcwd(), \"../../..\"))\n",
-    "sys.path.insert(0, root_dir)"
+    "alias = \"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install \"pydantic>=2.0.0\" sagemaker-core"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install sagemaker-2.232.4.dev0.tar.gz"
    ]
   },
   {
@@ -37,6 +51,7 @@
     "model_trainer = ModelTrainer(\n",
     "    training_image=pytorch_image,\n",
     "    source_code=source_code,\n",
+    "    base_job_name=f\"{alias}-miminal-case\",\n",
     ")"
    ]
   },
@@ -46,14 +61,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_trainer.train(wait=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Successful Run - https://tiny.amazon.com/3maxeyb/IsenLink"
+    "model_trainer.train()"
    ]
   },
   {
@@ -82,16 +90,10 @@
     "model_trainer = ModelTrainer(\n",
     "    training_image=pytorch_image,\n",
     "    source_code=source_code,\n",
+    "    base_job_name=f\"{alias}-simple-case-1\",\n",
     ")\n",
     "\n",
-    "model_trainer.train(wait=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Successful Run - https://tiny.amazon.com/6uy7pmpj/IsenLink"
+    "model_trainer.train()"
    ]
   },
   {
@@ -118,6 +120,7 @@
     "model_trainer = ModelTrainer(\n",
     "    training_image=pytorch_image,\n",
     "    source_code=source_code,\n",
+    "    base_job_name=f\"{alias}-simple-case-2\",\n",
     ")"
    ]
   },
@@ -127,14 +130,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_trainer.train(wait=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Successful Run - https://tiny.amazon.com/7n4n4ogr/IsenLink"
+    "model_trainer.train()"
    ]
   },
   {
@@ -159,7 +155,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "access_token = os.environ.get(\"HF_TOKEN\", \"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\")\n",
+    "import os\n",
+    "\n",
+    "access_token = \"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\"\n",
+    "os.environ[\"HUGGING_FACE_HUB_TOKEN\"] = access_token\n",
+    "\n",
     "model_id = \"meta-llama/Llama-2-7b-hf\"\n",
     "\n",
     "dataset_name = \"tatsu-lab/alpaca\""
@@ -276,7 +276,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Model Trainer Torchrun"
+    "#### Model Trainer Torchrun - Manual"
    ]
   },
   {
@@ -298,8 +298,8 @@
     "env[\"RDMAV_FORK_SAFE\"] = \"1\"\n",
     "\n",
     "compute = Compute(\n",
-    "    instance_count=2,\n",
-    "    instance_type=\"ml.p4d.24xlarge\",\n",
+    "    instance_count=1,\n",
+    "    instance_type=\"ml.g5.48xlarge\",\n",
     "    volume_size_in_gb=96,\n",
     "    keep_alive_period_in_seconds=3600\n",
     ")\n",
@@ -329,7 +329,7 @@
     "source_code = SourceCode(\n",
     "    source_dir=\"distributed-training/scripts\",\n",
     "    requirements=\"requirements.txt\",\n",
-    "    command=\"torchrun --nnodes 2 \\\n",
+    "    command=\"torchrun --nnodes 1 \\\n",
     "            --nproc_per_node 8 \\\n",
     "            --master_addr algo-1 \\\n",
     "            --master_port 7777 \\\n",
@@ -343,6 +343,7 @@
     "    environment=env,\n",
     "    hyperparameters=hyperparameters,\n",
     "    source_code=source_code,\n",
+    "    base_job_name=f\"{alias}-distributed-case\",\n",
     ")"
    ]
   },
@@ -356,14 +357,14 @@
     "    channel_name=\"dataset\",\n",
     "    data_source=training_input_path,\n",
     ")\n",
-    "model_trainer.train(input_data_config=[test_data], wait=False)"
+    "model_trainer.train(input_data_config=[test_data])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Successful Run - https://tiny.amazon.com/10wljn1yu/IsenLink"
+    "#### Model Trainer Torchrun - Abstractions"
    ]
   },
   {
@@ -379,7 +380,7 @@
     "\n",
     "compute = Compute(\n",
     "    instance_count=2,\n",
-    "    instance_type=\"ml.p4d.24xlarge\",\n",
+    "    instance_type=\"ml.g5.48xlarge\",\n",
     "    volume_size_in_gb=96,\n",
     "    keep_alive_period_in_seconds=3600\n",
     ")\n",
@@ -445,6 +446,7 @@
     "    hyperparameters=hyperparameters,\n",
     "    source_code=source_code,\n",
     "    distributed_runner=mpi,\n",
+    "    base_job_name=f\"{alias}-distributed-abstractions\",\n",
     ")"
    ]
   },
@@ -458,7 +460,7 @@
     "    channel_name=\"dataset\",\n",
     "    data_source=training_input_path,\n",
     ")\n",
-    "model_trainer.train(input_data_config=[test_data], wait=False)"
+    "model_trainer.train(input_data_config=[test_data])"
    ]
   },
   {
@@ -511,13 +513,14 @@
     "    }\n",
     "}\n",
     "\n",
-    "training_image = \"059094755717.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-gpu\"\n",
+    "training_image = \"438465156666.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-gpu\"\n",
     "\n",
     "model_trainer = ModelTrainer.from_recipe(\n",
     "    training_recipe=\"training/llama/hf_llama3_8b_seq8192_gpu\",\n",
     "    training_image=training_image,\n",
     "    recipe_overrides=recipe_overrides,\n",
-    "    compute=Compute(instance_type=\"ml.p4d.24xlarge\")\n",
+    "    compute=Compute(instance_type=\"ml.g5.48xlarge\"),\n",
+    "    base_job_name=f\"{alias}-recipe-case-1\",\n",
     ")"
    ]
   },
@@ -530,13 +533,6 @@
     "model_trainer.train()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Successful Run - https://tiny.amazon.com/14jxjrndx/IsenLink"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -553,12 +549,13 @@
     "from sagemaker.modules.train import ModelTrainer\n",
     "from sagemaker.modules.configs import Compute\n",
     "\n",
-    "training_image = \"059094755717.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-gpu\"\n",
+    "training_image = \"438465156666.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-gpu\"\n",
     "\n",
     "model_trainer = ModelTrainer.from_recipe(\n",
     "    training_recipe=\"recipes/custom-recipe.yaml\",\n",
     "    training_image=training_image,\n",
-    "    compute=Compute(instance_type=\"ml.p4d.24xlarge\")\n",
+    "    compute=Compute(instance_type=\"ml.g5.48xlarge\"),\n",
+    "    base_job_name=f\"{alias}-recipe-case-2\",\n",
     ")"
    ]
   },
@@ -571,13 +568,6 @@
     "model_trainer.train()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Successful Run - https://tiny.amazon.com/dimbimx1/IsenLink"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -624,7 +614,7 @@
     "    \"FI_EFA_FORK_SAFE\": \"1\"\n",
     "}\n",
     "\n",
-    "training_image = \"059094755717.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-neuron\"\n",
+    "training_image = \"438465156666.dkr.ecr.us-west-2.amazonaws.com/sagemaker-recipes-neuron\"\n",
     "\n",
     "model_trainer = ModelTrainer.from_recipe(\n",
     "    training_recipe=\"https://raw.githubusercontent.com/aws-neuron/neuronx-distributed-training/refs/heads/main/examples/conf/hf_llama3_8B_config.yaml\",\n",
@@ -635,7 +625,7 @@
     "        instance_count=2,\n",
     "    ),\n",
     "    stopping_condition=StoppingCondition(\n",
-    "        max_runtime_in_seconds=86400\n",
+    "        max_runtime_in_seconds=3600\n",
     "    ),\n",
     "    environment=env\n",
     ")"
@@ -649,18 +639,11 @@
    "source": [
     "train = InputData(\n",
     "    channel_name=\"train\",\n",
-    "    data_source=\"s3://sagemaker-recipes-059094755717-data/data_llama3/\",\n",
+    "    data_source=\"s3://sagemaker-recipes-438465156666-data/data_llama3/\",\n",
     ")\n",
     "\n",
     "model_trainer.train(input_data_config=[train], wait=False)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Successful Run - https://tiny.amazon.com/125zldym8/IsenLink"
-   ]
   }
  ],
  "metadata": {
diff --git a/src/sagemaker/modules/testing_notebooks/model_builder_handshake.ipynb b/src/sagemaker/modules/testing_notebooks/model_builder_handshake.ipynb
index f24448d7d9..daeb2c7bfc 100644
--- a/src/sagemaker/modules/testing_notebooks/model_builder_handshake.ipynb
+++ b/src/sagemaker/modules/testing_notebooks/model_builder_handshake.ipynb
@@ -2,10 +2,32 @@
  "cells": [
   {
    "cell_type": "code",
+   "execution_count": null,
+   "id": "eca12282",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install \"pydantic>=2.0.0\" sagemaker-core"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "756ebbf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install sagemaker-2.232.4.dev0.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "initial_id",
    "metadata": {
     "collapsed": true
    },
+   "outputs": [],
    "source": [
     "from sagemaker_core.main.shapes import TrainingJob\n",
     "\n",
@@ -15,13 +37,14 @@
     "role = get_execution_role()\n",
     "region = sagemaker_session.boto_region_name\n",
     "bucket = sagemaker_session.default_bucket()"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "4b3a4f7d1713685f",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "\n",
     "from sagemaker.modules.configs import SourceCode\n",
@@ -38,14 +61,14 @@
     ")\n",
     "\n",
     "model_trainer.train()"
-   ],
-   "id": "4b3a4f7d1713685f",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "295a16ef277257a0",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "from sagemaker.serve.builder.schema_builder import SchemaBuilder\n",
@@ -88,14 +111,14 @@
     "assert model.model_data == model_trainer._latest_training_job.model_artifacts.s3_model_artifacts\n",
     "\n",
     "print(model.model_data)"
-   ],
-   "id": "295a16ef277257a0",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "935ea8486278d7b1",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "training_job: TrainingJob = model_trainer._latest_training_job\n",
     "\n",
@@ -112,18 +135,15 @@
     "assert model.model_data == training_job.model_artifacts.s3_model_artifacts\n",
     "\n",
     "print(model.model_data)"
-   ],
-   "id": "935ea8486278d7b1",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "source": "",
+   "execution_count": null,
    "id": "757180da84407a1a",
+   "metadata": {},
    "outputs": [],
-   "execution_count": null
+   "source": []
   }
  ],
  "metadata": {

From e4701e8b612414c0579b7cb8143fdcfd7f154eb3 Mon Sep 17 00:00:00 2001
From: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com>
Date: Thu, 14 Nov 2024 15:03:41 -0800
Subject: [PATCH 31/79] update notebooks (#1592)

---
 .../base_model_builder_deploy.ipynb           | 1395 ++---------------
 .../base_model_trainer.ipynb                  |  141 +-
 .../testing_notebooks/install_docker.sh       |   20 +
 .../sagemaker-2.232.4.dev0-py3-none-any.whl   |  Bin 0 -> 4471800 bytes
 .../sagemaker-2.232.4.dev0.tar.gz             |  Bin 0 -> 4026571 bytes
 5 files changed, 215 insertions(+), 1341 deletions(-)
 create mode 100644 src/sagemaker/modules/testing_notebooks/install_docker.sh
 create mode 100644 src/sagemaker/modules/testing_notebooks/sagemaker-2.232.4.dev0-py3-none-any.whl
 create mode 100644 src/sagemaker/modules/testing_notebooks/sagemaker-2.232.4.dev0.tar.gz

diff --git a/src/sagemaker/modules/testing_notebooks/base_model_builder_deploy.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_builder_deploy.ipynb
index 7dd93b7b85..adf89f5a60 100644
--- a/src/sagemaker/modules/testing_notebooks/base_model_builder_deploy.ipynb
+++ b/src/sagemaker/modules/testing_notebooks/base_model_builder_deploy.ipynb
@@ -1,8 +1,9 @@
 {
  "cells": [
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "270525e009204918",
+   "metadata": {},
    "source": [
     "## This Notebook is to showcase the deploy function of ModelBuilder. \n",
     "\n",
@@ -10,23 +11,47 @@
     "The deploy function mainly provides 2 advantages:\n",
     "1. Simplification of parameters - Reducing the total parameters required from 22 to 7. \n",
     "2. Validation/Error Handling with enum to make sure the deployment mode related parameters are provided in the call"
-   ],
-   "id": "270525e009204918"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "### Basic Setup",
-   "id": "bb8a9608ba668104"
+   "id": "bb8a9608ba668104",
+   "metadata": {},
+   "source": [
+    "### Basic Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80089403",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install \"pydantic>=2.0.0\" sagemaker-core"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e094f5f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install sagemaker-2.232.4.dev0.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8fdb05f0164a7b6",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-10-30T20:10:08.460832Z",
      "start_time": "2024-10-30T20:10:03.204079Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from sagemaker import Session, get_execution_role\n",
     "\n",
@@ -34,34 +59,27 @@
     "role = get_execution_role()\n",
     "region = sagemaker_session.boto_region_name\n",
     "bucket = sagemaker_session.default_bucket()\n"
-   ],
-   "id": "a8fdb05f0164a7b6",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sagemaker.config INFO - Not applying SDK defaults from location: /Library/Application Support/sagemaker/config.yaml\n",
-      "sagemaker.config INFO - Not applying SDK defaults from location: /Users/nargokul/Library/Application Support/sagemaker/config.yaml\n"
-     ]
-    }
-   ],
-   "execution_count": 1
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "### Create Training Job and Model Builder",
-   "id": "75baff9492b9c9a6"
+   "id": "75baff9492b9c9a6",
+   "metadata": {},
+   "source": [
+    "### Create Training Job and Model Builder"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d4ade0d9d11b649",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-10-30T20:10:09.506486Z",
      "start_time": "2024-10-30T20:10:08.468687Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from sklearn.datasets import load_iris\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -103,28 +121,19 @@
     "\n",
     "print(s3_input_path)\n",
     "print(s3_output_path)"
-   ],
-   "id": "2d4ade0d9d11b649",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "s3://sagemaker-us-west-2-211125564141/DEMO-scikit-iris/data/train.csv\n",
-      "s3://sagemaker-us-west-2-211125564141/DEMO-scikit-iris/output\n"
-     ]
-    }
-   ],
-   "execution_count": 2
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdda06c2b848ce94",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-10-30T20:12:53.241670Z",
      "start_time": "2024-10-30T20:10:09.640599Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from sagemaker_core.main.shapes import AlgorithmSpecification, Channel, DataSource, S3DataSource,OutputDataConfig, ResourceConfig, StoppingCondition\n",
     "import uuid\n",
@@ -207,858 +216,77 @@
     "    instance_type=\"ml.c6i.xlarge\"\n",
     ")\n",
     "model_builder.build()\n"
-   ],
-   "id": "cdda06c2b848ce94",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Library/Python/3.9/site-packages/pydantic/_internal/_fields.py:200: UserWarning: Field name \"json\" in \"MonitoringDatasetFormat\" shadows an attribute in parent \"Base\"\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[2;36m[10/30/24 13:10:11]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating training_job resource.                                     \u001B]8;id=847076;file:///Library/Python/3.9/site-packages/sagemaker_core/main/resources.py\u001B\\\u001B[2mresources.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=443564;file:///Library/Python/3.9/site-packages/sagemaker_core/main/resources.py#26309\u001B\\\u001B[2m26309\u001B[0m\u001B]8;;\u001B\\\n"
-      ],
-      "text/html": [
-       "
[10/30/24 13:10:11] INFO     Creating training_job resource.                                     resources.py:26309\n",
-       "
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-      ]
-     },
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-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-      ],
-      "text/html": [
-       "
                    WARNING  No boto3 session provided. Creating a new session.                        utils.py:336\n",
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;215;175;0mWARNING \u001B[0m No region provided. Using default region.                                 \u001B]8;id=825815;file:///Library/Python/3.9/site-packages/sagemaker_core/main/utils.py\u001B\\\u001B[2mutils.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=145527;file:///Library/Python/3.9/site-packages/sagemaker_core/main/utils.py#340\u001B\\\u001B[2m340\u001B[0m\u001B]8;;\u001B\\\n"
-      ],
-      "text/html": [
-       "
                    WARNING  No region provided. Using default region.                                 utils.py:340\n",
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;215;175;0mWARNING \u001B[0m No config provided. Using default config.                                 \u001B]8;id=288203;file:///Library/Python/3.9/site-packages/sagemaker_core/main/utils.py\u001B\\\u001B[2mutils.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=337502;file:///Library/Python/3.9/site-packages/sagemaker_core/main/utils.py#344\u001B\\\u001B[2m344\u001B[0m\u001B]8;;\u001B\\\n"
-      ],
-      "text/html": [
-       "
                    WARNING  No config provided. Using default config.                                 utils.py:344\n",
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Found credentials in shared credentials file: ~\u001B[38;2;225;0;225m/.aws/\u001B[0m\u001B[38;2;225;0;225mcredentials\u001B[0m   \u001B]8;id=439359;file:///Library/Python/3.9/site-packages/botocore/credentials.py\u001B\\\u001B[2mcredentials.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=831253;file:///Library/Python/3.9/site-packages/botocore/credentials.py#1278\u001B\\\u001B[2m1278\u001B[0m\u001B]8;;\u001B\\\n"
-      ],
-      "text/html": [
-       "
                    INFO     Found credentials in shared credentials file: ~/.aws/credentials   credentials.py:1278\n",
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Output()"
-      ],
-      "application/vnd.jupyter.widget-view+json": {
-       "version_major": 2,
-       "version_minor": 0,
-       "model_id": "b0346627a92443f28c46d3ce8402f842"
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
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-    {
-     "data": {
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-       "\u001B[2;36m[10/30/24 13:12:53]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Final Resource Status: \u001B[1mCompleted\u001B[0m                                    \u001B]8;id=750619;file:///Library/Python/3.9/site-packages/sagemaker_core/main/resources.py\u001B\\\u001B[2mresources.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=635418;file:///Library/Python/3.9/site-packages/sagemaker_core/main/resources.py#26595\u001B\\\u001B[2m26595\u001B[0m\u001B]8;;\u001B\\\n"
-      ],
-      "text/html": [
-       "
[10/30/24 13:12:53] INFO     Final Resource Status: Completed                                    resources.py:26595\n",
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [],
-      "text/html": [
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Found credentials in shared credentials file: ~\u001B[38;2;225;0;225m/.aws/\u001B[0m\u001B[38;2;225;0;225mcredentials\u001B[0m   \u001B]8;id=493314;file:///Library/Python/3.9/site-packages/botocore/credentials.py\u001B\\\u001B[2mcredentials.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=633308;file:///Library/Python/3.9/site-packages/botocore/credentials.py#1278\u001B\\\u001B[2m1278\u001B[0m\u001B]8;;\u001B\\\n"
-      ],
-      "text/html": [
-       "
                    INFO     Found credentials in shared credentials file: ~/.aws/credentials   credentials.py:1278\n",
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ModelBuilder: INFO:     Either inference spec or model is provided. ModelBuilder is not handling MLflow model input\n",
-      "ModelBuilder: INFO:     Skipping auto detection as the image uri is provided 433757028032.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 3
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "#### Real Time Deployment",
-   "id": "b0d045e8f64e3787"
+   "id": "b0d045e8f64e3787",
+   "metadata": {},
+   "source": [
+    "#### Real Time Deployment"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96bb41b4146549dd",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-10-30T20:16:00.777140Z",
      "start_time": "2024-10-30T20:12:53.258703Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "real_time_predictor = model_builder.deploy(\n",
     "    initial_instance_count=1)"
-   ],
-   "id": "96bb41b4146549dd",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ModelBuilder: INFO:     ModelBuilder will collect telemetry to help us better understand our user's needs, diagnose issues, and deliver additional features. To opt out of telemetry, please disable via TelemetryOptOut in intelligent defaults. See https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk for more info.\n"
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-       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
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-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ModelBuilder: DEBUG:     ModelBuilder metrics emitted.\n"
-     ]
-    }
-   ],
-   "execution_count": 4
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "#### Serverless Deployment",
-   "id": "5104c00db40527fd"
+   "id": "5104c00db40527fd",
+   "metadata": {},
+   "source": [
+    "#### Serverless Deployment"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a5de6c58da465f9",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-10-30T20:18:15.929714Z",
      "start_time": "2024-10-30T20:16:00.806356Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig\n",
     "\n",
     "serverless_predictor = model_builder.deploy(\n",
     "    inference_config=ServerlessInferenceConfig())"
-   ],
-   "id": "9a5de6c58da465f9",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ModelBuilder: INFO:     ModelBuilder will collect telemetry to help us better understand our user's needs, diagnose issues, and deliver additional features. To opt out of telemetry, please disable via TelemetryOptOut in intelligent defaults. See https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk for more info.\n"
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-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ModelBuilder: DEBUG:     ModelBuilder metrics emitted.\n"
-     ]
-    }
-   ],
-   "execution_count": 5
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "#### Async Deployment",
-   "id": "6462fe2d8f85dba7"
+   "id": "6462fe2d8f85dba7",
+   "metadata": {},
+   "source": [
+    "#### Async Deployment"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "271e8dcfb015d69a",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-10-30T20:21:27.809852Z",
      "start_time": "2024-10-30T20:18:15.955152Z"
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from sagemaker.s3_utils import s3_path_join\n",
     "from sagemaker.async_inference import AsyncInferenceConfig\n",
@@ -1067,398 +295,22 @@
     "    inference_config=AsyncInferenceConfig(\n",
     "        output_path=s3_path_join(\"s3://\", bucket, \"async_inference/output\")),\n",
     ")"
-   ],
-   "id": "271e8dcfb015d69a",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ModelBuilder: INFO:     ModelBuilder will collect telemetry to help us better understand our user's needs, diagnose issues, and deliver additional features. To opt out of telemetry, please disable via TelemetryOptOut in intelligent defaults. See https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk for more info.\n"
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-       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
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-       "\u001B[2;36m                    \u001B[0m           \u001B[1;38;2;225;0;225mmonkey.patch_all\u001B[0m\u001B[1m(\u001B[0m\u001B[38;2;215;175;0msubprocess\u001B[0m=\u001B[3;38;2;0;135;0mTrue\u001B[0m\u001B[1m)\u001B[0m                                    \u001B[2m               \u001B[0m\n"
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-       "                             : MonkeyPatchWarning: Monkey-patching ssl after ssl has already been                  \n",
-       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
-       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
-       "                             monkey-patch earlier. See                                                             \n",
-       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
-       "                             imports (NOT patched): ['urllib3.util                                                 \n",
-       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py)',                  \n",
-       "                             'urllib3.util.ssl_                                                                    \n",
-       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py)'].                     \n",
-       "                               monkey.patch_all(subprocess=True)                                                   \n",
-       "
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-      ]
-     },
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-       "\u001B[2;36m                    \u001B[0m         imported may lead to errors, including RecursionError on Python \u001B[1;36m3.6\u001B[0m.   \u001B[2m               \u001B[0m\n",
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-       "\u001B[2;36m                    \u001B[0m         monkey-patch earlier. See                                              \u001B[2m               \u001B[0m\n",
-       "\u001B[2;36m                    \u001B[0m         \u001B[4;38;2;0;105;255mhttps://github.com/gevent/gevent/issues/1016.\u001B[0m Modules that had direct  \u001B[2m               \u001B[0m\n",
-       "\u001B[2;36m                    \u001B[0m         imports \u001B[1m(\u001B[0mNOT patched\u001B[1m)\u001B[0m: \u001B[1m[\u001B[0m\u001B[38;2;0;135;0m'urllib3.util \u001B[0m                                 \u001B[2m               \u001B[0m\n",
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-       "\u001B[2;36m                    \u001B[0m           \u001B[1;38;2;225;0;225mmonkey.patch_all\u001B[0m\u001B[1m(\u001B[0m\u001B[38;2;215;175;0msubprocess\u001B[0m=\u001B[3;38;2;0;135;0mTrue\u001B[0m\u001B[1m)\u001B[0m                                    \u001B[2m               \u001B[0m\n"
-      ],
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-       "                             imported may lead to errors, including RecursionError on Python 3.6.                  \n",
-       "                             It may also silently lead to incorrect behaviour on Python 3.7. Please                \n",
-       "                             monkey-patch earlier. See                                                             \n",
-       "                             https://github.com/gevent/gevent/issues/1016. Modules that had direct                 \n",
-       "                             imports (NOT patched): ['urllib3.util                                                 \n",
-       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/__init__.py)',                  \n",
-       "                             'urllib3.util.ssl_                                                                    \n",
-       "                             (/opt/amazon/lib/python3.7/site-packages/urllib3/util/ssl_.py)'].                     \n",
-       "                               monkey.patch_all(subprocess=True)                                                   \n",
-       "
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-      ]
-     },
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-     "output_type": "display_data"
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-       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-18-16-602        \u001B[2m               \u001B[0m\n"
-      ],
-      "text/html": [
-       "
[10/30/24 13:21:26] INFO     Created endpoint with name                                             session.py:8294\n",
-       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-18-16-602                       \n",
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ModelBuilder: DEBUG:     ModelBuilder metrics emitted.\n"
-     ]
-    }
-   ],
-   "execution_count": 6
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "#### Batch Deployment",
-   "id": "f8458db5bfb1a0c5"
+   "id": "f8458db5bfb1a0c5",
+   "metadata": {},
+   "source": [
+    "#### Batch Deployment"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
+   "execution_count": null,
+   "id": "5a80ccf640a5dca9",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from sagemaker.s3_utils import s3_path_join\n",
     "\n",
@@ -1471,31 +323,31 @@
     "        output_path=s3_path_join(\"s3://\", bucket, \"async_inference/output\"),\n",
     "    )\n",
     ")"
-   ],
-   "id": "5a80ccf640a5dca9",
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "d97c14830180dd4",
+   "metadata": {},
    "source": [
     "#### MultiModel Deployment\n",
     "\n",
     "Similar syntax applies for Multi-Container Endpoint as well"
-   ],
-   "id": "d97c14830180dd4"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9788d4f7fab3b7ca",
    "metadata": {
-    "jupyter": {
-     "is_executing": true
-    },
     "ExecuteTime": {
      "start_time": "2024-10-30T20:30:57.436543Z"
+    },
+    "jupyter": {
+     "is_executing": true
     }
    },
-   "cell_type": "code",
+   "outputs": [],
    "source": [
     "from sagemaker.compute_resource_requirements import ResourceRequirements\n",
     "\n",
@@ -1509,56 +361,15 @@
     "        limits={},\n",
     "    ),\n",
     "    initial_instance_count=1)"
-   ],
-   "id": "9788d4f7fab3b7ca",
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ModelBuilder: INFO:     ModelBuilder will collect telemetry to help us better understand our user's needs, diagnose issues, and deliver additional features. To opt out of telemetry, please disable via TelemetryOptOut in intelligent defaults. See https://sagemaker.readthedocs.io/en/stable/overview.html#configuring-and-using-defaults-with-the-sagemaker-python-sdk for more info.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\u001B[2;36m[10/30/24 13:30:58]\u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating endpoint-config with name                                     \u001B]8;id=987242;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=233313;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#5817\u001B\\\u001B[2m5817\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-30-57-438        \u001B[2m               \u001B[0m\n"
-      ],
-      "text/html": [
-       "
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-       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-30-57-438                       \n",
-       "
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-      ]
-     },
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-    },
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-       "\u001B[2;36m                   \u001B[0m\u001B[2;36m \u001B[0m\u001B[1;38;2;0;105;255mINFO    \u001B[0m Creating endpoint with name                                            \u001B]8;id=10549;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py\u001B\\\u001B[2msession.py\u001B[0m\u001B]8;;\u001B\\\u001B[2m:\u001B[0m\u001B]8;id=757746;file:///Users/nargokul/workspace/sagemaker-python-sdk-staging/sagemaker-python-sdk-staging/src/sagemaker/session.py#4639\u001B\\\u001B[2m4639\u001B[0m\u001B]8;;\u001B\\\n",
-       "\u001B[2;36m                    \u001B[0m         model-name-f76fb1fa96fa11efb5372ad633a9-\u001B[1;36m2024\u001B[0m-10-30-20-30-57-438        \u001B[2m               \u001B[0m\n"
-      ],
-      "text/html": [
-       "
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-       "                             model-name-f76fb1fa96fa11efb5372ad633a9-2024-10-30-20-30-57-438                       \n",
-       "
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": null
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "code",
-   "outputs": [],
    "execution_count": null,
-   "source": "",
-   "id": "724aec7045fd80f1"
+   "id": "724aec7045fd80f1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
index c738093772..944a3ce941 100644
--- a/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
+++ b/src/sagemaker/modules/testing_notebooks/base_model_trainer.ipynb
@@ -31,7 +31,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simple Case Minimally Setup ModelTrainer and Execute Commands"
+    "## Script Mode Case - 1"
    ]
   },
   {
@@ -51,7 +51,7 @@
     "model_trainer = ModelTrainer(\n",
     "    training_image=pytorch_image,\n",
     "    source_code=source_code,\n",
-    "    base_job_name=f\"{alias}-miminal-case\",\n",
+    "    base_job_name=f\"{alias}-simple-case-1\",\n",
     ")"
    ]
   },
@@ -68,39 +68,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simple Script Mode Case - 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sagemaker.modules.train import ModelTrainer\n",
-    "from sagemaker.modules.configs import SourceCode\n",
-    "\n",
-    "pytorch_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310\"\n",
-    "\n",
-    "source_code = SourceCode(\n",
-    "    source_dir=\"basic-script-mode\",\n",
-    "    command=\"python custom_script.py\",\n",
-    ")\n",
-    "\n",
-    "model_trainer = ModelTrainer(\n",
-    "    training_image=pytorch_image,\n",
-    "    source_code=source_code,\n",
-    "    base_job_name=f\"{alias}-simple-case-1\",\n",
-    ")\n",
-    "\n",
-    "model_trainer.train()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simple Script Mode Case - 2\n"
+    "## Script Mode Case - 2"
    ]
   },
   {
@@ -137,7 +105,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Distributed Training Case"
+    "# Distributed Training Setup"
    ]
   },
   {
@@ -276,7 +244,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Model Trainer Torchrun - Manual"
+    "## Distributed Training - Case 1"
    ]
   },
   {
@@ -343,7 +311,7 @@
     "    environment=env,\n",
     "    hyperparameters=hyperparameters,\n",
     "    source_code=source_code,\n",
-    "    base_job_name=f\"{alias}-distributed-case\",\n",
+    "    base_job_name=f\"{alias}-distributed-case-1\",\n",
     ")"
    ]
   },
@@ -364,7 +332,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Model Trainer Torchrun - Abstractions"
+    "## Distributed Training - Case 2"
    ]
   },
   {
@@ -407,11 +375,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker.modules.distributed import (\n",
-    "    Torchrun,\n",
-    "    MPI,\n",
-    "    SMP\n",
-    ")\n",
+    "from sagemaker.modules.distributed import Torchrun, SMP\n",
     "\n",
     "source_code = SourceCode(\n",
     "    source_dir=\"distributed-training/scripts\",\n",
@@ -430,6 +394,85 @@
     "    )\n",
     ")\n",
     "\n",
+    "model_trainer = ModelTrainer(\n",
+    "    training_image=hugging_face_image,\n",
+    "    compute=compute,\n",
+    "    hyperparameters=hyperparameters,\n",
+    "    source_code=source_code,\n",
+    "    distributed_runner=torchrun,\n",
+    "    base_job_name=f\"{alias}-distributed-case-2\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data = InputData(\n",
+    "    channel_name=\"dataset\",\n",
+    "    data_source=training_input_path,\n",
+    ")\n",
+    "model_trainer.train(input_data_config=[test_data])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distributed Training - Case 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sagemaker.modules.train import ModelTrainer\n",
+    "from sagemaker.modules.configs import (\n",
+    "    Compute, SourceCode, InputData\n",
+    ")\n",
+    "\n",
+    "compute = Compute(\n",
+    "    instance_count=2,\n",
+    "    instance_type=\"ml.g5.48xlarge\",\n",
+    "    volume_size_in_gb=96,\n",
+    "    keep_alive_period_in_seconds=3600\n",
+    ")\n",
+    "\n",
+    "hugging_face_image = \"763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:2.0.0-transformers4.28.1-gpu-py310-cu118-ubuntu20.04\"\n",
+    "\n",
+    "hyperparameters = {\n",
+    "    \"dataset_path\": \"/opt/ml/input/data/dataset\",\n",
+    "    \"epochs\": 1,\n",
+    "    \"max_steps\": 100,\n",
+    "    \"fsdp\": \"full_shard auto_wrap\",\n",
+    "    \"fsdp_transformer_layer_cls_to_wrap\": \"LlamaDecoderLayer\",\n",
+    "    \"gradient_checkpointing\": True,\n",
+    "    \"optimizer\": \"adamw_torch\",\n",
+    "    \"per_device_train_batch_size\": 1,\n",
+    "    \"model_id\": model_id,\n",
+    "    \"access_token\": access_token,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sagemaker.modules.distributed import MPI\n",
+    "\n",
+    "source_code = SourceCode(\n",
+    "    source_dir=\"distributed-training/scripts\",\n",
+    "    requirements=\"requirements.txt\",\n",
+    "    entry_script=\"run_clm_no_trainer.py\",\n",
+    ")\n",
+    "\n",
+    "\n",
     "# Run using MPI\n",
     "mpi = MPI(\n",
     "    mpi_additional_options=[\n",
@@ -446,7 +489,7 @@
     "    hyperparameters=hyperparameters,\n",
     "    source_code=source_code,\n",
     "    distributed_runner=mpi,\n",
-    "    base_job_name=f\"{alias}-distributed-abstractions\",\n",
+    "    base_job_name=f\"{alias}-distributed-case-3\",\n",
     ")"
    ]
   },
@@ -467,14 +510,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## ModelTrainer Recipes"
+    "# ModelTrainer Recipes"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### SageMaker GPU Recipe"
+    "## Training Recipes - Case 1"
    ]
   },
   {
@@ -537,7 +580,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Custom Recipe"
+    "## Training Recipes - Case 2"
    ]
   },
   {
@@ -572,7 +615,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Trainium Recipe"
+    "## Training Recipes - Case 3"
    ]
   },
   {
diff --git a/src/sagemaker/modules/testing_notebooks/install_docker.sh b/src/sagemaker/modules/testing_notebooks/install_docker.sh
new file mode 100644
index 0000000000..11d4fe7758
--- /dev/null
+++ b/src/sagemaker/modules/testing_notebooks/install_docker.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+sudo apt-get -y install ca-certificates curl gnupg
+
+sudo install -m 0755 -d /etc/apt/keyrings
+curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
+sudo chmod a+r /etc/apt/keyrings/docker.gpg
+echo \
+  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
+  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
+  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
+sudo apt-get -y update
+
+# pick the latest patch from:
+# apt-cache madison docker-ce | awk '{ print $3 }' | grep -i 20.10
+VERSION_STRING=5:20.10.24~3-0~ubuntu-jammy
+sudo apt-get install docker-ce-cli=$VERSION_STRING docker-compose-plugin -y
+
+# validate the Docker Client is able to access Docker Server at [unix:///docker/proxy.sock]
+docker version
\ No newline at end of file
diff --git a/src/sagemaker/modules/testing_notebooks/sagemaker-2.232.4.dev0-py3-none-any.whl b/src/sagemaker/modules/testing_notebooks/sagemaker-2.232.4.dev0-py3-none-any.whl
new file mode 100644
index 0000000000000000000000000000000000000000..4632026fa70e81368464536583247ccc7fc113ed
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