diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2349827551..c43a7c91db 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,23 @@
# Changelog
+## v2.247.1 (2025-06-23)
+
+### Bug Fixes and Other Changes
+
+ * update image_uri_configs 06-19-2025 07:18:34 PST
+
+## v2.247.0 (2025-06-13)
+
+### Features
+
+ * Add support for MetricDefinitions in ModelTrainer
+
+### Bug Fixes and Other Changes
+
+ * update jumpstart region_config, update image_uri_configs 06-12-2025 07:18:12 PST
+ * Add ignore_patterns in ModelTrainer to ignore specific files/folders
+ * Allow import failure for internal _hashlib module
+
## v2.246.0 (2025-06-04)
### Features
diff --git a/VERSION b/VERSION
index 657c15330d..cdbe343ddb 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-2.246.1.dev0
+2.247.2.dev0
diff --git a/doc/_templates/custom-logo.html b/doc/_templates/custom-logo.html
new file mode 100644
index 0000000000..4a02b20be2
--- /dev/null
+++ b/doc/_templates/custom-logo.html
@@ -0,0 +1,6 @@
+
+ 
+ SageMaker Python SDK
+
\ No newline at end of file
diff --git a/doc/_templates/navbar-right.html b/doc/_templates/navbar-right.html
new file mode 100644
index 0000000000..93b2e0b276
--- /dev/null
+++ b/doc/_templates/navbar-right.html
@@ -0,0 +1,9 @@
+
+
diff --git a/doc/_templates/search.html b/doc/_templates/search.html
index 93c01c7799..5e8b7642a4 100644
--- a/doc/_templates/search.html
+++ b/doc/_templates/search.html
@@ -7,7 +7,7 @@
:copyright: Copyright 2007-2013 by the Sphinx team, see AUTHORS.
:license: BSD, see https://github.com/sphinx-doc/sphinx/blob/master/LICENSE for details.
#}
-{%- extends "layout.html" %}
+{% extends "!layout.html" %}
{% set title = _('Search') %}
{% set display_vcs_links = False %}
{%- block scripts %}
diff --git a/doc/conf.py b/doc/conf.py
index 6c88ddd0e7..d2ccc4fe09 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -35,12 +35,34 @@
"sphinx.ext.autosummary",
"sphinx.ext.napoleon",
"sphinx.ext.autosectionlabel",
+ "myst_parser",
+ "sphinx_design",
+ "sphinx_tabs.tabs"
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
-source_suffix = ".rst" # The suffix of source filenames.
+source_suffix = {
+ '.rst': 'restructuredtext',
+ '.md': 'markdown',
+}
+
+# MyST parser settings
+# myst_heading_anchors = 3
+myst_enable_extensions = [
+ "amsmath",
+ "colon_fence",
+ "deflist",
+ "dollarmath",
+ "html_admonition",
+ "html_image",
+ "replacements",
+ "smartquotes",
+ "substitution",
+ "tasklist",
+]
+myst_url_schemes = ("http", "https", "mailto")
master_doc = "index" # The master toctree document.
copyright = "%s, Amazon" % datetime.now().year
@@ -58,17 +80,21 @@
autodoc_default_flags = ["show-inheritance", "members", "undoc-members"]
autodoc_member_order = "bysource"
-html_theme = "sphinx_rtd_theme"
+html_theme = "sphinx_book_theme"
html_theme_options = {
- "collapse_navigation": True,
- "sticky_navigation": True,
- "navigation_depth": 6,
- "includehidden": True,
- "titles_only": False,
+ "use_repository_button": False,
+ "use_edit_page_button": False,
+ "use_issues_button": False,
+ "use_download_button": False,
+ "toc_title": "On This Page",
+ "navbar_start": ["custom-logo.html"],
+ "navbar_center": [],
+ "navbar_end": ["navbar-right.html"],
+ "navigation_depth": 2,
+ "collapse_navigation": True
}
-
html_static_path = ["_static"]
htmlhelp_basename = "%sdoc" % project
@@ -82,10 +108,12 @@
]
html_css_files = [
+ "https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css",
"https://cdn.datatables.net/1.10.23/css/jquery.dataTables.min.css",
"theme_overrides.css",
"pagination.css",
"search_accessories.css",
+ "custom.css"
]
# Example configuration for intersphinx: refer to the Python standard library.
diff --git a/doc/deploy.md b/doc/deploy.md
new file mode 100644
index 0000000000..1dcf7985a3
--- /dev/null
+++ b/doc/deploy.md
@@ -0,0 +1,401 @@
+# Deploy Models with SageMaker Python SDK
+
+Amazon SageMaker provides flexible deployment options to serve your machine learning models for inference. This guide covers the key deployment interfaces and helps you choose the right approach for your workload requirements.
+
+## Deployment Options Overview
+
+| Deployment Option | Description | Best For |
+|-------------------|-------------|----------|
+| **Real-time Endpoints** | Synchronous, low-latency inference | Interactive applications, real-time predictions |
+| **Serverless Inference** | Auto-scaling, pay-per-use endpoints | Intermittent traffic, cost optimization |
+| **Asynchronous Inference** | Process requests in queue | Large payloads, long processing times |
+| **Batch Transform** | Offline predictions for datasets | Bulk inference, ETL workflows |
+| **Multi-model Endpoints** | Host multiple models on shared resources | Cost efficiency, similar model types |
+
+## Deployment Workflow
+
+Regardless of which deployment option you choose, the general workflow follows these steps:
+
+1. **Prepare your model artifacts** - Ensure your trained model is in a compatible format
+2. **Create a model in SageMaker** - Register your model artifacts with SageMaker
+3. **Configure the deployment** - Choose deployment options and resources
+4. **Deploy the model** - Create an endpoint or batch transform job
+5. **Invoke for predictions** - Send requests to your deployed model
+
+## Choosing the Right Deployment Option
+
+### Real-time Endpoints
+
+Real-time endpoints provide synchronous, low-latency inference via a REST API. They're ideal for applications that require immediate responses.
+
+Key features:
+- **Low-latency responses** for real-time applications
+- **Auto-scaling** based on traffic patterns
+- **High availability** with multi-AZ deployment
+- **Monitoring** for performance and data quality
+
+```python
+import sagemaker
+from sagemaker.model import Model
+
+# Create a model from trained artifacts
+model = Model(
+ image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.13.1-cpu-py39",
+ model_data="s3://my-bucket/model.tar.gz",
+ role=sagemaker.get_execution_role(),
+ env={"SAGEMAKER_MODEL_SERVER_TIMEOUT": "3600"}
+)
+
+# Deploy to a real-time endpoint
+predictor = model.deploy(
+ initial_instance_count=1,
+ instance_type="ml.m5.xlarge",
+ endpoint_name="my-endpoint"
+)
+
+# Make a prediction
+response = predictor.predict(data)
+```
+
+### Serverless Inference
+
+Serverless Inference automatically provisions and scales compute capacity based on the volume of inference requests. You pay only for the compute time used to process requests, making it ideal for intermittent workloads.
+
+Key features:
+- **No infrastructure management** required
+- **Pay-per-use** pricing model
+- **Auto-scaling** from zero to handle traffic spikes
+- **Configurable memory** sizes
+
+```python
+import sagemaker
+from sagemaker.model import Model
+from sagemaker.serverless import ServerlessInferenceConfig
+
+# Configure serverless settings
+serverless_config = ServerlessInferenceConfig(
+ memory_size_in_mb=2048,
+ max_concurrency=5
+)
+
+# Create a model
+model = Model(
+ image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.13.1-cpu-py39",
+ model_data="s3://my-bucket/model.tar.gz",
+ role=sagemaker.get_execution_role()
+)
+
+# Deploy with serverless configuration
+predictor = model.deploy(
+ serverless_inference_config=serverless_config,
+ endpoint_name="my-serverless-endpoint"
+)
+
+# Make a prediction
+response = predictor.predict(data)
+```
+
+### Asynchronous Inference
+
+Asynchronous Inference queues incoming requests and processes them asynchronously. This is ideal for workloads with large payloads or long processing times.
+
+Key features:
+- **Queue-based processing** for handling traffic spikes
+- **Support for large payloads** up to 1GB
+- **Long processing times** without timeout concerns
+- **Cost optimization** through efficient resource utilization
+
+```python
+import sagemaker
+from sagemaker.model import Model
+from sagemaker.async_inference import AsyncInferenceConfig
+
+# Configure async inference
+async_config = AsyncInferenceConfig(
+ output_path="s3://my-bucket/async-results/",
+ max_concurrent_invocations_per_instance=4,
+ notification_config={
+ "SuccessTopic": "arn:aws:sns:us-west-2:123456789012:success-topic",
+ "ErrorTopic": "arn:aws:sns:us-west-2:123456789012:error-topic"
+ }
+)
+
+# Create a model
+model = Model(
+ image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.13.1-cpu-py39",
+ model_data="s3://my-bucket/model.tar.gz",
+ role=sagemaker.get_execution_role()
+)
+
+# Deploy with async configuration
+async_predictor = model.deploy(
+ initial_instance_count=1,
+ instance_type="ml.m5.xlarge",
+ async_inference_config=async_config,
+ endpoint_name="my-async-endpoint"
+)
+
+# Submit a request for async processing
+response = async_predictor.predict_async(
+ data="s3://my-bucket/input/data.csv",
+ input_content_type="text/csv"
+)
+```
+
+### Batch Transform
+
+Batch Transform is designed for offline processing of large datasets. It's ideal for scenarios where you need to generate predictions for a complete dataset rather than individual requests.
+
+Key features:
+- **Efficient processing** of large datasets
+- **Automatic scaling** to handle large workloads
+- **Cost-effective** for bulk inference
+- **Integration with data processing pipelines**
+
+```python
+import sagemaker
+from sagemaker.model import Model
+
+# Create a model
+model = Model(
+ image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.13.1-cpu-py39",
+ model_data="s3://my-bucket/model.tar.gz",
+ role=sagemaker.get_execution_role()
+)
+
+# Create a transformer for batch processing
+transformer = model.transformer(
+ instance_count=1,
+ instance_type="ml.m5.xlarge",
+ output_path="s3://my-bucket/batch-output/",
+ strategy="MultiRecord",
+ assemble_with="Line",
+ accept="text/csv"
+)
+
+# Start a batch transform job
+transformer.transform(
+ data="s3://my-bucket/batch-input/",
+ content_type="text/csv",
+ split_type="Line"
+)
+```
+
+### Multi-model Endpoints
+
+Multi-model Endpoints allow you to host multiple models on a single endpoint, sharing compute resources among them. This is ideal for scenarios where you have many similar models that don't all need to be active simultaneously.
+
+Key features:
+- **Cost efficiency** through resource sharing
+- **Dynamic loading** of models into memory
+- **Simplified management** of many models
+- **Reduced overhead** compared to individual endpoints
+
+```python
+import sagemaker
+from sagemaker.multidatamodel import MultiDataModel
+
+# Create a multi-model endpoint
+mme = MultiDataModel(
+ name="my-multi-model-endpoint",
+ model_data_prefix="s3://my-bucket/models/",
+ image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.13.1-cpu-py39",
+ role=sagemaker.get_execution_role()
+)
+
+# Deploy the multi-model endpoint
+predictor = mme.deploy(
+ initial_instance_count=1,
+ instance_type="ml.m5.xlarge"
+)
+
+# Make a prediction with a specific model
+response = predictor.predict(
+ target_model="model1.tar.gz",
+ data=data
+)
+```
+
+## Inference APIs
+
+### ModelBuilder
+
+The `ModelBuilder` class provides a simplified interface for creating and deploying models. It handles the complexity of model creation and deployment with a focus on ease of use.
+
+```python
+from sagemaker.model_builder import ModelBuilder
+from sagemaker.schemas import SchemaBuilder
+
+# Define input and output schemas
+schema_builder = SchemaBuilder()
+schema_builder.add_request_content_type("application/json")
+schema_builder.add_response_content_type("application/json")
+
+# Create a model with ModelBuilder
+model_builder = ModelBuilder(
+ model_name="my-model",
+ role=sagemaker.get_execution_role(),
+ schema=schema_builder.build(),
+ container_config={
+ "image_uri": "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.13.1-cpu-py39",
+ "model_data_url": "s3://my-bucket/model.tar.gz"
+ }
+)
+
+# Deploy the model
+predictor = model_builder.deploy(
+ instance_type="ml.m5.xlarge",
+ initial_instance_count=1
+)
+```
+
+### Model
+
+The `Model` class is the foundation for all deployment options in SageMaker. It encapsulates the model artifacts, inference code, and dependencies needed for deployment.
+
+```python
+from sagemaker.model import Model
+
+model = Model(
+ image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.13.1-cpu-py39",
+ model_data="s3://my-bucket/model.tar.gz",
+ role=sagemaker.get_execution_role(),
+ env={"MODEL_SERVER_WORKERS": "2"}
+)
+```
+
+### Predictors
+
+Predictors are client objects that provide a convenient interface for making inference requests to deployed endpoints.
+
+```python
+# After deploying a model
+predictor = model.deploy(
+ initial_instance_count=1,
+ instance_type="ml.m5.xlarge"
+)
+
+# Make predictions with different content types
+json_response = predictor.predict(data, content_type="application/json")
+csv_response = predictor.predict(data, content_type="text/csv")
+
+# Serialize and deserialize data automatically
+from sagemaker.serializers import JSONSerializer
+from sagemaker.deserializers import JSONDeserializer
+
+predictor.serializer = JSONSerializer()
+predictor.deserializer = JSONDeserializer()
+
+# Now predictor automatically handles serialization/deserialization
+response = predictor.predict({"data": [1, 2, 3, 4]})
+```
+
+## Advanced Deployment Features
+
+### Inference Optimization
+
+SageMaker provides several options to optimize your model for inference:
+
+- **SageMaker Neo** - Automatically optimize models for specific hardware
+- **Elastic Inference** - Add GPU acceleration to CPU instances
+- **Inference Recommender** - Get recommendations for optimal deployment configurations
+
+### Inference Pipelines
+
+Inference Pipelines allow you to chain multiple models and preprocessing/postprocessing steps into a single endpoint:
+
+```python
+from sagemaker.pipeline import PipelineModel
+
+# Create individual models
+preprocessor_model = Model(...)
+inference_model = Model(...)
+postprocessor_model = Model(...)
+
+# Create a pipeline of models
+pipeline_model = PipelineModel(
+ models=[preprocessor_model, inference_model, postprocessor_model],
+ role=sagemaker.get_execution_role()
+)
+
+# Deploy the pipeline
+pipeline_predictor = pipeline_model.deploy(
+ initial_instance_count=1,
+ instance_type="ml.m5.xlarge"
+)
+```
+
+### A/B Testing
+
+SageMaker supports A/B testing through production variants, allowing you to test different models or configurations:
+
+```python
+from sagemaker.model import Model
+
+# Create two model variants
+model_a = Model(...)
+model_b = Model(...)
+
+# Deploy both models to the same endpoint with traffic splitting
+production_variants = [
+ {
+ "VariantName": "ModelA",
+ "ModelName": model_a.name,
+ "InitialInstanceCount": 1,
+ "InstanceType": "ml.m5.xlarge",
+ "InitialVariantWeight": 0.7
+ },
+ {
+ "VariantName": "ModelB",
+ "ModelName": model_b.name,
+ "InitialInstanceCount": 1,
+ "InstanceType": "ml.m5.xlarge",
+ "InitialVariantWeight": 0.3
+ }
+]
+
+# Create the endpoint with both variants
+sagemaker_session = sagemaker.Session()
+sagemaker_session.endpoint_from_production_variants(
+ name="ab-test-endpoint",
+ production_variants=production_variants
+)
+```
+
+### Model Monitoring
+
+SageMaker Model Monitor allows you to monitor the quality of your deployed models:
+
+```python
+from sagemaker.model_monitor import DataCaptureConfig, ModelMonitor
+
+# Configure data capture for the endpoint
+data_capture_config = DataCaptureConfig(
+ enable_capture=True,
+ sampling_percentage=100,
+ destination_s3_uri="s3://my-bucket/monitor-data/"
+)
+
+# Deploy model with monitoring enabled
+predictor = model.deploy(
+ initial_instance_count=1,
+ instance_type="ml.m5.xlarge",
+ data_capture_config=data_capture_config
+)
+
+# Create a model monitor
+monitor = ModelMonitor(
+ role=sagemaker.get_execution_role(),
+ instance_count=1,
+ instance_type="ml.m5.xlarge"
+)
+
+# Create a monitoring schedule
+monitor.create_monitoring_schedule(
+ monitor_schedule_name="my-monitoring-schedule",
+ endpoint_input=predictor.endpoint_name,
+ statistics="s3://my-bucket/baseline/statistics.json",
+ constraints="s3://my-bucket/baseline/constraints.json",
+ schedule_cron_expression="cron(0 * ? * * *)" # Hourly
+)
+```
diff --git a/doc/doc_utils/jumpstart_doc_utils.py b/doc/doc_utils/jumpstart_doc_utils.py
index 458da694d5..1fc47437f5 100644
--- a/doc/doc_utils/jumpstart_doc_utils.py
+++ b/doc/doc_utils/jumpstart_doc_utils.py
@@ -351,7 +351,7 @@ def create_jumpstart_model_table():
proprietary_content_entries = create_proprietary_model_table()
- f = open("doc_utils/pretrainedmodels.rst", "a")
+ f = open("doc_utils/pretrainedmodels.rst", "w")
f.writelines(file_content_intro)
f.writelines(open_weight_content_entries)
f.writelines(proprietary_content_entries)
diff --git a/doc/doc_utils/pretrainedmodels.rst b/doc/doc_utils/pretrainedmodels.rst
index e69de29bb2..56651191c1 100644
--- a/doc/doc_utils/pretrainedmodels.rst
+++ b/doc/doc_utils/pretrainedmodels.rst
@@ -0,0 +1,6428 @@
+.. _all-pretrained-models:
+
+.. |external-link| raw:: html
+
+
+
+================================================
+Built-in Algorithms with pre-trained Model Table
+================================================
+
+ The SageMaker Python SDK uses model IDs and model versions to access the necessary
+ utilities for pre-trained models. This table serves to provide the core material plus
+ some extra information that can be useful in selecting the correct model ID and
+ corresponding parameters.
+
+ If you want to automatically use the latest version of the model, use "*" for the `model_version` attribute.
+ We highly suggest pinning an exact model version however.
+
+ These models are also available through the
+ `JumpStart UI in SageMaker Studio `__
+
+.. list-table:: Available Models
+ :widths: 50 20 20 20 30 20
+ :header-rows: 1
+ :class: datatable
+
+ * - Model ID
+ - Fine Tunable?
+ - Latest Version
+ - Min SDK Version
+ - Problem Type
+ - Source
+ * - autogluon-classification-ensemble
+ - True
+ - 2.0.15
+ - 2.189.0
+ - Classification
+ - `GluonCV `__ |external-link|
+ * - autogluon-forecasting-chronos-bolt-base
+ - False
+ - 2.0.5
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - autogluon-forecasting-chronos-bolt-small
+ - False
+ - 1.0.8
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - autogluon-forecasting-chronos-t5-base
+ - False
+ - 2.0.8
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - autogluon-forecasting-chronos-t5-large
+ - False
+ - 2.0.8
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - autogluon-forecasting-chronos-t5-small
+ - False
+ - 2.0.8
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - autogluon-regression-ensemble
+ - True
+ - 2.0.15
+ - 2.189.0
+ - Regression
+ - `GluonCV `__ |external-link|
+ * - catboost-classification-model
+ - True
+ - 2.1.21
+ - 2.189.0
+ - Classification
+ - `Catboost `__ |external-link|
+ * - catboost-regression-model
+ - True
+ - 2.1.21
+ - 2.189.0
+ - Regression
+ - `Catboost `__ |external-link|
+ * - deepseek-llm-r1
+ - False
+ - 4.0.7
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - deepseek-llm-r1-0528
+ - False
+ - 1.0.2
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - deepseek-llm-r1-distill-llama-70b
+ - False
+ - 2.0.7
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - deepseek-llm-r1-distill-llama-8b
+ - False
+ - 2.0.7
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - deepseek-llm-r1-distill-qwen-1-5b
+ - False
+ - 2.0.7
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - deepseek-llm-r1-distill-qwen-14b
+ - False
+ - 2.0.7
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - deepseek-llm-r1-distill-qwen-32b
+ - False
+ - 2.0.7
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - deepseek-llm-r1-distill-qwen-7b
+ - False
+ - 2.0.7
+ - 2.237.1
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-asr-whisper-base
+ - False
+ - 3.0.13
+ - 2.189.0
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-asr-whisper-large
+ - False
+ - 3.0.13
+ - 2.189.0
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-asr-whisper-large-v2
+ - False
+ - 3.0.13
+ - 2.189.0
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-asr-whisper-large-v3
+ - False
+ - 1.0.13
+ - 2.189.0
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-asr-whisper-large-v3-turbo
+ - False
+ - 1.1.10
+ - 2.189.0
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-asr-whisper-medium
+ - False
+ - 3.0.13
+ - 2.189.0
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-asr-whisper-small
+ - False
+ - 3.0.13
+ - 2.189.0
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-asr-whisper-tiny
+ - False
+ - 3.0.13
+ - 2.189.0
+ - Source
+ - `HuggingFace `__ |external-link|
+ * - huggingface-eqa-bert-base-cased
+ - True
+ - 3.0.6
+ - 2.189.0
+ - Question Answering
+ - `HuggingFace `__ |external-link|
+ * - huggingface-eqa-bert-base-multilingual-cased
+ - True
+ - 3.0.6
+ - 2.189.0
+ - Question Answering
+ - `HuggingFace `__ |external-link|
+ * - huggingface-eqa-bert-base-multilingual-uncased
+ - True
+ - 3.0.6
+ - 2.237.1
+ - Question Answering
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