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test_models.py
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# -*- coding: utf-8 -*-
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
#
import importlib
from concurrent import futures
import json
import pathlib
import pytest
import requests
from datetime import datetime
from unittest import mock
from unittest.mock import patch
from urllib import request
from google.api_core import operation as ga_operation
from google.api_core import exceptions as api_exceptions
from google.auth import credentials as auth_credentials
from google.cloud import storage
from google.cloud import aiplatform
from google.cloud.aiplatform import base, explain
from google.cloud.aiplatform import initializer
from google.cloud.aiplatform import models
from google.cloud.aiplatform import utils
from google.cloud.aiplatform.utils import gcs_utils
from google.cloud.aiplatform.metadata import constants as metadata_constants
from google.cloud.aiplatform import constants
from google.cloud.aiplatform.preview import models as preview_models
from google.cloud.aiplatform.compat.services import (
deployment_resource_pool_service_client,
deployment_resource_pool_service_client_v1beta1,
endpoint_service_client,
endpoint_service_client_v1beta1,
model_service_client,
job_service_client,
pipeline_service_client,
)
from google.cloud.aiplatform.compat.types import (
batch_prediction_job as gca_batch_prediction_job,
deployment_resource_pool as gca_deployment_resource_pool,
deployment_resource_pool_v1beta1 as gca_deployment_resource_pool_v1beta1,
encryption_spec as gca_encryption_spec,
endpoint as gca_endpoint,
endpoint_service as gca_endpoint_service,
endpoint_v1beta1 as gca_endpoint_v1beta1,
endpoint_service_v1beta1 as gca_endpoint_service_v1beta1,
env_var as gca_env_var,
explanation as gca_explanation,
io as gca_io,
job_state as gca_job_state,
machine_resources as gca_machine_resources,
machine_resources_v1beta1 as gca_machine_resources_v1beta1,
reservation_affinity_v1 as gca_reservation_affinity_v1,
manual_batch_tuning_parameters as gca_manual_batch_tuning_parameters_compat,
model as gca_model,
model_evaluation as gca_model_evaluation,
model_service as gca_model_service,
pipeline_job as gca_pipeline_job,
pipeline_state as gca_pipeline_state,
context as gca_context,
)
from google.cloud.aiplatform.prediction import LocalModel
from google.cloud.aiplatform_v1 import Execution as GapicExecution
from google.cloud.aiplatform.model_evaluation import model_evaluation_job
from google.protobuf import (
field_mask_pb2,
struct_pb2,
timestamp_pb2,
duration_pb2,
)
import constants as test_constants
_TEST_PROJECT = test_constants.ProjectConstants._TEST_PROJECT
_TEST_PROJECT_2 = "test-project-2"
_TEST_LOCATION = test_constants.ProjectConstants._TEST_LOCATION
_TEST_LOCATION_2 = "europe-west4"
_TEST_PARENT = test_constants.ProjectConstants._TEST_PARENT
_TEST_MODEL_NAME = test_constants.ModelConstants._TEST_MODEL_NAME
_TEST_MODEL_NAME_ALT = "456"
_TEST_MODEL_ID = "my-model"
_TEST_MODEL_PARENT = test_constants.ModelConstants._TEST_MODEL_PARENT
_TEST_MODEL_PARENT_ALT = (
f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/models/{_TEST_MODEL_NAME_ALT}"
)
_TEST_ARTIFACT_URI = "gs://test/artifact/uri"
_TEST_SERVING_CONTAINER_IMAGE = (
test_constants.ModelConstants._TEST_SERVING_CONTAINER_IMAGE
)
_TEST_SERVING_CONTAINER_PREDICTION_ROUTE = "predict"
_TEST_SERVING_CONTAINER_HEALTH_ROUTE = "metadata"
_TEST_DESCRIPTION = "test description"
_TEST_SERVING_CONTAINER_COMMAND = ["python3", "run_my_model.py"]
_TEST_SERVING_CONTAINER_ARGS = ["--test", "arg"]
_TEST_SERVING_CONTAINER_ENVIRONMENT_VARIABLES = {
"learning_rate": 0.01,
"loss_fn": "mse",
}
_TEST_SERVING_CONTAINER_PORTS = [8888, 10000]
_TEST_SERVING_CONTAINER_GRPC_PORTS = [7777, 7000]
_TEST_SERVING_CONTAINER_DEPLOYMENT_TIMEOUT = 100
_TEST_SERVING_CONTAINER_SHARED_MEMORY_SIZE_MB = 1000
_TEST_SERVING_CONTAINER_STARTUP_PROBE_EXEC = ["a", "b"]
_TEST_SERVING_CONTAINER_STARTUP_PROBE_PERIOD_SECONDS = 5
_TEST_SERVING_CONTAINER_STARTUP_PROBE_TIMEOUT_SECONDS = 100
_TEST_SERVING_CONTAINER_HEALTH_PROBE_EXEC = ["c", "d"]
_TEST_SERVING_CONTAINER_HEALTH_PROBE_PERIOD_SECONDS = 20
_TEST_SERVING_CONTAINER_HEALTH_PROBE_TIMEOUT_SECONDS = 200
_TEST_ID = "1028944691210842416"
_TEST_LABEL = test_constants.ProjectConstants._TEST_LABELS
_TEST_APPENDED_USER_AGENT = ["fake_user_agent", "another_fake_user_agent"]
_TEST_MACHINE_TYPE = "n1-standard-4"
_TEST_REQUIRED_REPLICA_COUNT = 1
_TEST_ACCELERATOR_TYPE = "NVIDIA_TESLA_P100"
_TEST_ACCELERATOR_COUNT = 2
_TEST_STARTING_REPLICA_COUNT = 2
_TEST_MAX_REPLICA_COUNT = 12
_TEST_SPOT = True
_TEST_RESERVATION_AFFINITY_TYPE = "SPECIFIC_RESERVATION"
_TEST_RESERVATION_AFFINITY_KEY = "compute.googleapis.com/reservation-name"
_TEST_RESERVATION_AFFINITY_VALUES = [
"projects/fake-project-id/zones/fake-zone/reservations/fake-reservation-name"
]
_TEST_TPU_MACHINE_TYPE = "ct5lp-hightpu-4t"
_TEST_TPU_TOPOLOGY = "2x2"
_TEST_BATCH_SIZE = 16
_TEST_PIPELINE_RESOURCE_NAME = (
"projects/my-project/locations/us-central1/trainingPipeline/12345"
)
_TEST_BATCH_PREDICTION_GCS_SOURCE = "gs://example-bucket/folder/instance.jsonl"
_TEST_BATCH_PREDICTION_GCS_SOURCE_LIST = [
"gs://example-bucket/folder/instance1.jsonl",
"gs://example-bucket/folder/instance2.jsonl",
]
_TEST_BATCH_PREDICTION_GCS_DEST_PREFIX = "gs://example-bucket/folder/output"
_TEST_BATCH_PREDICTION_BQ_PREFIX = "ucaip-sample-tests"
_TEST_BATCH_PREDICTION_BQ_DEST_PREFIX_WITH_PROTOCOL = (
f"bq://{_TEST_BATCH_PREDICTION_BQ_PREFIX}"
)
_TEST_BATCH_PREDICTION_DISPLAY_NAME = "test-batch-prediction-job"
_TEST_BATCH_PREDICTION_JOB_NAME = (
job_service_client.JobServiceClient.batch_prediction_job_path(
project=_TEST_PROJECT, location=_TEST_LOCATION, batch_prediction_job=_TEST_ID
)
)
_TEST_INSTANCE_SCHEMA_URI = "gs://test/schema/instance.yaml"
_TEST_PARAMETERS_SCHEMA_URI = "gs://test/schema/parameters.yaml"
_TEST_PREDICTION_SCHEMA_URI = "gs://test/schema/predictions.yaml"
_TEST_CREDENTIALS = mock.Mock(spec=auth_credentials.AnonymousCredentials())
_TEST_SERVICE_ACCOUNT = "[email protected]"
_TEST_MODEL_GARDEN_SOURCE_MODEL_NAME = "publishers/meta/models/llama3_1"
_TEST_EXPLANATION_METADATA = explain.ExplanationMetadata(
inputs={
"features": {
"input_tensor_name": "dense_input",
"encoding": "BAG_OF_FEATURES",
"modality": "numeric",
"index_feature_mapping": ["abc", "def", "ghj"],
}
},
outputs={"medv": {"output_tensor_name": "dense_2"}},
)
_TEST_EXPLANATION_PARAMETERS = (
test_constants.ModelConstants._TEST_EXPLANATION_PARAMETERS
)
_TEST_EXPLANATION_METADATA_EXAMPLES = explain.ExplanationMetadata(
outputs={"embedding": {"output_tensor_name": "embedding"}},
inputs={
"my_input": {
"input_tensor_name": "bytes_inputs",
"encoding": "IDENTITY",
"modality": "image",
},
"id": {"input_tensor_name": "id", "encoding": "IDENTITY"},
},
)
_TEST_EXPLANATION_PARAMETERS_EXAMPLES_PRESETS = explain.ExplanationParameters(
{
"examples": {
"example_gcs_source": {
"gcs_source": {
"uris": ["gs://example-bucket/folder/instance1.jsonl"],
},
},
"neighbor_count": 10,
"presets": {"query": "FAST", "modality": "TEXT"},
}
}
)
_TEST_EXPLANATION_PARAMETERS_EXAMPLES_FULL_CONFIG = explain.ExplanationParameters(
{
"examples": {
"example_gcs_source": {
"gcs_source": {
"uris": ["gs://example-bucket/folder/instance1.jsonl"],
},
},
"neighbor_count": 10,
"nearest_neighbor_search_config": [
{
"contentsDeltaUri": "",
"config": {
"dimensions": 50,
"approximateNeighborsCount": 10,
"distanceMeasureType": "SQUARED_L2_DISTANCE",
"featureNormType": "NONE",
"algorithmConfig": {
"treeAhConfig": {
"leafNodeEmbeddingCount": 1000,
"leafNodesToSearchPercent": 100,
}
},
},
}
],
}
}
)
# CMEK encryption
_TEST_ENCRYPTION_KEY_NAME = "key_1234"
_TEST_ENCRYPTION_SPEC = gca_encryption_spec.EncryptionSpec(
kms_key_name=_TEST_ENCRYPTION_KEY_NAME
)
_TEST_MODEL_RESOURCE_NAME = test_constants.ModelConstants._TEST_MODEL_RESOURCE_NAME
_TEST_MODEL_RESOURCE_NAME_CUSTOM_PROJECT = (
model_service_client.ModelServiceClient.model_path(
_TEST_PROJECT_2, _TEST_LOCATION, _TEST_ID
)
)
_TEST_MODEL_RESOURCE_NAME_CUSTOM_LOCATION = (
model_service_client.ModelServiceClient.model_path(
_TEST_PROJECT, _TEST_LOCATION_2, _TEST_ID
)
)
_TEST_OUTPUT_DIR = "gs://my-output-bucket"
_TEST_CONTAINER_REGISTRY_DESTINATION = (
"us-central1-docker.pkg.dev/projectId/repoName/imageName"
)
_TEST_EXPORT_FORMAT_ID_IMAGE = "custom-trained"
_TEST_EXPORT_FORMAT_ID_ARTIFACT = "tf-saved-model"
_TEST_SUPPORTED_EXPORT_FORMATS_IMAGE = [
gca_model.Model.ExportFormat(
id=_TEST_EXPORT_FORMAT_ID_IMAGE,
exportable_contents=[gca_model.Model.ExportFormat.ExportableContent.IMAGE],
)
]
_TEST_SUPPORTED_EXPORT_FORMATS_ARTIFACT = [
gca_model.Model.ExportFormat(
id=_TEST_EXPORT_FORMAT_ID_ARTIFACT,
exportable_contents=[gca_model.Model.ExportFormat.ExportableContent.ARTIFACT],
)
]
_TEST_SUPPORTED_EXPORT_FORMATS_BOTH = [
gca_model.Model.ExportFormat(
id=_TEST_EXPORT_FORMAT_ID_ARTIFACT,
exportable_contents=[
gca_model.Model.ExportFormat.ExportableContent.ARTIFACT,
gca_model.Model.ExportFormat.ExportableContent.IMAGE,
],
)
]
_TEST_SUPPORTED_EXPORT_FORMATS_UNSUPPORTED = []
# Model Evaluation
_TEST_MODEL_EVAL_RESOURCE_NAME = f"{_TEST_MODEL_RESOURCE_NAME}/evaluations/{_TEST_ID}"
_TEST_MODEL_EVAL_METRICS = test_constants.ModelConstants._TEST_MODEL_EVAL_METRICS
_TEST_MODEL_EVAL_LIST = [
gca_model_evaluation.ModelEvaluation(
name=_TEST_MODEL_EVAL_RESOURCE_NAME,
),
gca_model_evaluation.ModelEvaluation(
name=_TEST_MODEL_EVAL_RESOURCE_NAME,
),
gca_model_evaluation.ModelEvaluation(
name=_TEST_MODEL_EVAL_RESOURCE_NAME,
),
]
# model.evaluate
_TEST_PIPELINE_JOB_ID = "sample-test-pipeline-202111111"
_TEST_PIPELINE_JOB_NAME = f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/pipelineJobs/{_TEST_PIPELINE_JOB_ID}"
_TEST_PIPELINE_CREATE_TIME = datetime.now()
_TEST_NETWORK = f"projects/{_TEST_PROJECT}/global/networks/{_TEST_PIPELINE_JOB_ID}"
_TEST_MODEL_EVAL_CLASS_LABELS = ["dog", "cat", "rabbit"]
_TEST_BIGQUERY_EVAL_INPUT_URI = "bq://my-project.my-dataset.my-table"
_TEST_BIGQUERY_EVAL_DESTINATION_URI = "bq://my-project.my-dataset"
_TEST_EVAL_RESOURCE_DISPLAY_NAME = "my-eval-resource-display-name"
_TEST_GCS_BUCKET_NAME = "my-bucket"
_TEST_MODEL_EVAL_PIPELINE_PARAMETER_VALUES = {
"batch_predict_gcs_source_uris": ["gs://my-bucket/my-prediction-data.csv"],
"dataflow_service_account": _TEST_SERVICE_ACCOUNT,
"batch_predict_instances_format": "csv",
"model_name": _TEST_MODEL_RESOURCE_NAME,
"evaluation_display_name": _TEST_EVAL_RESOURCE_DISPLAY_NAME,
"prediction_type": "classification",
"project": _TEST_PROJECT,
"location": _TEST_LOCATION,
"batch_predict_gcs_destination_output_uri": _TEST_GCS_BUCKET_NAME,
"target_field_name": "predict_class",
}
_TEST_MODEL_EVAL_PIPELINE_SPEC_JSON = json.dumps(
{
"pipelineInfo": {"name": "evaluation-default-pipeline"},
"root": {
"dag": {"tasks": {}},
"inputDefinitions": {
"parameters": {
"batch_predict_gcs_source_uris": {"type": "STRING"},
"dataflow_service_account": {"type": "STRING"},
"batch_predict_instances_format": {"type": "STRING"},
"batch_predict_machine_type": {"type": "STRING"},
"location": {"type": "STRING"},
"model_name": {"type": "STRING"},
"prediction_type": {"type": "STRING"},
"project": {"type": "STRING"},
"batch_predict_gcs_destination_output_uri": {"type": "STRING"},
"target_field_name": {"type": "STRING"},
}
},
},
"schemaVersion": "2.0.0",
"sdkVersion": "kfp-1.8.12",
"components": {},
}
)
_TEST_MODEL_EVAL_PIPELINE_JOB = json.dumps(
{
"runtimeConfig": {"parameters": _TEST_MODEL_EVAL_PIPELINE_PARAMETER_VALUES},
"pipelineInfo": {"name": "evaluation-default-pipeline"},
"root": {
"dag": {"tasks": {}},
"inputDefinitions": {
"parameters": {
"batch_predict_gcs_source_uris": {"type": "STRING"},
"dataflow_service_account": {"type": "STRING"},
"evaluation_class_labels": {"type": "STRING"},
"batch_predict_instances_format": {"type": "STRING"},
"batch_predict_machine_type": {"type": "STRING"},
"location": {"type": "STRING"},
"model_name": {"type": "STRING"},
"prediction_type": {"type": "STRING"},
"project": {"type": "STRING"},
"batch_predict_gcs_destination_output_uri": {"type": "STRING"},
"target_field_name": {"type": "STRING"},
}
},
},
"schemaVersion": "2.0.0",
"sdkVersion": "kfp-1.8.12",
"components": {},
}
)
_TEST_MODEL_EVAL_PIPELINE_JOB_WITH_BQ_INPUT = json.dumps(
{
"runtimeConfig": {"parameters": _TEST_MODEL_EVAL_PIPELINE_PARAMETER_VALUES},
"pipelineInfo": {"name": "evaluation-default-pipeline"},
"root": {
"dag": {"tasks": {}},
"inputDefinitions": {
"parameters": {
"batch_predict_gcs_source_uris": {"type": "STRING"},
"dataflow_service_account": {"type": "STRING"},
"evaluation_class_labels": {"type": "STRING"},
"batch_predict_instances_format": {"type": "STRING"},
"batch_predict_predictions_format": {"type": "STRING"},
"batch_predict_bigquery_source_uri": {"type": "STRING"},
"batch_predict_bigquery_destination_output_uri": {"type": "STRING"},
"batch_predict_machine_type": {"type": "STRING"},
"location": {"type": "STRING"},
"model_name": {"type": "STRING"},
"prediction_type": {"type": "STRING"},
"project": {"type": "STRING"},
"batch_predict_gcs_destination_output_uri": {"type": "STRING"},
"target_field_name": {"type": "STRING"},
}
},
},
"schemaVersion": "2.0.0",
"sdkVersion": "kfp-1.8.12",
"components": {},
}
)
_TEST_LOCAL_MODEL = LocalModel(
serving_container_image_uri=_TEST_SERVING_CONTAINER_IMAGE,
serving_container_predict_route=_TEST_SERVING_CONTAINER_PREDICTION_ROUTE,
serving_container_health_route=_TEST_SERVING_CONTAINER_HEALTH_ROUTE,
)
_TEST_VERSION_ID = test_constants.ModelConstants._TEST_VERSION_ID
_TEST_VERSION_ALIAS_1 = test_constants.ModelConstants._TEST_VERSION_ALIAS_1
_TEST_VERSION_ALIAS_2 = test_constants.ModelConstants._TEST_VERSION_ALIAS_2
_TEST_MODEL_VERSION_DESCRIPTION_1 = "My version 1 description"
_TEST_MODEL_VERSION_DESCRIPTION_2 = (
test_constants.ModelConstants._TEST_MODEL_VERSION_DESCRIPTION_2
)
_TEST_MODEL_VERSION_DESCRIPTION_3 = "My version 3 description"
_TEST_MODEL_VERSIONS_LIST = [
gca_model.Model(
version_id="1",
create_time=timestamp_pb2.Timestamp(),
update_time=timestamp_pb2.Timestamp(),
display_name=_TEST_MODEL_NAME,
name=f"{_TEST_MODEL_PARENT}@1",
version_aliases=["default"],
version_description=_TEST_MODEL_VERSION_DESCRIPTION_1,
),
gca_model.Model(
version_id="2",
create_time=timestamp_pb2.Timestamp(),
update_time=timestamp_pb2.Timestamp(),
display_name=_TEST_MODEL_NAME,
name=f"{_TEST_MODEL_PARENT}@2",
version_aliases=[_TEST_VERSION_ALIAS_1, _TEST_VERSION_ALIAS_2],
version_description=_TEST_MODEL_VERSION_DESCRIPTION_2,
),
gca_model.Model(
version_id="3",
create_time=timestamp_pb2.Timestamp(),
update_time=timestamp_pb2.Timestamp(),
display_name=_TEST_MODEL_NAME,
name=f"{_TEST_MODEL_PARENT}@3",
version_aliases=[],
version_description=_TEST_MODEL_VERSION_DESCRIPTION_3,
labels=_TEST_LABEL,
),
]
_TEST_MODEL_VERSIONS_WITH_FILTER_LIST = [_TEST_MODEL_VERSIONS_LIST[2]]
_TEST_MODELS_LIST = _TEST_MODEL_VERSIONS_LIST + [
gca_model.Model(
version_id="1",
create_time=timestamp_pb2.Timestamp(),
update_time=timestamp_pb2.Timestamp(),
display_name=_TEST_MODEL_NAME_ALT,
name=_TEST_MODEL_PARENT_ALT,
version_aliases=["default"],
version_description=_TEST_MODEL_VERSION_DESCRIPTION_1,
),
]
_TEST_MODEL_OBJ_WITH_VERSION = (
test_constants.ModelConstants._TEST_MODEL_OBJ_WITH_VERSION
)
_TEST_NETWORK = f"projects/{_TEST_PROJECT}/global/networks/{_TEST_ID}"
_TEST_RAW_PREDICT_URL = f"https://{_TEST_LOCATION}-{constants.base.API_BASE_PATH}/v1/projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/endpoints/{_TEST_ID}:rawPredict"
_TEST_RAW_PREDICT_DATA = b""
_TEST_RAW_PREDICT_HEADER = {"Content-Type": "application/json"}
_TEST_DRP_NAME = f"projects/{_TEST_PROJECT}/locations/{_TEST_LOCATION}/deploymentResourcePools/{_TEST_ID}"
_TEST_METRIC_NAME_CPU_UTILIZATION = (
"aiplatform.googleapis.com/prediction/online/cpu/utilization"
)
_TEST_METRIC_NAME_GPU_UTILIZATION = (
"aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle"
)
_TEST_LABELS = {"label1": "value1", "label2": "value2"}
@pytest.fixture
def mock_model():
model = mock.MagicMock(models.Model)
model.name = _TEST_ID
model._latest_future = None
model._exception = None
model._gca_resource = gca_model.Model(
display_name=_TEST_MODEL_NAME, description=_TEST_DESCRIPTION, labels=_TEST_LABEL
)
yield model
@pytest.fixture
def update_model_mock(mock_model):
with patch.object(model_service_client.ModelServiceClient, "update_model") as mock:
mock.return_value = mock_model
yield mock
@pytest.fixture
def authorized_session_mock():
with patch(
"google.auth.transport.requests.AuthorizedSession"
) as MockAuthorizedSession:
mock_auth_session = MockAuthorizedSession(_TEST_CREDENTIALS)
yield mock_auth_session
@pytest.fixture
def raw_predict_mock(authorized_session_mock):
with patch.object(authorized_session_mock, "post") as mock_post:
mock_post.return_value = requests.models.Response()
yield mock_post
@pytest.fixture
def get_endpoint_mock():
with mock.patch.object(
endpoint_service_client.EndpointServiceClient, "get_endpoint"
) as get_endpoint_mock:
test_endpoint_resource_name = (
endpoint_service_client.EndpointServiceClient.endpoint_path(
_TEST_PROJECT, _TEST_LOCATION, _TEST_ID
)
)
get_endpoint_mock.return_value = gca_endpoint.Endpoint(
display_name=_TEST_MODEL_NAME,
name=test_endpoint_resource_name,
)
yield get_endpoint_mock
@pytest.fixture
def create_endpoint_mock():
with mock.patch.object(
endpoint_service_client.EndpointServiceClient, "create_endpoint"
) as create_endpoint_mock:
create_endpoint_lro_mock = mock.Mock(ga_operation.Operation)
create_endpoint_lro_mock.result.return_value = gca_endpoint.Endpoint(
name=test_constants.EndpointConstants._TEST_ENDPOINT_NAME,
display_name=test_constants.EndpointConstants._TEST_DISPLAY_NAME,
)
create_endpoint_mock.return_value = create_endpoint_lro_mock
yield create_endpoint_mock
@pytest.fixture
def get_model_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME,
)
yield get_model_mock
@pytest.fixture
def get_model_with_custom_location_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME_CUSTOM_LOCATION,
)
yield get_model_mock
@pytest.fixture
def get_model_with_custom_project_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME_CUSTOM_PROJECT,
artifact_uri=_TEST_ARTIFACT_URI,
description=_TEST_DESCRIPTION,
)
yield get_model_mock
@pytest.fixture
def get_model_with_training_job():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME_CUSTOM_PROJECT,
training_pipeline=_TEST_PIPELINE_RESOURCE_NAME,
)
yield get_model_mock
@pytest.fixture
def get_model_with_supported_export_formats_image():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME,
supported_export_formats=_TEST_SUPPORTED_EXPORT_FORMATS_IMAGE,
)
yield get_model_mock
@pytest.fixture
def get_model_with_supported_export_formats_artifact():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME,
supported_export_formats=_TEST_SUPPORTED_EXPORT_FORMATS_ARTIFACT,
)
yield get_model_mock
@pytest.fixture
def get_model_with_supported_export_formats_artifact_and_version():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME,
supported_export_formats=_TEST_SUPPORTED_EXPORT_FORMATS_ARTIFACT,
version_id=_TEST_VERSION_ID,
)
yield get_model_mock
@pytest.fixture
def get_model_with_both_supported_export_formats():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME,
supported_export_formats=_TEST_SUPPORTED_EXPORT_FORMATS_BOTH,
)
yield get_model_mock
@pytest.fixture
def get_model_with_unsupported_export_formats():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = gca_model.Model(
display_name=_TEST_MODEL_NAME,
name=_TEST_MODEL_RESOURCE_NAME,
supported_export_formats=_TEST_SUPPORTED_EXPORT_FORMATS_UNSUPPORTED,
)
yield get_model_mock
@pytest.fixture
def get_model_with_version():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model"
) as get_model_mock:
get_model_mock.return_value = _TEST_MODEL_OBJ_WITH_VERSION
yield get_model_mock
@pytest.fixture
def upload_model_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "upload_model"
) as upload_model_mock:
mock_lro = mock.Mock(ga_operation.Operation)
mock_lro.result.return_value = gca_model_service.UploadModelResponse(
model=_TEST_MODEL_RESOURCE_NAME
)
upload_model_mock.return_value = mock_lro
yield upload_model_mock
@pytest.fixture
def upload_model_with_version_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "upload_model"
) as upload_model_mock:
mock_lro = mock.Mock(ga_operation.Operation)
mock_lro.result.return_value = gca_model_service.UploadModelResponse(
model=_TEST_MODEL_RESOURCE_NAME, model_version_id=_TEST_VERSION_ID
)
upload_model_mock.return_value = mock_lro
yield upload_model_mock
@pytest.fixture
def upload_model_with_custom_project_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "upload_model"
) as upload_model_mock:
mock_lro = mock.Mock(ga_operation.Operation)
mock_lro.result.return_value = gca_model_service.UploadModelResponse(
model=_TEST_MODEL_RESOURCE_NAME_CUSTOM_PROJECT
)
upload_model_mock.return_value = mock_lro
yield upload_model_mock
@pytest.fixture
def upload_model_with_custom_location_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "upload_model"
) as upload_model_mock:
mock_lro = mock.Mock(ga_operation.Operation)
mock_lro.result.return_value = gca_model_service.UploadModelResponse(
model=_TEST_MODEL_RESOURCE_NAME_CUSTOM_LOCATION
)
upload_model_mock.return_value = mock_lro
yield upload_model_mock
@pytest.fixture
def export_model_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "export_model"
) as export_model_mock:
export_model_lro_mock = mock.Mock(ga_operation.Operation)
export_model_lro_mock.metadata = gca_model_service.ExportModelOperationMetadata(
output_info=gca_model_service.ExportModelOperationMetadata.OutputInfo(
artifact_output_uri=_TEST_OUTPUT_DIR
)
)
export_model_lro_mock.result.return_value = None
export_model_mock.return_value = export_model_lro_mock
yield export_model_mock
@pytest.fixture
def delete_model_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "delete_model"
) as delete_model_mock:
delete_model_lro_mock = mock.Mock(ga_operation.Operation)
delete_model_lro_mock.result.return_value = (
gca_model_service.DeleteModelRequest()
)
delete_model_mock.return_value = delete_model_lro_mock
yield delete_model_mock
@pytest.fixture
def copy_model_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "copy_model"
) as copy_model_mock:
mock_lro = mock.Mock(ga_operation.Operation)
mock_lro.result.return_value = gca_model_service.CopyModelResponse(
model=_TEST_MODEL_RESOURCE_NAME_CUSTOM_LOCATION
)
copy_model_mock.return_value = mock_lro
yield copy_model_mock
@pytest.fixture
def deploy_model_mock():
with mock.patch.object(
endpoint_service_client.EndpointServiceClient, "deploy_model"
) as deploy_model_mock:
deployed_model = gca_endpoint.DeployedModel(
model=_TEST_MODEL_RESOURCE_NAME,
display_name=_TEST_MODEL_NAME,
)
deploy_model_lro_mock = mock.Mock(ga_operation.Operation)
deploy_model_lro_mock.result.return_value = (
gca_endpoint_service.DeployModelResponse(
deployed_model=deployed_model,
)
)
deploy_model_mock.return_value = deploy_model_lro_mock
yield deploy_model_mock
@pytest.fixture
def get_batch_prediction_job_mock():
with mock.patch.object(
job_service_client.JobServiceClient, "get_batch_prediction_job"
) as get_batch_prediction_job_mock:
batch_prediction_mock = mock.Mock(
spec=gca_batch_prediction_job.BatchPredictionJob
)
batch_prediction_mock.state = gca_job_state.JobState.JOB_STATE_SUCCEEDED
batch_prediction_mock.name = _TEST_BATCH_PREDICTION_JOB_NAME
get_batch_prediction_job_mock.return_value = batch_prediction_mock
yield get_batch_prediction_job_mock
@pytest.fixture
def create_batch_prediction_job_mock():
with mock.patch.object(
job_service_client.JobServiceClient, "create_batch_prediction_job"
) as create_batch_prediction_job_mock:
batch_prediction_job_mock = mock.Mock(
spec=gca_batch_prediction_job.BatchPredictionJob
)
batch_prediction_job_mock.name = _TEST_BATCH_PREDICTION_JOB_NAME
create_batch_prediction_job_mock.return_value = batch_prediction_job_mock
yield create_batch_prediction_job_mock
@pytest.fixture
def get_training_job_non_existent_mock():
with patch.object(
pipeline_service_client.PipelineServiceClient, "get_training_pipeline"
) as get_training_job_non_existent_mock:
get_training_job_non_existent_mock.side_effect = api_exceptions.NotFound("404")
yield get_training_job_non_existent_mock
@pytest.fixture
def create_client_mock():
with mock.patch.object(
initializer.global_config, "create_client"
) as create_client_mock:
api_client_mock = mock.Mock(spec=model_service_client.ModelServiceClient)
api_client_mock.get_model.return_value = _TEST_MODEL_OBJ_WITH_VERSION
create_client_mock.return_value = api_client_mock
yield create_client_mock
@pytest.fixture
def mock_storage_blob_upload_from_filename():
with patch(
"google.cloud.storage.Blob.upload_from_filename"
) as mock_blob_upload_from_filename, patch(
"google.cloud.storage.Bucket.exists", return_value=True
):
yield mock_blob_upload_from_filename
# ModelEvaluation mocks
@pytest.fixture
def mock_model_eval_get():
with mock.patch.object(
model_service_client.ModelServiceClient, "get_model_evaluation"
) as mock_get_model_eval:
mock_get_model_eval.return_value = gca_model_evaluation.ModelEvaluation(
name=_TEST_MODEL_EVAL_RESOURCE_NAME,
metrics=_TEST_MODEL_EVAL_METRICS,
metadata={"pipeline_job_resource_name": _TEST_PIPELINE_JOB_NAME},
)
yield mock_get_model_eval
@pytest.fixture
def mock_get_model_evaluation():
with mock.patch.object(
aiplatform.model_evaluation._ModelEvaluationJob, "get_model_evaluation"
) as mock_get_model_eval:
mock_get_model_eval.return_value = aiplatform.ModelEvaluation(
evaluation_name=_TEST_MODEL_EVAL_RESOURCE_NAME
)
yield mock_get_model_eval
@pytest.fixture
def list_model_evaluations_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "list_model_evaluations"
) as list_model_evaluations_mock:
list_model_evaluations_mock.return_value = _TEST_MODEL_EVAL_LIST
yield list_model_evaluations_mock
@pytest.fixture
def list_model_versions_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "list_model_versions"
) as list_model_versions_mock:
list_model_versions_mock.return_value = _TEST_MODEL_VERSIONS_LIST
yield list_model_versions_mock
@pytest.fixture
def list_model_versions_with_filter_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "list_model_versions"
) as list_model_versions_mock:
list_model_versions_mock.return_value = _TEST_MODEL_VERSIONS_WITH_FILTER_LIST
yield list_model_versions_mock
@pytest.fixture
def list_models_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "list_models"
) as list_models_mock:
list_models_mock.return_value = _TEST_MODELS_LIST
yield list_models_mock
@pytest.fixture
def delete_model_version_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "delete_model_version"
) as delete_model_version_mock:
mock_lro = mock.Mock(ga_operation.Operation)
delete_model_version_mock.return_value = mock_lro
yield delete_model_version_mock
@pytest.fixture
def merge_version_aliases_mock():
with mock.patch.object(
model_service_client.ModelServiceClient, "merge_version_aliases"
) as merge_version_aliases_mock:
merge_version_aliases_mock.return_value = _TEST_MODEL_OBJ_WITH_VERSION
yield merge_version_aliases_mock
# model.evaluate fixtures
@pytest.fixture
def mock_pipeline_service_create():
with mock.patch.object(
pipeline_service_client.PipelineServiceClient, "create_pipeline_job"
) as mock_create_pipeline_job:
mock_create_pipeline_job.return_value = gca_pipeline_job.PipelineJob(
name=_TEST_PIPELINE_JOB_NAME,
state=gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
create_time=_TEST_PIPELINE_CREATE_TIME,
service_account=_TEST_SERVICE_ACCOUNT,
network=_TEST_NETWORK,
)
yield mock_create_pipeline_job
_TEST_COMPONENT_IDENTIFIER = "fpc-model-evaluation"
_TEST_BATCH_PREDICTION_JOB_ID = "614161631630327111"
_TEST_BATCH_PREDICTION_RESOURCE_NAME = (
job_service_client.JobServiceClient.batch_prediction_job_path(
_TEST_PROJECT, _TEST_LOCATION, _TEST_BATCH_PREDICTION_JOB_ID
)
)
_EVAL_GCP_RESOURCES_STR = (
'{\n "resources": [\n {\n "resourceType": "ModelEvaluation",\n "resourceUri": "https://us-central1-aiplatform.googleapis.com/v1/'
+ _TEST_MODEL_EVAL_RESOURCE_NAME
+ '"\n }\n ]\n}'
)
_BP_JOB_GCP_RESOURCES_STR = (
'{\n "resources": [\n {\n "resourceType": "BatchPredictionJob",\n "resourceUri": "https://us-central1-aiplatform.googleapis.com/v1/'
+ _TEST_BATCH_PREDICTION_RESOURCE_NAME
+ '"\n }\n ]\n}'
)
_TEST_PIPELINE_JOB_DETAIL_EVAL = {
"output:gcp_resources": _EVAL_GCP_RESOURCES_STR,
"component_type": _TEST_COMPONENT_IDENTIFIER,
}
_TEST_PIPELINE_JOB_DETAIL_BP = {
"output:gcp_resources": _BP_JOB_GCP_RESOURCES_STR,
}
_TEST_EVAL_METRICS_ARTIFACT_NAME = (
"projects/123/locations/us-central1/metadataStores/default/artifacts/456"
)
_TEST_EVAL_METRICS_ARTIFACT_URI = "gs://test-bucket/eval_pipeline_root/123/evaluation-default-pipeline-20220615135923/model-evaluation-2_-789/evaluation_metrics"