def testDocumentSuccess(self):
documented_test_key = tfx_doc_controls.documented(
'test key', 'test value')
self.assertEqual(1, len(tfx_doc_controls.EXTRA_DOCS))
self.assertEqual(
'test value',
tfx_doc_controls.EXTRA_DOCS.get(id(documented_test_key)))
from __future__ import division
from __future__ import print_function
from typing import Any, Dict, List, Text
import absl
from tfx import types
from tfx.components.trainer import executor as tfx_trainer_executor
from tfx.dsl.components.base import base_executor
from tfx.extensions.google_cloud_ai_platform import runner
from tfx.types import standard_component_specs
from tfx.utils import doc_controls
from tfx.utils import json_utils
TRAINING_ARGS_KEY = doc_controls.documented(
obj='ai_platform_training_args',
doc='Keys to the items in custom_config of Trainer for passing '
'training_args to AI Platform.')
JOB_ID_KEY = doc_controls.documented(
obj='ai_platform_training_job_id',
doc='Keys to the items in custom_config of Trainer for specifying job id.')
ENABLE_UCAIP_KEY = doc_controls.documented(
obj='ai_platform_training_enable_ucaip',
doc='Keys to the items in custom_config of Trainer for enabling uCAIP '
'Training.')
UCAIP_REGION_KEY = doc_controls.documented(
obj='ai_platform_training_ucaip_region',
doc='Keys to the items in custom_config of Trainer for specify the region '
'of uCAIP.')
# 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.
"""Google Cloud AI Platform constants module."""
from tfx.utils import doc_controls
ENABLE_VERTEX_KEY = doc_controls.documented(
obj='ai_platform_enable_vertex',
doc='Keys to the items in custom_config of Trainer and Pusher for enabling'
' Vertex AI.')
VERTEX_REGION_KEY = doc_controls.documented(
obj='ai_platform_vertex_region',
doc='Keys to the items in custom_config of Trainer and Pusher for '
'specifying the region of Vertex AI.')
# Prediction container registry: https://gcr.io/cloud-aiplatform/prediction.
VERTEX_CONTAINER_IMAGE_URI_KEY = doc_controls.documented(
obj='ai_platform_vertex_container_image_uri',
doc='Keys to the items in custom_config of Pusher/BulkInferrer for the '
'serving container image URI in Vertex AI.')
# Keys to the items in custom_config passed as a part of exec_properties.
SERVING_ARGS_KEY = doc_controls.documented(
from absl import logging
from tfx import types
from tfx.components.tuner import executor as tuner_executor
from tfx.dsl.components.base import base_executor
from tfx.extensions.google_cloud_ai_platform import constants
from tfx.extensions.google_cloud_ai_platform import runner
from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor
from tfx.types import standard_component_specs
from tfx.utils import doc_controls
from tfx.utils import json_utils
TUNING_ARGS_KEY = doc_controls.documented(
obj='ai_platform_tuning_args',
doc='Keys to the items in custom_config of Tuner for passing '
'training_job to AI Platform, and the GCP project under which '
'the training job will be executed. In Vertex AI, this corresponds to '
'a CustomJob as defined in:'
'https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.customJobs#CustomJob.'
'In CAIP, this corresponds to TrainingInputs as defined in:'
'https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#TrainingInput'
)
REMOTE_TRIALS_WORKING_DIR_KEY = doc_controls.documented(
obj='remote_trials_working_dir',
doc='Keys to the items in custom_config of Tuner for specifying a working '
'dir for remote trial.')
# Directory to store intermediate hyperparamter search progress.
# TODO(b/160188053): Use the same temp dir as the calling Executor.
_WORKING_DIRECTORY = '/tmp'
from tfx import types
from tfx.components.pusher import executor as tfx_pusher_executor
from tfx.types import artifact_utils
from tfx.types import standard_component_specs
from tfx.utils import doc_controls
from tfx.utils import io_utils
from tfx.utils import json_utils
from tfx.utils import telemetry_utils
_POLLING_INTERVAL_IN_SECONDS = 30
_GCS_PREFIX = 'gs://'
# Keys to the items in custom_config passed as a part of exec_properties.
SERVING_ARGS_KEY = doc_controls.documented(
obj='bigquery_serving_args',
doc='Keys to the items in custom_config of Pusher for passing serving args '
'to Big Query.')
# BigQueryML serving argument keys
_PROJECT_ID_KEY = 'project_id'
_BQ_DATASET_ID_KEY = 'bq_dataset_id'
_MODEL_NAME_KEY = 'model_name'
# Project where query will be executed
_COMPUTE_PROJECT_ID_KEY = 'compute_project_id'
# Keys for custom_config.
_CUSTOM_CONFIG_KEY = 'custom_config'
# Model name should be enclosed within backticks.
# model_path should ends with asterisk glob (/*).
from tensorflow.python.saved_model import loader_impl # pylint:disable=g-direct-tensorflow-import
# TODO(b/140306674): Stop using the internal TF API.
_CLOUD_PUSH_DESTINATION_RE = re.compile(
r'^projects\/([^\/]+)\/models\/([^\/]+)\/versions\/([^\/]+)$')
_CLOUD_PUSH_DESTINATION_RE_DEFAULT_VERSION = re.compile(
r'^projects\/([^\/]+)\/models\/([^\/]+)$')
# We define the following aliases of Any because the actual types are not
# public.
_SignatureDef = Any
# Keys to the items in custom_config passed as a part of exec_properties.
SERVING_ARGS_KEY = doc_controls.documented(
obj='ai_platform_serving_args',
doc='Keys to the items in custom_config of Bulk Inferrer for passing bulk'
'inferrer args to AI Platform.')
# Keys for custom_config.
_CUSTOM_CONFIG_KEY = 'custom_config'
class Executor(bulk_inferrer_executor.Executor):
"""Bulk inferer executor for inference on AI Platform."""
def Do(self, input_dict: Dict[str, List[types.Artifact]],
output_dict: Dict[str, List[types.Artifact]],
exec_properties: Dict[str, Any]) -> None:
"""Runs batch inference on a given model with given input examples.
This function creates a new model (if necessary) and a new model version
before inference, and cleans up resources after inference. It provides
re-executability as it cleans up (only) the model resources that are created
import absl
from tfx import types
from tfx.components.trainer import executor as tfx_trainer_executor
from tfx.dsl.components.base import base_executor
from tfx.extensions.google_cloud_ai_platform import runner
from tfx.extensions.google_cloud_ai_platform.constants import ENABLE_VERTEX_KEY
from tfx.extensions.google_cloud_ai_platform.constants import VERTEX_REGION_KEY
from tfx.types import standard_component_specs
from tfx.utils import doc_controls
from tfx.utils import json_utils
TRAINING_ARGS_KEY = doc_controls.documented(
obj='ai_platform_training_args',
doc='Keys to the items in custom_config of Trainer for passing '
'training_job to AI Platform, and the GCP project under which '
'the training job will be executed. In Vertex AI, this corresponds to '
'a CustomJob as defined in:'
'https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.customJobs#CustomJob.'
'In CAIP, this corresponds to TrainingInputs as defined in:'
'https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs#TrainingInput'
)
JOB_ID_KEY = doc_controls.documented(
obj='ai_platform_training_job_id',
doc='Keys to the items in custom_config of Trainer for specifying job id.')
ENABLE_UCAIP_KEY = doc_controls.documented(
obj='ai_platform_training_enable_ucaip',
doc='Deprecated. Please use ENABLE_VERTEX_KEY instead. Keys to the items in'
' custom_config of Trainer for enabling uCAIP Training. ')
UCAIP_REGION_KEY = doc_controls.documented(
import multiprocessing
import os
from typing import Any, Dict, List, Text
from absl import logging
from tfx import types
from tfx.components.tuner import executor as tuner_executor
from tfx.dsl.components.base import base_executor
from tfx.extensions.google_cloud_ai_platform import runner
from tfx.extensions.google_cloud_ai_platform.trainer import executor as ai_platform_trainer_executor
from tfx.types import standard_component_specs
from tfx.utils import doc_controls
from tfx.utils import json_utils
TUNING_ARGS_KEY = doc_controls.documented(
obj='ai_platform_tuning_args',
doc='Keys to the items in custom_config of Tuner for passing tuning args '
'to AI Platform.')
REMOTE_TRIALS_WORKING_DIR_KEY = doc_controls.documented(
obj='remote_trials_working_dir',
doc='Keys to the items in custom_config of Tuner for specifying a working '
'dir for remote trial.')
# Directory to store intermediate hyperparamter search progress.
# TODO(b/160188053): Use the same temp dir as the calling Executor.
_WORKING_DIRECTORY = '/tmp'
class Executor(base_executor.BaseExecutor):
"""Tuner executor that launches parallel tuning flock on Cloud AI Platform.
from tfx.extensions.google_cloud_ai_platform import runner
from tfx.types import artifact_utils
from tfx.types import standard_component_specs
from tfx.utils import doc_controls
from tfx.utils import io_utils
from tfx.utils import json_utils
from tfx.utils import telemetry_utils
# Google Cloud AI Platform's ModelVersion resource path format.
# https://cloud.google.com/ai-platform/prediction/docs/reference/rest/v1/projects.models.versions/get
_CAIP_MODEL_VERSION_PATH_FORMAT = (
'projects/{project_id}/models/{model}/versions/{version}')
# Keys to the items in custom_config passed as a part of exec_properties.
SERVING_ARGS_KEY = doc_controls.documented(
obj='ai_platform_serving_args',
doc='Keys to the items in custom_config of Pusher for passing serving args '
'to AI Platform.')
ENDPOINT_ARGS_KEY = doc_controls.documented(
obj='endpoint',
doc='Keys to the items in custom_config of Pusher for optional endpoint '
'override.')
# Keys for custom_config.
_CUSTOM_CONFIG_KEY = 'custom_config'
class Executor(tfx_pusher_executor.Executor):
"""Deploy a model to Google Cloud AI Platform serving."""
def Do(self, input_dict: Dict[Text, List[types.Artifact]],
output_dict: Dict[Text, List[types.Artifact]],
