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Deprecate AutoMLTrainModelOperator for Vision and Video (#36473)
Co-authored-by: Ulada Zakharava <Vlada_Zakharava@epam.com>
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tests/system/providers/google/cloud/automl/example_automl_video_classification.py
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| # | ||
| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you 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. | ||
| """ | ||
| Example Airflow DAG that uses Google AutoML services. | ||
| """ | ||
| from __future__ import annotations | ||
|
|
||
| import os | ||
| from datetime import datetime | ||
|
|
||
| from google.cloud.aiplatform import schema | ||
| from google.protobuf.struct_pb2 import Value | ||
|
|
||
| from airflow.models.dag import DAG | ||
| from airflow.providers.google.cloud.operators.gcs import ( | ||
| GCSCreateBucketOperator, | ||
| GCSDeleteBucketOperator, | ||
| GCSSynchronizeBucketsOperator, | ||
| ) | ||
| from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import ( | ||
| CreateAutoMLVideoTrainingJobOperator, | ||
| DeleteAutoMLTrainingJobOperator, | ||
| ) | ||
| from airflow.providers.google.cloud.operators.vertex_ai.dataset import ( | ||
| CreateDatasetOperator, | ||
| DeleteDatasetOperator, | ||
| ImportDataOperator, | ||
| ) | ||
| from airflow.utils.trigger_rule import TriggerRule | ||
|
|
||
| ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default") | ||
| PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default") | ||
| DAG_ID = "example_automl_video_clss" | ||
| REGION = "us-central1" | ||
| VIDEO_DISPLAY_NAME = f"auto-ml-video-clss-{ENV_ID}" | ||
| MODEL_DISPLAY_NAME = f"auto-ml-video-clss-model-{ENV_ID}" | ||
|
|
||
| RESOURCE_DATA_BUCKET = "airflow-system-tests-resources" | ||
| VIDEO_GCS_BUCKET_NAME = f"bucket_video_clss_{ENV_ID}".replace("_", "-") | ||
|
|
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| VIDEO_DATASET = { | ||
| "display_name": f"video-dataset-{ENV_ID}", | ||
| "metadata_schema_uri": schema.dataset.metadata.video, | ||
| "metadata": Value(string_value="video-dataset"), | ||
| } | ||
| VIDEO_DATA_CONFIG = [ | ||
| { | ||
| "import_schema_uri": schema.dataset.ioformat.video.classification, | ||
| "gcs_source": {"uris": [f"gs://{VIDEO_GCS_BUCKET_NAME}/automl/classification.csv"]}, | ||
| }, | ||
| ] | ||
|
|
||
|
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| # Example DAG for AutoML Video Intelligence Classification | ||
| with DAG( | ||
| DAG_ID, | ||
| schedule="@once", | ||
| start_date=datetime(2021, 1, 1), | ||
| catchup=False, | ||
| tags=["example", "automl", "video", "classification"], | ||
| ) as dag: | ||
| create_bucket = GCSCreateBucketOperator( | ||
| task_id="create_bucket", | ||
| bucket_name=VIDEO_GCS_BUCKET_NAME, | ||
| storage_class="REGIONAL", | ||
| location=REGION, | ||
| ) | ||
|
|
||
| move_dataset_file = GCSSynchronizeBucketsOperator( | ||
| task_id="move_dataset_to_bucket", | ||
| source_bucket=RESOURCE_DATA_BUCKET, | ||
| source_object="automl/datasets/video", | ||
| destination_bucket=VIDEO_GCS_BUCKET_NAME, | ||
| destination_object="automl", | ||
| recursive=True, | ||
| ) | ||
|
|
||
| create_video_dataset = CreateDatasetOperator( | ||
| task_id="video_dataset", | ||
| dataset=VIDEO_DATASET, | ||
| region=REGION, | ||
| project_id=PROJECT_ID, | ||
| ) | ||
| video_dataset_id = create_video_dataset.output["dataset_id"] | ||
|
|
||
| import_video_dataset = ImportDataOperator( | ||
| task_id="import_video_data", | ||
| dataset_id=video_dataset_id, | ||
| region=REGION, | ||
| project_id=PROJECT_ID, | ||
| import_configs=VIDEO_DATA_CONFIG, | ||
| ) | ||
|
|
||
| # [START howto_cloud_create_video_classification_training_job_operator] | ||
| create_auto_ml_video_training_job = CreateAutoMLVideoTrainingJobOperator( | ||
| task_id="auto_ml_video_task", | ||
| display_name=VIDEO_DISPLAY_NAME, | ||
| prediction_type="classification", | ||
| model_type="CLOUD", | ||
| dataset_id=video_dataset_id, | ||
| model_display_name=MODEL_DISPLAY_NAME, | ||
| region=REGION, | ||
| project_id=PROJECT_ID, | ||
| ) | ||
| # [END howto_cloud_create_video_classification_training_job_operator] | ||
|
|
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| delete_auto_ml_video_training_job = DeleteAutoMLTrainingJobOperator( | ||
| task_id="delete_auto_ml_video_training_job", | ||
| training_pipeline_id="{{ task_instance.xcom_pull(task_ids='auto_ml_video_task', " | ||
| "key='training_id') }}", | ||
| region=REGION, | ||
| project_id=PROJECT_ID, | ||
| trigger_rule=TriggerRule.ALL_DONE, | ||
| ) | ||
|
|
||
| delete_video_dataset = DeleteDatasetOperator( | ||
| task_id="delete_video_dataset", | ||
| dataset_id=video_dataset_id, | ||
| region=REGION, | ||
| project_id=PROJECT_ID, | ||
| trigger_rule=TriggerRule.ALL_DONE, | ||
| ) | ||
|
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| delete_bucket = GCSDeleteBucketOperator( | ||
| task_id="delete_bucket", | ||
| bucket_name=VIDEO_GCS_BUCKET_NAME, | ||
| trigger_rule=TriggerRule.ALL_DONE, | ||
| ) | ||
|
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||
| ( | ||
| # TEST SETUP | ||
| [ | ||
| create_bucket >> move_dataset_file, | ||
| create_video_dataset, | ||
| ] | ||
| >> import_video_dataset | ||
| # TEST BODY | ||
| >> create_auto_ml_video_training_job | ||
| # TEST TEARDOWN | ||
| >> delete_auto_ml_video_training_job | ||
| >> delete_video_dataset | ||
| >> delete_bucket | ||
| ) | ||
|
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| from tests.system.utils.watcher import watcher | ||
|
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| # This test needs watcher in order to properly mark success/failure | ||
| # when "tearDown" task with trigger rule is part of the DAG | ||
| list(dag.tasks) >> watcher() | ||
|
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| from tests.system.utils import get_test_run # noqa: E402 | ||
|
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| # Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest) | ||
| test_run = get_test_run(dag) |
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