Create CustomJob and Datasets operators for Vertex AI service (#21253)

airflow/providers/google/cloud/example_dags/example_vertex_ai.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 demonstrates operators for the Google Vertex AI service in the Google
+Cloud Platform.
+
+This DAG relies on the following OS environment variables:
+
+* GCP_VERTEX_AI_BUCKET - Google Cloud Storage bucket where the model will be saved
+after training process was finished.
+* CUSTOM_CONTAINER_URI - path to container with model.
+* PYTHON_PACKAGE_GSC_URI - path to test model in archive.
+* LOCAL_TRAINING_SCRIPT_PATH - path to local training script.
+* DATASET_ID - ID of dataset which will be used in training process.
+"""
+import os
+from datetime import datetime
+from uuid import uuid4
+
+from google.protobuf.struct_pb2 import Value
+
+from airflow import models
+from airflow.providers.google.cloud.operators.vertex_ai.custom_job import (
+    CreateCustomContainerTrainingJobOperator,
+    CreateCustomPythonPackageTrainingJobOperator,
+    CreateCustomTrainingJobOperator,
+    DeleteCustomTrainingJobOperator,
+    ListCustomTrainingJobOperator,
+)
+from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
+    CreateDatasetOperator,
+    DeleteDatasetOperator,
+    ExportDataOperator,
+    GetDatasetOperator,
+    ImportDataOperator,
+    ListDatasetsOperator,
+    UpdateDatasetOperator,
+)
+
+PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "an-id")
+REGION = os.environ.get("GCP_LOCATION", "us-central1")
+BUCKET = os.environ.get("GCP_VERTEX_AI_BUCKET", "vertex-ai-system-tests")
+
+STAGING_BUCKET = f"gs://{BUCKET}"
+DISPLAY_NAME = str(uuid4())  # Create random display name
+CONTAINER_URI = "gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest"
+CUSTOM_CONTAINER_URI = os.environ.get("CUSTOM_CONTAINER_URI", "path_to_container_with_model")
+MODEL_SERVING_CONTAINER_URI = "gcr.io/cloud-aiplatform/prediction/tf2-cpu.2-2:latest"
+REPLICA_COUNT = 1
+MACHINE_TYPE = "n1-standard-4"
+ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
+ACCELERATOR_COUNT = 0
+TRAINING_FRACTION_SPLIT = 0.7
+TEST_FRACTION_SPLIT = 0.15
+VALIDATION_FRACTION_SPLIT = 0.15
+
+PYTHON_PACKAGE_GCS_URI = os.environ.get("PYTHON_PACKAGE_GSC_URI", "path_to_test_model_in_arch")
+PYTHON_MODULE_NAME = "aiplatform_custom_trainer_script.task"
+
+LOCAL_TRAINING_SCRIPT_PATH = os.environ.get("LOCAL_TRAINING_SCRIPT_PATH", "path_to_training_script")
+
+TRAINING_PIPELINE_ID = "test-training-pipeline-id"
+CUSTOM_JOB_ID = "test-custom-job-id"
+
+IMAGE_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml",
+    "metadata": Value(string_value="test-image-dataset"),
+}
+TABULAR_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/tabular_1.0.0.yaml",
+    "metadata": Value(string_value="test-tabular-dataset"),
+}
+TEXT_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/text_1.0.0.yaml",
+    "metadata": Value(string_value="test-text-dataset"),
+}
+VIDEO_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/video_1.0.0.yaml",
+    "metadata": Value(string_value="test-video-dataset"),
+}
+TIME_SERIES_DATASET = {
+    "display_name": str(uuid4()),
+    "metadata_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/metadata/time_series_1.0.0.yaml",
+    "metadata": Value(string_value="test-video-dataset"),
+}
+DATASET_ID = os.environ.get("DATASET_ID", "test-dataset-id")
+TEST_EXPORT_CONFIG = {"gcs_destination": {"output_uri_prefix": "gs://test-vertex-ai-bucket/exports"}}
+TEST_IMPORT_CONFIG = [
+    {
+        "data_item_labels": {
+            "test-labels-name": "test-labels-value",
+        },
+        "import_schema_uri": (
+            "gs://google-cloud-aiplatform/schema/dataset/ioformat/image_bounding_box_io_format_1.0.0.yaml"
+        ),
+        "gcs_source": {
+            "uris": ["gs://ucaip-test-us-central1/dataset/salads_oid_ml_use_public_unassigned.jsonl"]
+        },
+    },
+]
+DATASET_TO_UPDATE = {"display_name": "test-name"}
+TEST_UPDATE_MASK = {"paths": ["displayName"]}
+
+with models.DAG(
+    "example_gcp_vertex_ai_custom_jobs",
+    schedule_interval="@once",
+    start_date=datetime(2021, 1, 1),
+    catchup=False,
+) as custom_jobs_dag:
+    # [START how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
+    create_custom_container_training_job = CreateCustomContainerTrainingJobOperator(
+        task_id="custom_container_task",
+        staging_bucket=STAGING_BUCKET,
+        display_name=f"train-housing-container-{DISPLAY_NAME}",
+        container_uri=CUSTOM_CONTAINER_URI,
+        model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
+        # run params
+        dataset_id=DATASET_ID,
+        command=["python3", "task.py"],
+        model_display_name=f"container-housing-model-{DISPLAY_NAME}",
+        replica_count=REPLICA_COUNT,
+        machine_type=MACHINE_TYPE,
+        accelerator_type=ACCELERATOR_TYPE,
+        accelerator_count=ACCELERATOR_COUNT,
+        training_fraction_split=TRAINING_FRACTION_SPLIT,
+        validation_fraction_split=VALIDATION_FRACTION_SPLIT,
+        test_fraction_split=TEST_FRACTION_SPLIT,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    # [END how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
+
+    # [START how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator]
+    create_custom_python_package_training_job = CreateCustomPythonPackageTrainingJobOperator(
+        task_id="python_package_task",
+        staging_bucket=STAGING_BUCKET,
+        display_name=f"train-housing-py-package-{DISPLAY_NAME}",
+        python_package_gcs_uri=PYTHON_PACKAGE_GCS_URI,
+        python_module_name=PYTHON_MODULE_NAME,
+        container_uri=CONTAINER_URI,
+        model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
+        # run params
+        dataset_id=DATASET_ID,
+        model_display_name=f"py-package-housing-model-{DISPLAY_NAME}",
+        replica_count=REPLICA_COUNT,
+        machine_type=MACHINE_TYPE,
+        accelerator_type=ACCELERATOR_TYPE,
+        accelerator_count=ACCELERATOR_COUNT,
+        training_fraction_split=TRAINING_FRACTION_SPLIT,
+        validation_fraction_split=VALIDATION_FRACTION_SPLIT,
+        test_fraction_split=TEST_FRACTION_SPLIT,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    # [END how_to_cloud_vertex_ai_create_custom_python_package_training_job_operator]
+
+    # [START how_to_cloud_vertex_ai_create_custom_training_job_operator]
+    create_custom_training_job = CreateCustomTrainingJobOperator(
+        task_id="custom_task",
+        staging_bucket=STAGING_BUCKET,
+        display_name=f"train-housing-custom-{DISPLAY_NAME}",
+        script_path=LOCAL_TRAINING_SCRIPT_PATH,
+        container_uri=CONTAINER_URI,
+        requirements=["gcsfs==0.7.1"],
+        model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
+        # run params
+        dataset_id=DATASET_ID,
+        replica_count=1,
+        model_display_name=f"custom-housing-model-{DISPLAY_NAME}",
+        sync=False,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    # [END how_to_cloud_vertex_ai_create_custom_training_job_operator]
+
+    # [START how_to_cloud_vertex_ai_delete_custom_training_job_operator]
+    delete_custom_training_job = DeleteCustomTrainingJobOperator(
+        task_id="delete_custom_training_job",
+        training_pipeline_id=TRAINING_PIPELINE_ID,
+        custom_job_id=CUSTOM_JOB_ID,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    # [END how_to_cloud_vertex_ai_delete_custom_training_job_operator]
+
+    # [START how_to_cloud_vertex_ai_list_custom_training_job_operator]
+    list_custom_training_job = ListCustomTrainingJobOperator(
+        task_id="list_custom_training_job",
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    # [END how_to_cloud_vertex_ai_list_custom_training_job_operator]
+
+with models.DAG(
+    "example_gcp_vertex_ai_dataset",
+    schedule_interval="@once",
+    start_date=datetime(2021, 1, 1),
+    catchup=False,
+) as dataset_dag:
+    # [START how_to_cloud_vertex_ai_create_dataset_operator]
+    create_image_dataset_job = CreateDatasetOperator(
+        task_id="image_dataset",
+        dataset=IMAGE_DATASET,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    create_tabular_dataset_job = CreateDatasetOperator(
+        task_id="tabular_dataset",
+        dataset=TABULAR_DATASET,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    create_text_dataset_job = CreateDatasetOperator(
+        task_id="text_dataset",
+        dataset=TEXT_DATASET,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    create_video_dataset_job = CreateDatasetOperator(
+        task_id="video_dataset",
+        dataset=VIDEO_DATASET,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    create_time_series_dataset_job = CreateDatasetOperator(
+        task_id="time_series_dataset",
+        dataset=TIME_SERIES_DATASET,
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    # [END how_to_cloud_vertex_ai_create_dataset_operator]
+
+    # [START how_to_cloud_vertex_ai_delete_dataset_operator]
+    delete_dataset_job = DeleteDatasetOperator(
+        task_id="delete_dataset",
+        dataset_id=create_text_dataset_job.output['dataset_id'],
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    # [END how_to_cloud_vertex_ai_delete_dataset_operator]
+
+    # [START how_to_cloud_vertex_ai_get_dataset_operator]
+    get_dataset = GetDatasetOperator(
+        task_id="get_dataset",
+        project_id=PROJECT_ID,
+        region=REGION,
+        dataset_id=create_tabular_dataset_job.output['dataset_id'],
+    )
+    # [END how_to_cloud_vertex_ai_get_dataset_operator]
+
+    # [START how_to_cloud_vertex_ai_export_data_operator]
+    export_data_job = ExportDataOperator(
+        task_id="export_data",
+        dataset_id=create_image_dataset_job.output['dataset_id'],
+        region=REGION,
+        project_id=PROJECT_ID,
+        export_config=TEST_EXPORT_CONFIG,
+    )
+    # [END how_to_cloud_vertex_ai_export_data_operator]
+
+    # [START how_to_cloud_vertex_ai_import_data_operator]
+    import_data_job = ImportDataOperator(
+        task_id="import_data",
+        dataset_id=create_image_dataset_job.output['dataset_id'],
+        region=REGION,
+        project_id=PROJECT_ID,
+        import_configs=TEST_IMPORT_CONFIG,
+    )
+    # [END how_to_cloud_vertex_ai_import_data_operator]
+
+    # [START how_to_cloud_vertex_ai_list_dataset_operator]
+    list_dataset_job = ListDatasetsOperator(
+        task_id="list_dataset",
+        region=REGION,
+        project_id=PROJECT_ID,
+    )
+    # [END how_to_cloud_vertex_ai_list_dataset_operator]
+
+    # [START how_to_cloud_vertex_ai_update_dataset_operator]
+    update_dataset_job = UpdateDatasetOperator(
+        task_id="update_dataset",
+        project_id=PROJECT_ID,
+        region=REGION,
+        dataset_id=create_video_dataset_job.output['dataset_id'],
+        dataset=DATASET_TO_UPDATE,
+        update_mask=TEST_UPDATE_MASK,
+    )
+    # [END how_to_cloud_vertex_ai_update_dataset_operator]
+
+    create_time_series_dataset_job
+    create_text_dataset_job >> delete_dataset_job
+    create_tabular_dataset_job >> get_dataset
+    create_image_dataset_job >> import_data_job >> export_data_job
+    create_video_dataset_job >> update_dataset_job
+    list_dataset_job

airflow/providers/google/cloud/hooks/vertex_ai/__init__.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.