Source code for tests.system.providers.google.cloud.ml_engine.example_mlengine

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"""
Example Airflow DAG for Google ML Engine service.
"""
from __future__ import annotations

import os
from datetime import datetime

from google.cloud.aiplatform import schema
from google.protobuf.json_format import ParseDict
from google.protobuf.struct_pb2 import Value

from airflow import models
from airflow.operators.bash import BashOperator
from airflow.providers.google.cloud.operators.gcs import (
    GCSCreateBucketOperator,
    GCSDeleteBucketOperator,
    GCSSynchronizeBucketsOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.batch_prediction_job import (
    CreateBatchPredictionJobOperator,
    DeleteBatchPredictionJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.custom_job import (
    CreateCustomPythonPackageTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
    CreateDatasetOperator,
    DeleteDatasetOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.model_service import (
    DeleteModelOperator,
    DeleteModelVersionOperator,
    GetModelOperator,
    ListModelVersionsOperator,
    SetDefaultVersionOnModelOperator,
)
from airflow.utils.trigger_rule import TriggerRule

[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
[docs]DAG_ID = "example_gcp_mlengine"
[docs]REGION = "us-central1"
[docs]PACKAGE_DISPLAY_NAME = f"package-{DAG_ID}-{ENV_ID}".replace("_", "-")
[docs]MODEL_DISPLAY_NAME = f"model-{DAG_ID}-{ENV_ID}".replace("_", "-")
[docs]JOB_DISPLAY_NAME = f"batch_job_{DAG_ID}_{ENV_ID}".replace("-", "_")
[docs]RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
[docs]CUSTOM_PYTHON_GCS_BUCKET_NAME = f"bucket_python_{DAG_ID}_{ENV_ID}".replace("_", "-")
[docs]BQ_SOURCE = "bq://bigquery-public-data.ml_datasets.penguins"
[docs]TABULAR_DATASET = { "display_name": f"tabular-dataset-{ENV_ID}", "metadata_schema_uri": schema.dataset.metadata.tabular, "metadata": ParseDict( {"input_config": {"bigquery_source": {"uri": BQ_SOURCE}}}, Value(), ), }
[docs]REPLICA_COUNT = 1
[docs]MACHINE_TYPE = "n1-standard-4"
[docs]ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
[docs]ACCELERATOR_COUNT = 0
[docs]TRAINING_FRACTION_SPLIT = 0.7
[docs]TEST_FRACTION_SPLIT = 0.15
[docs]VALIDATION_FRACTION_SPLIT = 0.15
[docs]PYTHON_PACKAGE_GCS_URI = f"gs://{CUSTOM_PYTHON_GCS_BUCKET_NAME}/vertex-ai/penguins_trainer_script-0.1.zip"
[docs]PYTHON_MODULE_NAME = "penguins_trainer_script.task"
[docs]TRAIN_IMAGE = "us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-8:latest"
[docs]DEPLOY_IMAGE = "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest"
with models.DAG( dag_id=DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "ml_engine"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator( task_id="create_bucket", bucket_name=CUSTOM_PYTHON_GCS_BUCKET_NAME, storage_class="REGIONAL", location=REGION, )
move_data_files = GCSSynchronizeBucketsOperator( task_id="move_files_to_bucket", source_bucket=RESOURCE_DATA_BUCKET, source_object="vertex-ai/penguins-data", destination_bucket=CUSTOM_PYTHON_GCS_BUCKET_NAME, destination_object="vertex-ai", recursive=True, ) create_tabular_dataset = CreateDatasetOperator( task_id="tabular_dataset", dataset=TABULAR_DATASET, region=REGION, project_id=PROJECT_ID, ) tabular_dataset_id = create_tabular_dataset.output["dataset_id"] # [START howto_operator_create_custom_python_training_job_v1] create_custom_python_package_training_job = CreateCustomPythonPackageTrainingJobOperator( task_id="create_custom_python_package_training_job", staging_bucket=f"gs://{CUSTOM_PYTHON_GCS_BUCKET_NAME}", display_name=PACKAGE_DISPLAY_NAME, python_package_gcs_uri=PYTHON_PACKAGE_GCS_URI, python_module_name=PYTHON_MODULE_NAME, container_uri=TRAIN_IMAGE, model_serving_container_image_uri=DEPLOY_IMAGE, bigquery_destination=f"bq://{PROJECT_ID}", # run params dataset_id=tabular_dataset_id, model_display_name=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 howto_operator_create_custom_python_training_job_v1] model_id_v1 = create_custom_python_package_training_job.output["model_id"] # [START howto_operator_gcp_mlengine_get_model] get_model = GetModelOperator( task_id="get_model", region=REGION, project_id=PROJECT_ID, model_id=model_id_v1 ) # [END howto_operator_gcp_mlengine_get_model] # [START howto_operator_gcp_mlengine_print_model] get_model_result = BashOperator( bash_command=f"echo {get_model.output}", task_id="get_model_result", ) # [END howto_operator_gcp_mlengine_print_model] # [START howto_operator_create_custom_python_training_job_v2] create_custom_python_package_training_job_v2 = CreateCustomPythonPackageTrainingJobOperator( task_id="create_custom_python_package_training_job_v2", staging_bucket=f"gs://{CUSTOM_PYTHON_GCS_BUCKET_NAME}", display_name=PACKAGE_DISPLAY_NAME, python_package_gcs_uri=PYTHON_PACKAGE_GCS_URI, python_module_name=PYTHON_MODULE_NAME, container_uri=TRAIN_IMAGE, model_serving_container_image_uri=DEPLOY_IMAGE, bigquery_destination=f"bq://{PROJECT_ID}", parent_model=model_id_v1, # run params dataset_id=tabular_dataset_id, model_display_name=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 howto_operator_create_custom_python_training_job_v2] model_id_v2 = create_custom_python_package_training_job_v2.output["model_id"] # [START howto_operator_gcp_mlengine_default_version] set_default_version = SetDefaultVersionOnModelOperator( task_id="set_default_version", project_id=PROJECT_ID, region=REGION, model_id=model_id_v2, ) # [END howto_operator_gcp_mlengine_default_version] # [START howto_operator_gcp_mlengine_list_versions] list_model_versions = ListModelVersionsOperator( task_id="list_model_versions", region=REGION, project_id=PROJECT_ID, model_id=model_id_v2 ) # [END howto_operator_gcp_mlengine_list_versions] # [START howto_operator_start_batch_prediction] create_batch_prediction_job = CreateBatchPredictionJobOperator( task_id="create_batch_prediction_job", job_display_name=JOB_DISPLAY_NAME, model_name=model_id_v2, predictions_format="bigquery", bigquery_source=BQ_SOURCE, bigquery_destination_prefix=f"bq://{PROJECT_ID}", region=REGION, project_id=PROJECT_ID, machine_type=MACHINE_TYPE, ) # [END howto_operator_start_batch_prediction] # [START howto_operator_gcp_mlengine_delete_version] delete_model_version_1 = DeleteModelVersionOperator( task_id="delete_model_version_1", project_id=PROJECT_ID, region=REGION, model_id=model_id_v2, trigger_rule=TriggerRule.ALL_DONE, ) # [END howto_operator_gcp_mlengine_delete_version] # [START howto_operator_gcp_mlengine_delete_model] delete_model = DeleteModelOperator( task_id="delete_model", project_id=PROJECT_ID, region=REGION, model_id=model_id_v1, trigger_rule=TriggerRule.ALL_DONE, ) # [END howto_operator_gcp_mlengine_delete_model] delete_batch_prediction_job = DeleteBatchPredictionJobOperator( task_id="delete_batch_prediction_job", batch_prediction_job_id=create_batch_prediction_job.output["batch_prediction_job_id"], region=REGION, project_id=PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE, ) delete_tabular_dataset = DeleteDatasetOperator( task_id="delete_tabular_dataset", dataset_id=tabular_dataset_id, region=REGION, project_id=PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE, ) delete_bucket = GCSDeleteBucketOperator( task_id="delete_bucket", bucket_name=CUSTOM_PYTHON_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE, ) ( # TEST SETUP create_bucket >> move_data_files >> create_tabular_dataset # TEST BODY >> create_custom_python_package_training_job >> create_custom_python_package_training_job_v2 >> create_batch_prediction_job >> get_model >> get_model_result >> list_model_versions >> set_default_version # TEST TEARDOWN >> delete_model_version_1 >> delete_model >> delete_batch_prediction_job >> delete_tabular_dataset >> delete_bucket ) from tests.system.utils.watcher import watcher # 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() from tests.system.utils import get_test_run # noqa: E402 # Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
[docs]test_run = get_test_run(dag)

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