Source code for tests.system.providers.google.cloud.vertex_ai.example_vertex_ai_endpoint

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# mypy ignore arg types (for templated fields)
# type: ignore[arg-type]

"""
Example Airflow DAG for Google Vertex AI service testing Endpoint Service operations.
"""
from __future__ import annotations

import os
from datetime import datetime
from pathlib import Path

from google.cloud import aiplatform
from google.cloud.aiplatform import schema
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
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
    CreateAutoMLImageTrainingJobOperator,
    DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
    CreateDatasetOperator,
    DeleteDatasetOperator,
    ImportDataOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.endpoint_service import (
    CreateEndpointOperator,
    DeleteEndpointOperator,
    DeployModelOperator,
    ListEndpointsOperator,
    UndeployModelOperator,
)
from airflow.providers.google.cloud.transfers.local_to_gcs import LocalFilesystemToGCSOperator
from airflow.utils.trigger_rule import TriggerRule

[docs]ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID")
[docs]PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
[docs]DAG_ID = "vertex_ai_endpoint_service_operations"
[docs]REGION = "us-central1"
[docs]IMAGE_DISPLAY_NAME = f"auto-ml-image-{ENV_ID}"
[docs]MODEL_DISPLAY_NAME = f"auto-ml-image-model-{ENV_ID}"
[docs]DATA_SAMPLE_GCS_BUCKET_NAME = f"bucket_{DAG_ID}_{ENV_ID}"
[docs]DATA_SAMPLE_GCS_OBJECT_NAME = "vertex-ai/image-dataset.csv"
[docs]IMAGE_ZIP_CSV_FILE_LOCAL_PATH = str(Path(__file__).parent / "resources" / "image-dataset.csv.zip")
[docs]IMAGE_CSV_FILE_LOCAL_PATH = "/endpoint/image-dataset.csv"
[docs]IMAGE_DATASET = { "display_name": f"image-dataset-{ENV_ID}", "metadata_schema_uri": schema.dataset.metadata.image, "metadata": Value(string_value="image-dataset"),
}
[docs]IMAGE_DATA_CONFIG = [ { "import_schema_uri": schema.dataset.ioformat.image.single_label_classification, "gcs_source": {"uris": [f"gs://{DATA_SAMPLE_GCS_BUCKET_NAME}/vertex-ai/image-dataset.csv"]},
}, ]
[docs]ENDPOINT_CONF = { "display_name": f"endpoint_test_{ENV_ID}",
} with models.DAG( DAG_ID, schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, render_template_as_native_obj=True, tags=["example", "vertex_ai", "endpoint_service"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator( task_id="create_bucket", bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME, storage_class="REGIONAL", location=REGION,
) unzip_file = BashOperator( task_id="unzip_csv_data_file", bash_command=f"unzip {IMAGE_ZIP_CSV_FILE_LOCAL_PATH} -d /endpoint/", ) upload_files = LocalFilesystemToGCSOperator( task_id="upload_file_to_bucket", src=IMAGE_CSV_FILE_LOCAL_PATH, dst=DATA_SAMPLE_GCS_OBJECT_NAME, bucket=DATA_SAMPLE_GCS_BUCKET_NAME, ) create_image_dataset = CreateDatasetOperator( task_id="image_dataset", dataset=IMAGE_DATASET, region=REGION, project_id=PROJECT_ID, ) image_dataset_id = create_image_dataset.output["dataset_id"] import_image_dataset = ImportDataOperator( task_id="import_image_data", dataset_id=image_dataset_id, region=REGION, project_id=PROJECT_ID, import_configs=IMAGE_DATA_CONFIG, ) create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator( task_id="auto_ml_image_task", display_name=IMAGE_DISPLAY_NAME, dataset_id=image_dataset_id, prediction_type="classification", multi_label=False, model_type="CLOUD", training_fraction_split=0.6, validation_fraction_split=0.2, test_fraction_split=0.2, budget_milli_node_hours=8000, model_display_name=MODEL_DISPLAY_NAME, disable_early_stopping=False, region=REGION, project_id=PROJECT_ID, ) DEPLOYED_MODEL = { # format: 'projects/{project}/locations/{location}/models/{model}' "model": "{{ti.xcom_pull('auto_ml_image_task')['name']}}", "display_name": f"temp_endpoint_test_{ENV_ID}", "dedicated_resources": { "machine_spec": { "machine_type": "n1-standard-2", "accelerator_type": aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, "accelerator_count": 1, }, "min_replica_count": 1, "max_replica_count": 1, }, } # [START how_to_cloud_vertex_ai_create_endpoint_operator] create_endpoint = CreateEndpointOperator( task_id="create_endpoint", endpoint=ENDPOINT_CONF, region=REGION, project_id=PROJECT_ID, ) # [END how_to_cloud_vertex_ai_create_endpoint_operator] # [START how_to_cloud_vertex_ai_delete_endpoint_operator] delete_endpoint = DeleteEndpointOperator( task_id="delete_endpoint", endpoint_id=create_endpoint.output["endpoint_id"], region=REGION, project_id=PROJECT_ID, ) # [END how_to_cloud_vertex_ai_delete_endpoint_operator] # [START how_to_cloud_vertex_ai_list_endpoints_operator] list_endpoints = ListEndpointsOperator( task_id="list_endpoints", region=REGION, project_id=PROJECT_ID, ) # [END how_to_cloud_vertex_ai_list_endpoints_operator] # [START how_to_cloud_vertex_ai_deploy_model_operator] deploy_model = DeployModelOperator( task_id="deploy_model", endpoint_id=create_endpoint.output["endpoint_id"], deployed_model=DEPLOYED_MODEL, traffic_split={"0": 100}, region=REGION, project_id=PROJECT_ID, ) # [END how_to_cloud_vertex_ai_deploy_model_operator] # [START how_to_cloud_vertex_ai_undeploy_model_operator] undeploy_model = UndeployModelOperator( task_id="undeploy_model", endpoint_id=create_endpoint.output["endpoint_id"], deployed_model_id=deploy_model.output["deployed_model_id"], region=REGION, project_id=PROJECT_ID, ) # [END how_to_cloud_vertex_ai_undeploy_model_operator] delete_auto_ml_image_training_job = DeleteAutoMLTrainingJobOperator( task_id="delete_auto_ml_training_job", training_pipeline_id=create_auto_ml_image_training_job.output["training_id"], region=REGION, project_id=PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE, ) delete_image_dataset = DeleteDatasetOperator( task_id="delete_image_dataset", dataset_id=image_dataset_id, region=REGION, project_id=PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE, ) delete_bucket = GCSDeleteBucketOperator( task_id="delete_bucket", bucket_name=DATA_SAMPLE_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE, ) clear_folder = BashOperator( task_id="clear_folder", bash_command="rm -r /endpoint/*", ) ( # TEST SETUP [ create_bucket, create_image_dataset, ] >> unzip_file >> upload_files >> import_image_dataset >> create_auto_ml_image_training_job # TEST BODY >> create_endpoint >> deploy_model >> undeploy_model >> delete_endpoint >> list_endpoints # TEST TEARDOWN >> delete_auto_ml_image_training_job >> delete_image_dataset >> delete_bucket >> clear_folder ) 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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