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

#
# 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.

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

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

import os
from datetime import datetime
from pathlib import Path

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.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_auto_ml_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]IMAGE_GCS_BUCKET_NAME = f"bucket_image_{DAG_ID}_{ENV_ID}"
[docs]RESOURCES_PATH = Path(__file__).parent / "resources"
[docs]IMAGE_ZIP_CSV_FILE_LOCAL_PATH = str(RESOURCES_PATH / "image-dataset.csv.zip")
[docs]IMAGE_GCS_OBJECT_NAME = "vertex-ai/image-dataset.csv"
[docs]IMAGE_CSV_FILE_LOCAL_PATH = "/image/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://{IMAGE_GCS_BUCKET_NAME}/vertex-ai/image-dataset.csv"]},
}, ] with models.DAG( f"{DAG_ID}_image_training_job", schedule="@once", start_date=datetime(2021, 1, 1), catchup=False, tags=["example", "vertex_ai", "auto_ml"], ) as dag:
[docs] create_bucket = GCSCreateBucketOperator( task_id="create_bucket", bucket_name=IMAGE_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 /image/", ) upload_files = LocalFilesystemToGCSOperator( task_id="upload_file_to_bucket", src=IMAGE_CSV_FILE_LOCAL_PATH, dst=IMAGE_GCS_OBJECT_NAME, bucket=IMAGE_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, ) # [START how_to_cloud_vertex_ai_create_auto_ml_image_training_job_operator] 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, ) # [END how_to_cloud_vertex_ai_create_auto_ml_image_training_job_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=IMAGE_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE, ) clear_folder = BashOperator( task_id="clear_folder", bash_command="rm -r /image/*", ) ( # TEST SETUP [ create_bucket, create_image_dataset, ] >> unzip_file >> upload_files >> import_image_dataset # TEST BODY >> create_auto_ml_image_training_job # 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)

Was this entry helpful?