airflow example_automl_video_intelligence_tracking 源码

airflow example_automl_video_intelligence_tracking 代码

文件路径：/airflow/providers/google/cloud/example_dags/example_automl_video_intelligence_tracking.py

#
# 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 typing import cast

from airflow import models
from airflow.models.xcom_arg import XComArg
from airflow.providers.google.cloud.hooks.automl import CloudAutoMLHook
from airflow.providers.google.cloud.operators.automl import (
    AutoMLCreateDatasetOperator,
    AutoMLDeleteDatasetOperator,
    AutoMLDeleteModelOperator,
    AutoMLImportDataOperator,
    AutoMLTrainModelOperator,
)

GCP_PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "your-project-id")
GCP_AUTOML_LOCATION = os.environ.get("GCP_AUTOML_LOCATION", "us-central1")
GCP_AUTOML_TRACKING_BUCKET = os.environ.get(
    "GCP_AUTOML_TRACKING_BUCKET",
    "gs://INVALID BUCKET NAME/youtube_8m_videos_animal_tiny.csv",
)

# Example values
DATASET_ID = "VOT123456789"

# Example model
MODEL = {
    "display_name": "auto_model_1",
    "dataset_id": DATASET_ID,
    "video_object_tracking_model_metadata": {},
}

# Example dataset
DATASET = {
    "display_name": "test_video_tracking_dataset",
    "video_object_tracking_dataset_metadata": {},
}

IMPORT_INPUT_CONFIG = {"gcs_source": {"input_uris": [GCP_AUTOML_TRACKING_BUCKET]}}

extract_object_id = CloudAutoMLHook.extract_object_id


# Example DAG for AutoML Video Intelligence Object Tracking
with models.DAG(
    "example_automl_video_tracking",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    user_defined_macros={"extract_object_id": extract_object_id},
    tags=['example'],
) as example_dag:
    create_dataset_task = AutoMLCreateDatasetOperator(
        task_id="create_dataset_task", dataset=DATASET, location=GCP_AUTOML_LOCATION
    )

    dataset_id = cast(str, XComArg(create_dataset_task, key="dataset_id"))

    import_dataset_task = AutoMLImportDataOperator(
        task_id="import_dataset_task",
        dataset_id=dataset_id,
        location=GCP_AUTOML_LOCATION,
        input_config=IMPORT_INPUT_CONFIG,
    )

    MODEL["dataset_id"] = dataset_id

    create_model = AutoMLTrainModelOperator(task_id="create_model", model=MODEL, location=GCP_AUTOML_LOCATION)

    model_id = cast(str, XComArg(create_model, key="model_id"))

    delete_model_task = AutoMLDeleteModelOperator(
        task_id="delete_model_task",
        model_id=model_id,
        location=GCP_AUTOML_LOCATION,
        project_id=GCP_PROJECT_ID,
    )

    delete_datasets_task = AutoMLDeleteDatasetOperator(
        task_id="delete_datasets_task",
        dataset_id=dataset_id,
        location=GCP_AUTOML_LOCATION,
        project_id=GCP_PROJECT_ID,
    )

    import_dataset_task >> create_model
    delete_model_task >> delete_datasets_task

    # Task dependencies created via `XComArgs`:
    #   create_dataset_task >> import_dataset_task
    #   create_dataset_task >> create_model
    #   create_model >> delete_model_task
    #   create_dataset_task >> delete_datasets_task

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