tests.system.google.cloud.ml_engine.example_mlengine¶

Google ML Engine 服务的 Airflow DAG 示例。

模块内容¶

tests.system.google.cloud.ml_engine.example_mlengine.DAG_ID = 'gcp_mlengine'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.REGION = 'us-central1'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.PACKAGE_DISPLAY_NAME = ''[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.MODEL_DISPLAY_NAME = ''[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.JOB_DISPLAY_NAME = ''[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.RESOURCE_DATA_BUCKET = 'airflow-system-tests-resources'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.CUSTOM_PYTHON_GCS_BUCKET_NAME = ''[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.BQ_SOURCE = 'bq://bigquery-public-data.ml_datasets.penguins'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.MACHINE_TYPE = 'n1-standard-4'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.ACCELERATOR_TYPE = 'ACCELERATOR_TYPE_UNSPECIFIED'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.ACCELERATOR_COUNT = 0[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.TRAINING_FRACTION_SPLIT = 0.7[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.TEST_FRACTION_SPLIT = 0.15[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.VALIDATION_FRACTION_SPLIT = 0.15[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.PYTHON_PACKAGE_GCS_URI = 'gs:///vertex-ai/penguins_trainer_script-0.1.zip'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.PYTHON_MODULE_NAME = 'penguins_trainer_script.task'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.TRAIN_IMAGE = 'us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-8:latest'[源]¶

tests.system.google.cloud.ml_engine.example_mlengine.DEPLOY_IMAGE = 'us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-8:latest'[源]¶

`PROJECT_ID`
`ENV_ID`
`DAG_ID`
`REGION`
`PACKAGE_DISPLAY_NAME`
`MODEL_DISPLAY_NAME`
`JOB_DISPLAY_NAME`
`RESOURCE_DATA_BUCKET`
`CUSTOM_PYTHON_GCS_BUCKET_NAME`
`BQ_SOURCE`
`TABULAR_DATASET`
`REPLICA_COUNT`
`MACHINE_TYPE`
`ACCELERATOR_TYPE`
`ACCELERATOR_COUNT`
`TRAINING_FRACTION_SPLIT`
`TEST_FRACTION_SPLIT`
`VALIDATION_FRACTION_SPLIT`
`PYTHON_PACKAGE_GCS_URI`
`PYTHON_MODULE_NAME`
`TRAIN_IMAGE`
`DEPLOY_IMAGE`
`create_bucket`
`test_run`