Add guide for AI Platform (previously Machine Learning Engine) Operators (#9798)
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Vismita Uppalli
GitHub
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Коммит
6d65c15d15
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@ -62,6 +62,7 @@ with models.DAG(
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schedule_interval=None, # Override to match your needs
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tags=['example'],
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) as dag:
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# [START howto_operator_gcp_mlengine_training]
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training = MLEngineStartTrainingJobOperator(
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task_id="training",
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project_id=PROJECT_ID,
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@ -74,7 +75,9 @@ with models.DAG(
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training_python_module=TRAINER_PY_MODULE,
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training_args=[],
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)
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# [END howto_operator_gcp_mlengine_training]
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# [START howto_operator_gcp_mlengine_create_model]
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create_model = MLEngineCreateModelOperator(
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task_id="create-model",
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project_id=PROJECT_ID,
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@ -82,18 +85,24 @@ with models.DAG(
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"name": MODEL_NAME,
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},
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)
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# [END howto_operator_gcp_mlengine_create_model]
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# [START howto_operator_gcp_mlengine_get_model]
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get_model = MLEngineGetModelOperator(
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task_id="get-model",
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project_id=PROJECT_ID,
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model_name=MODEL_NAME,
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)
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# [END howto_operator_gcp_mlengine_get_model]
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# [START howto_operator_gcp_mlengine_print_model]
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get_model_result = BashOperator(
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bash_command="echo \"{{ task_instance.xcom_pull('get-model') }}\"",
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task_id="get-model-result",
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)
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# [END howto_operator_gcp_mlengine_print_model]
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# [START howto_operator_gcp_mlengine_create_version1]
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create_version = MLEngineCreateVersionOperator(
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task_id="create-version",
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project_id=PROJECT_ID,
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@ -108,7 +117,9 @@ with models.DAG(
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"pythonVersion": "3.7"
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}
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)
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# [END howto_operator_gcp_mlengine_create_version1]
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# [START howto_operator_gcp_mlengine_create_version2]
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create_version_2 = MLEngineCreateVersionOperator(
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task_id="create-version-2",
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project_id=PROJECT_ID,
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@ -123,25 +134,33 @@ with models.DAG(
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"pythonVersion": "3.7"
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}
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)
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# [END howto_operator_gcp_mlengine_create_version2]
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# [START howto_operator_gcp_mlengine_default_version]
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set_defaults_version = MLEngineSetDefaultVersionOperator(
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task_id="set-default-version",
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project_id=PROJECT_ID,
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model_name=MODEL_NAME,
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version_name="v2",
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)
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# [END howto_operator_gcp_mlengine_default_version]
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# [START howto_operator_gcp_mlengine_list_versions]
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list_version = MLEngineListVersionsOperator(
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task_id="list-version",
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project_id=PROJECT_ID,
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model_name=MODEL_NAME,
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)
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# [END howto_operator_gcp_mlengine_list_versions]
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# [START howto_operator_gcp_mlengine_print_versions]
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list_version_result = BashOperator(
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bash_command="echo \"{{ task_instance.xcom_pull('list-version') }}\"",
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task_id="list-version-result",
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)
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# [END howto_operator_gcp_mlengine_print_versions]
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# [START howto_operator_gcp_mlengine_get_prediction]
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prediction = MLEngineStartBatchPredictionJobOperator(
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task_id="prediction",
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project_id=PROJECT_ID,
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@ -152,20 +171,25 @@ with models.DAG(
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input_paths=[PREDICTION_INPUT],
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output_path=PREDICTION_OUTPUT,
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)
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# [END howto_operator_gcp_mlengine_get_prediction]
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# [START howto_operator_gcp_mlengine_delete_version]
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delete_version = MLEngineDeleteVersionOperator(
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task_id="delete-version",
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project_id=PROJECT_ID,
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model_name=MODEL_NAME,
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version_name="v1"
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)
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# [END howto_operator_gcp_mlengine_delete_version]
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# [START howto_operator_gcp_mlengine_delete_model]
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delete_model = MLEngineDeleteModelOperator(
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task_id="delete-model",
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project_id=PROJECT_ID,
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model_name=MODEL_NAME,
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delete_contents=True
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)
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# [END howto_operator_gcp_mlengine_delete_model]
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training >> create_version
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training >> create_version_2
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@ -178,6 +202,7 @@ with models.DAG(
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list_version >> delete_version
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delete_version >> delete_model
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# [START howto_operator_gcp_mlengine_get_metric]
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def get_metric_fn_and_keys():
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"""
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Gets metric function and keys used to generate summary
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@ -186,7 +211,9 @@ with models.DAG(
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val = float(inst['dense_4'][0])
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return tuple([val]) # returns a tuple.
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return normalize_value, ['val'] # key order must match.
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# [END howto_operator_gcp_mlengine_get_metric]
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# [START howto_operator_gcp_mlengine_validate_error]
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def validate_err_and_count(summary: Dict) -> Dict:
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"""
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Validate summary result
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@ -198,7 +225,9 @@ with models.DAG(
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if summary['count'] != 20:
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raise ValueError('Invalid value val != 20; summary={}'.format(summary))
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return summary
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# [END howto_operator_gcp_mlengine_validate_error]
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# [START howto_operator_gcp_mlengine_evaluate]
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evaluate_prediction, evaluate_summary, evaluate_validation = mlengine_operator_utils.create_evaluate_ops(
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task_prefix="evaluate-ops",
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data_format="TEXT",
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@ -218,6 +247,7 @@ with models.DAG(
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version_name="v1",
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py_interpreter="python3",
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)
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# [END howto_operator_gcp_mlengine_evaluate]
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create_model >> create_version >> evaluate_prediction
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evaluate_validation >> delete_version
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@ -71,6 +71,10 @@ class MLEngineStartBatchPredictionJobOperator(BaseOperator):
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"""
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Start a Google Cloud ML Engine prediction job.
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineStartBatchPredictionJobOperator`
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NOTE: For model origin, users should consider exactly one from the
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three options below:
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@ -351,6 +355,10 @@ class MLEngineCreateModelOperator(BaseOperator):
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"""
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Creates a new model.
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineCreateModelOperator`
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The model should be provided by the `model` parameter.
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:param model: A dictionary containing the information about the model.
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@ -395,6 +403,10 @@ class MLEngineGetModelOperator(BaseOperator):
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"""
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Gets a particular model
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineGetModelOperator`
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The name of model shold be specified in `model_name`.
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:param model_name: The name of the model.
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@ -438,6 +450,10 @@ class MLEngineDeleteModelOperator(BaseOperator):
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"""
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Deletes a model.
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineDeleteModelOperator`
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The model should be provided by the `model_name` parameter.
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:param model_name: The name of the model.
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@ -615,6 +631,10 @@ class MLEngineCreateVersionOperator(BaseOperator):
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"""
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Creates a new version in the model
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineCreateVersionOperator`
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Model should be specified by `model_name`, in which case the `version` parameter should contain all the
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information to create that version
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@ -678,6 +698,10 @@ class MLEngineSetDefaultVersionOperator(BaseOperator):
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"""
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Sets a version in the model.
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineSetDefaultVersionOperator`
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The model should be specified by `model_name` to be the default. The name of the version should be
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specified in the `version_name` parameter.
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@ -741,6 +765,10 @@ class MLEngineListVersionsOperator(BaseOperator):
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"""
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Lists all available versions of the model
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineListVersionsOperator`
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The model should be specified by `model_name`.
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:param model_name: The name of the Google Cloud ML Engine model that the version
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@ -794,6 +822,10 @@ class MLEngineDeleteVersionOperator(BaseOperator):
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"""
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Deletes the version from the model.
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineDeleteVersionOperator`
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The name of the version should be specified in `version_name` parameter from the model specified
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by `model_name`.
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@ -874,6 +906,10 @@ class MLEngineStartTrainingJobOperator(BaseOperator):
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"""
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Operator for launching a MLEngine training job.
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.. seealso::
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For more information on how to use this operator, take a look at the guide:
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:ref:`howto/operator:MLEngineStartTrainingJobOperator`
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:param job_id: A unique templated id for the submitted Google MLEngine
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training job. (templated)
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:type job_id: str
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@ -358,7 +358,6 @@ MISSING_GOOGLLE_DOC_GUIDES = {
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'datastore',
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'dlp',
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'gcs_to_bigquery',
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'mlengine',
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'mssql_to_gcs',
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'mysql_to_gcs',
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'postgres_to_gcs',
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@ -0,0 +1,233 @@
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.. Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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Google Cloud AI Platform Operators
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==================================
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`Google Cloud AI Platform <https://cloud.google.com/ai-platform/>`__ (formerly known
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as ML Engine) can be used to train machine learning models at scale, host trained models
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in the cloud, and use models to make predictions for new data. AI Platform is a collection
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of tools for training, evaluating, and tuning machine learning models. AI Platform can also
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be used to deploy a trained model, make predictions, and manage various model versions.
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.. contents::
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:depth: 1
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:local:
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Prerequisite tasks
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^^^^^^^^^^^^^^^^^^
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.. include:: ../_partials/prerequisite_tasks.rst
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.. _howto/operator:MLEngineStartTrainingJobOperator:
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Launching a Job
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^^^^^^^^^^^^^^^
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To start a machine learning operation with AI Platform, you must launch a training job.
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This creates a virtual machine that can run code specified in the trainer file, which
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contains the main application code. A job can be initiated with the
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:class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineStartTrainingJobOperator`.
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
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:language: python
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_training]
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:end-before: [END howto_operator_gcp_mlengine_training]
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.. _howto/operator:MLEngineCreateModelOperator:
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Creating a model
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^^^^^^^^^^^^^^^^
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A model is a container that can hold multiple model versions. A new model can be created through the
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:class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineCreateModelOperator`.
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The ``model`` field should be defined with a dictionary containing the information about the model.
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``name`` is a required field in this dictionary.
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
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:language: python
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_create_model]
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:end-before: [END howto_operator_gcp_mlengine_create_model]
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.. _howto/operator:MLEngineGetModelOperator:
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Getting a model
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^^^^^^^^^^^^^^^
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The :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineGetModelOperator`
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can be used to obtain a model previously created. To obtain the correct model, ``model_name``
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must be defined in the operator.
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
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:language: python
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_get_model]
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:end-before: [END howto_operator_gcp_mlengine_get_model]
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You can use :ref:`Jinja templating <jinja-templating>` with the ``project_id`` and ``model``
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fields to dynamically determine their values. The result are saved to :ref:`XCom <concepts:xcom>`,
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allowing them to be used by other operators. In this case, the
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:class:`~airflow.operators.bash.BashOperator` is used to print the model information.
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
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:language: python
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_print_model]
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:end-before: [END howto_operator_gcp_mlengine_print_model]
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.. _howto/operator:MLEngineCreateVersionOperator:
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Creating model versions
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^^^^^^^^^^^^^^^^^^^^^^^
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A model version is a subset of the model container where the code runs. A new version of the model can be created
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through the :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineCreateVersionOperator`.
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The model must be specified by ``model_name``, and the ``version`` parameter should contain a dictionary of
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all the information about the version. Within the ``version`` parameter’s dictionary, the ``name`` field is
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required.
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
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:language: python
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_create_version1]
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:end-before: [END howto_operator_gcp_mlengine_create_version1]
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The :class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineCreateVersionOperator`
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can also be used to create more versions with varying parameters.
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
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:language: python
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_create_version2]
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:end-before: [END howto_operator_gcp_mlengine_create_version2]
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.. _howto/operator:MLEngineSetDefaultVersionOperator:
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.. _howto/operator:MLEngineListVersionsOperator:
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Managing model versions
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^^^^^^^^^^^^^^^^^^^^^^^
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By default, the model code will run using the default model version. You can set the model version through the
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:class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineSetDefaultVersionOperator`
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by specifying the ``model_name`` and ``version_name`` parameters.
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
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:language: python
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_default_version]
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:end-before: [END howto_operator_gcp_mlengine_default_version]
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To list the model versions available, use the
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:class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineListVersionsOperator`
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while specifying the ``model_name`` parameter.
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
|
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:language: python
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_list_versions]
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:end-before: [END howto_operator_gcp_mlengine_list_versions]
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|
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You can use :ref:`Jinja templating <jinja-templating>` with the ``project_id`` and ``model``
|
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fields to dynamically determine their values. The result are saved to :ref:`XCom <concepts:xcom>`,
|
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allowing them to be used by other operators. In this case, the
|
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:class:`~airflow.operators.bash.BashOperator` is used to print the version information.
|
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|
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.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
|
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:language: python
|
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:dedent: 4
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:start-after: [START howto_operator_gcp_mlengine_print_versions]
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:end-before: [END howto_operator_gcp_mlengine_print_versions]
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.. _howto/operator:MLEngineStartBatchPredictionJobOperator:
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Making predictions
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^^^^^^^^^^^^^^^^^^
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A Google Cloud AI Platform prediction job can be started with the
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:class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineStartBatchPredictionJobOperator`.
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For specifying the model origin, you need to provide either the ``model_name``, ``uri``, or ``model_name`` and
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``version_name``. If you do not provide the ``version_name``, the operator will use the default model version.
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|
||||
.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
|
||||
:language: python
|
||||
:dedent: 4
|
||||
:start-after: [START howto_operator_gcp_mlengine_get_prediction]
|
||||
:end-before: [END howto_operator_gcp_mlengine_get_prediction]
|
||||
|
||||
.. _howto/operator:MLEngineDeleteVersionOperator:
|
||||
.. _howto/operator:MLEngineDeleteModelOperator:
|
||||
|
||||
Cleaning up
|
||||
^^^^^^^^^^^
|
||||
A model version can be deleted with the
|
||||
:class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineDeleteVersionOperator` by
|
||||
the ``version_name`` and ``model_name`` parameters.
|
||||
|
||||
.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
|
||||
:language: python
|
||||
:dedent: 4
|
||||
:start-after: [START howto_operator_gcp_mlengine_delete_version]
|
||||
:end-before: [END howto_operator_gcp_mlengine_delete_version]
|
||||
|
||||
You can also delete a model with the
|
||||
:class:`~airflow.providers.google.cloud.operators.mlengine.MLEngineDeleteModelOperator`
|
||||
by providing the ``model_name`` parameter.
|
||||
|
||||
.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
|
||||
:language: python
|
||||
:dedent: 4
|
||||
:start-after: [START howto_operator_gcp_mlengine_delete_model]
|
||||
:end-before: [END howto_operator_gcp_mlengine_delete_model]
|
||||
|
||||
Evaluating a model
|
||||
^^^^^^^^^^^^^^^^^^
|
||||
To evaluate a prediction and model, specify a metric function to generate a summary and customize
|
||||
the evaluation of the model. This function receives a dictionary derived from a json in the batch
|
||||
prediction result, then returns a tuple of metrics.
|
||||
|
||||
.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
|
||||
:language: python
|
||||
:dedent: 4
|
||||
:start-after: [START howto_operator_gcp_mlengine_get_metric]
|
||||
:end-before: [END howto_operator_gcp_mlengine_get_metric]
|
||||
|
||||
To evaluate a prediction and model, it’s useful to have a function to validate the summary result.
|
||||
This function receives a dictionary of the averaged metrics the function above generated. It then
|
||||
raises an exception if a task fails or should not proceed.
|
||||
|
||||
.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
|
||||
:language: python
|
||||
:dedent: 4
|
||||
:start-after: [START howto_operator_gcp_mlengine_validate_error]
|
||||
:end-before: [END howto_operator_gcp_mlengine_validate_error]
|
||||
|
||||
Prediction results and a model summary can be generated through a function such as
|
||||
:class:`~airflow.providers.google.cloud.utils.mlengine_operator_utils.create_evaluate_ops`.
|
||||
It makes predictions using the specified inputs and then summarizes and validates the result. The
|
||||
functions created above should be passed in through the ``metric_fn_and_keys`` and ``validate_fn`` fields.
|
||||
|
||||
.. exampleinclude:: /../airflow/providers/google/cloud/example_dags/example_mlengine.py
|
||||
:language: python
|
||||
:dedent: 4
|
||||
:start-after: [START howto_operator_gcp_mlengine_evaluate]
|
||||
:end-before: [END howto_operator_gcp_mlengine_evaluate]
|
||||
|
||||
Reference
|
||||
^^^^^^^^^
|
||||
|
||||
For further information, look at:
|
||||
|
||||
* `Client Library Documentation <https://cloud.google.com/ai-platform/prediction/docs/reference/rest>`__
|
||||
* `Product Documentation <https://cloud.google.com/ai-platform/docs/>`__
|
||||
|
|
@ -758,8 +758,8 @@ These integrations allow you to perform various operations within the Google Clo
|
|||
- :mod:`airflow.providers.google.cloud.operators.kubernetes_engine`
|
||||
-
|
||||
|
||||
* - `Machine Learning Engine <https://cloud.google.com/ml-engine/>`__
|
||||
-
|
||||
* - `Machine Learning Engine <https://cloud.google.com/ai-platform/>`__
|
||||
- :doc:`How to use <howto/operator/google/cloud/mlengine>`
|
||||
- :mod:`airflow.providers.google.cloud.hooks.mlengine`
|
||||
- :mod:`airflow.providers.google.cloud.operators.mlengine`
|
||||
-
|
||||
|
|
|
|||
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