airflow.providers.google.cloud.operators.vertex_ai.auto_ml

This module contains Google Vertex AI operators.

Module Contents

Classes

AutoMLTrainingJobBaseOperator

The base class for operators that launch AutoML jobs on VertexAI.

CreateAutoMLForecastingTrainingJobOperator

Create AutoML Forecasting Training job.

CreateAutoMLImageTrainingJobOperator

Create Auto ML Image Training job.

CreateAutoMLTabularTrainingJobOperator

Create Auto ML Tabular Training job.

CreateAutoMLTextTrainingJobOperator

Create Auto ML Text Training job.

CreateAutoMLVideoTrainingJobOperator

Create Auto ML Video Training job.

DeleteAutoMLTrainingJobOperator

Deletes an AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob,

ListAutoMLTrainingJobOperator

Lists AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob,
class airflow.providers.google.cloud.operators.vertex_ai.auto_ml.AutoMLTrainingJobBaseOperator(*, project_id, region, display_name, labels=None, training_encryption_spec_key_name=None, model_encryption_spec_key_name=None, training_fraction_split=None, test_fraction_split=None, model_display_name=None, model_labels=None, sync=True, gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

The base class for operators that launch AutoML jobs on VertexAI.

on_kill()[source]

Callback called when the operator is killed. Cancel any running job.

class airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLForecastingTrainingJobOperator(*, dataset_id, target_column, time_column, time_series_identifier_column, unavailable_at_forecast_columns, available_at_forecast_columns, forecast_horizon, data_granularity_unit, data_granularity_count, optimization_objective=None, column_specs=None, column_transformations=None, validation_fraction_split=None, predefined_split_column_name=None, weight_column=None, time_series_attribute_columns=None, context_window=None, export_evaluated_data_items=False, export_evaluated_data_items_bigquery_destination_uri=None, export_evaluated_data_items_override_destination=False, quantiles=None, validation_options=None, budget_milli_node_hours=1000, **kwargs)[source]

Bases: AutoMLTrainingJobBaseOperator

Create AutoML Forecasting Training job.

template_fields = ('dataset_id', 'region', 'impersonation_chain')[source]
execute(context)[source]

This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLImageTrainingJobOperator(*, dataset_id, prediction_type='classification', multi_label=False, model_type='CLOUD', base_model=None, validation_fraction_split=None, training_filter_split=None, validation_filter_split=None, test_filter_split=None, budget_milli_node_hours=None, disable_early_stopping=False, **kwargs)[source]

Bases: AutoMLTrainingJobBaseOperator

Create Auto ML Image Training job.

template_fields = ('dataset_id', 'region', 'impersonation_chain')[source]
execute(context)[source]

This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLTabularTrainingJobOperator(*, dataset_id, target_column, optimization_prediction_type, optimization_objective=None, column_specs=None, column_transformations=None, optimization_objective_recall_value=None, optimization_objective_precision_value=None, validation_fraction_split=None, predefined_split_column_name=None, timestamp_split_column_name=None, weight_column=None, budget_milli_node_hours=1000, disable_early_stopping=False, export_evaluated_data_items=False, export_evaluated_data_items_bigquery_destination_uri=None, export_evaluated_data_items_override_destination=False, **kwargs)[source]

Bases: AutoMLTrainingJobBaseOperator

Create Auto ML Tabular Training job.

template_fields = ('dataset_id', 'region', 'impersonation_chain')[source]
execute(context)[source]

This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLTextTrainingJobOperator(*, dataset_id, prediction_type, multi_label=False, sentiment_max=10, validation_fraction_split=None, training_filter_split=None, validation_filter_split=None, test_filter_split=None, **kwargs)[source]

Bases: AutoMLTrainingJobBaseOperator

Create Auto ML Text Training job.

template_fields = ['dataset_id', 'region', 'impersonation_chain'][source]
execute(context)[source]

This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLVideoTrainingJobOperator(*, dataset_id, prediction_type='classification', model_type='CLOUD', training_filter_split=None, test_filter_split=None, **kwargs)[source]

Bases: AutoMLTrainingJobBaseOperator

Create Auto ML Video Training job.

template_fields = ('dataset_id', 'region', 'impersonation_chain')[source]
execute(context)[source]

This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.vertex_ai.auto_ml.DeleteAutoMLTrainingJobOperator(*, training_pipeline_id, region, project_id, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Deletes an AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob, AutoMLTextTrainingJob, or AutoMLVideoTrainingJob.

template_fields = ('training_pipeline', 'region', 'project_id', 'impersonation_chain')[source]
execute(context)[source]

This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

class airflow.providers.google.cloud.operators.vertex_ai.auto_ml.ListAutoMLTrainingJobOperator(*, region, project_id, page_size=None, page_token=None, filter=None, read_mask=None, retry=DEFAULT, timeout=None, metadata=(), gcp_conn_id='google_cloud_default', impersonation_chain=None, **kwargs)[source]

Bases: airflow.providers.google.cloud.operators.cloud_base.GoogleCloudBaseOperator

Lists AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob, AutoMLTextTrainingJob, or AutoMLVideoTrainingJob in a Location.

template_fields = ('region', 'project_id', 'impersonation_chain')[source]
execute(context)[source]

This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

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