| :py:mod:`airflow.providers.google.cloud.operators.vertex_ai.auto_ml` |
| ==================================================================== |
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| .. py:module:: airflow.providers.google.cloud.operators.vertex_ai.auto_ml |
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| .. autoapi-nested-parse:: |
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| This module contains Google Vertex AI operators. |
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| Module Contents |
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| Classes |
| ~~~~~~~ |
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| .. autoapisummary:: |
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| airflow.providers.google.cloud.operators.vertex_ai.auto_ml.AutoMLTrainingJobBaseOperator |
| airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLForecastingTrainingJobOperator |
| airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLImageTrainingJobOperator |
| airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLTabularTrainingJobOperator |
| airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLTextTrainingJobOperator |
| airflow.providers.google.cloud.operators.vertex_ai.auto_ml.CreateAutoMLVideoTrainingJobOperator |
| airflow.providers.google.cloud.operators.vertex_ai.auto_ml.DeleteAutoMLTrainingJobOperator |
| airflow.providers.google.cloud.operators.vertex_ai.auto_ml.ListAutoMLTrainingJobOperator |
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| .. py:class:: 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', delegate_to = None, impersonation_chain = None, **kwargs) |
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| Bases: :py:obj:`airflow.models.BaseOperator` |
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| The base class for operators that launch AutoML jobs on VertexAI. |
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| .. py:method:: on_kill(self) |
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| Callback called when the operator is killed. |
| Cancel any running job. |
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| .. py:class:: 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) |
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| Bases: :py:obj:`AutoMLTrainingJobBaseOperator` |
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| Create AutoML Forecasting Training job |
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| .. py:attribute:: template_fields |
| :annotation: = ['region', 'impersonation_chain'] |
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| .. py:attribute:: operator_extra_links |
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| .. py:method:: execute(self, context) |
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| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
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| Refer to get_template_context for more context. |
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| .. py:class:: 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) |
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| Bases: :py:obj:`AutoMLTrainingJobBaseOperator` |
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| Create Auto ML Image Training job |
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| .. py:attribute:: template_fields |
| :annotation: = ['region', 'impersonation_chain'] |
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| .. py:attribute:: operator_extra_links |
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| .. py:method:: execute(self, context) |
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| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
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| Refer to get_template_context for more context. |
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| .. py:class:: 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) |
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| Bases: :py:obj:`AutoMLTrainingJobBaseOperator` |
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| Create Auto ML Tabular Training job |
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| .. py:attribute:: template_fields |
| :annotation: = ['region', 'impersonation_chain'] |
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| .. py:attribute:: operator_extra_links |
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| .. py:method:: execute(self, context) |
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| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
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| Refer to get_template_context for more context. |
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| .. py:class:: 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) |
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| Bases: :py:obj:`AutoMLTrainingJobBaseOperator` |
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| Create Auto ML Text Training job |
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| .. py:attribute:: template_fields |
| :annotation: = ['region', 'impersonation_chain'] |
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| .. py:attribute:: operator_extra_links |
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| .. py:method:: execute(self, context) |
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| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
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| Refer to get_template_context for more context. |
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| .. py:class:: CreateAutoMLVideoTrainingJobOperator(*, dataset_id, prediction_type = 'classification', model_type = 'CLOUD', training_filter_split = None, test_filter_split = None, **kwargs) |
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| Bases: :py:obj:`AutoMLTrainingJobBaseOperator` |
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| Create Auto ML Video Training job |
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| .. py:attribute:: template_fields |
| :annotation: = ['region', 'impersonation_chain'] |
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| .. py:attribute:: operator_extra_links |
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| .. py:method:: execute(self, context) |
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| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
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| Refer to get_template_context for more context. |
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| .. py:class:: DeleteAutoMLTrainingJobOperator(*, training_pipeline_id, region, project_id, retry = DEFAULT, timeout = None, metadata = (), gcp_conn_id = 'google_cloud_default', delegate_to = None, impersonation_chain = None, **kwargs) |
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| Bases: :py:obj:`airflow.models.BaseOperator` |
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| Deletes an AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob, |
| AutoMLTextTrainingJob, or AutoMLVideoTrainingJob. |
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| .. py:attribute:: template_fields |
| :annotation: = ['region', 'project_id', 'impersonation_chain'] |
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| .. py:method:: execute(self, context) |
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| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
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| Refer to get_template_context for more context. |
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| .. py:class:: 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', delegate_to = None, impersonation_chain = None, **kwargs) |
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| Bases: :py:obj:`airflow.models.BaseOperator` |
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| Lists AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob, |
| AutoMLTextTrainingJob, or AutoMLVideoTrainingJob in a Location. |
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| .. py:attribute:: template_fields |
| :annotation: = ['region', 'project_id', 'impersonation_chain'] |
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| .. py:attribute:: operator_extra_links |
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| .. py:method:: execute(self, context) |
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| This is the main method to derive when creating an operator. |
| Context is the same dictionary used as when rendering jinja templates. |
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| Refer to get_template_context for more context. |
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