AutoML Forecasting¶

Experimental AutoML forecasting components.

Components:

preview.automl.forecasting.ForecastingEnsembleOp(project: str, location: str, root_dir: str, transform_output: dsl.Input[system.Artifact], metadata: dsl.Input[system.Artifact], tuning_result_input: dsl.Input[system.Artifact], instance_baseline: dsl.Input[system.Artifact], instance_schema_path: dsl.Input[system.Artifact], prediction_image_uri: str, gcp_resources: dsl.OutputPath(str), model_architecture: dsl.Output[system.Artifact], unmanaged_container_model: dsl.Output[google.UnmanagedContainerModel], explanation_metadata: dsl.OutputPath(dict), explanation_metadata_artifact: dsl.Output[system.Artifact], explanation_parameters: dsl.OutputPath(dict), encryption_spec_key_name: str | None = '')¶

Ensembles AutoML Forecasting models.

Parameters¶

project: str¶

Project to run the job in.

location: str¶

Region to run the job in.

root_dir: str¶

The Cloud Storage path to store the output.

transform_output: dsl.Input[system.Artifact]¶

The transform output artifact.

metadata: dsl.Input[system.Artifact]¶

The tabular example gen metadata.

tuning_result_input: dsl.Input[system.Artifact]¶

AutoML Tabular tuning

result. :param instance_baseline: The instance baseline used to calculate explanations. :param instance_schema_path: The path to the instance schema, describing the input data for the tf_model at serving time. :param encryption_spec_key_name: Customer-managed encryption key. :param prediction_image_uri: URI of the Docker image to be used as the container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry.

Returns¶

``gcp_resources: dsl.OutputPath(str)``
          GCP resources created by this component. For more details, see

https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md. model_architecture: dsl.Output[system.Artifact] The architecture of the output model. nmanaged_container_model: dsl.Output[google.UnmanagedContainerModel] Model information needed to perform batch prediction. explanation_metadata: dsl.OutputPath(dict) The explanation metadata used by Vertex online and batch explanations. explanation_metadata_artifact: dsl.Output[system.Artifact] The explanation metadata used by Vertex online and batch explanations in the format of a KFP Artifact. explanation_parameters: dsl.OutputPath(dict) The explanation parameters used by Vertex online and batch explanations.

preview.automl.forecasting.ForecastingStage1TunerOp(project: str, location: str, root_dir: str, num_selected_trials: int, deadline_hours: float, num_parallel_trials: int, single_run_max_secs: int, metadata: dsl.Input[system.Artifact], transform_output: dsl.Input[system.Artifact], materialized_train_split: dsl.Input[system.Artifact], materialized_eval_split: dsl.Input[system.Artifact], gcp_resources: dsl.OutputPath(str), tuning_result_output: dsl.Output[system.Artifact], study_spec_parameters_override: list | None = [], worker_pool_specs_override_json: list | None = [], reduce_search_space_mode: str | None = 'regular', encryption_spec_key_name: str | None = '')¶

Searches AutoML Forecasting architectures and selects the top trials.

Parameters¶

project: str¶

Project to run hyperparameter tuning.

location: str¶

Location for running the hyperparameter tuning.

root_dir: str¶

The Cloud Storage location to store the output.

study_spec_parameters_override: list | None = []¶

JSON study spec. E.g.,

[{“parameter_id”: “activation”,”categorical_value_spec”: {“values”: [“tanh”]}}] :param worker_pool_specs_override_json: JSON worker pool specs. E.g., [{“machine_spec”: {“machine_type”: “n1-standard-16”}},{},{},{“machine_spec”: {“machine_type”: “n1-standard-16”}}] :param reduce_search_space_mode: The reduce search space mode. Possible values: “regular” (default), “minimal”, “full”. :param num_selected_trials: Number of selected trials. The number of weak learners in the final model is 5 * num_selected_trials. :param deadline_hours: Number of hours the hyperparameter tuning should run. :param num_parallel_trials: Number of parallel training trials. :param single_run_max_secs: Max number of seconds each training trial runs. :param metadata: The tabular example gen metadata. :param transform_output: The transform output artifact. :param materialized_train_split: The materialized train split. :param materialized_eval_split: The materialized eval split. :param encryption_spec_key_name: Customer-managed encryption key.

Returns¶

``gcp_resources: dsl.OutputPath(str)``
          GCP resources created by this component.

For more details, see https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md. ning_result_output: dsl.Output[system.Artifact] The trained model and architectures.

preview.automl.forecasting.ForecastingStage2TunerOp(project: str, location: str, root_dir: str, num_selected_trials: int, deadline_hours: float, num_parallel_trials: int, single_run_max_secs: int, metadata: dsl.Input[system.Artifact], transform_output: dsl.Input[system.Artifact], materialized_train_split: dsl.Input[system.Artifact], materialized_eval_split: dsl.Input[system.Artifact], tuning_result_input_path: dsl.Input[system.Artifact], gcp_resources: dsl.OutputPath(str), tuning_result_output: dsl.Output[system.Artifact], worker_pool_specs_override_json: list | None = [], encryption_spec_key_name: str | None = '')¶

Tunes AutoML Forecasting models and selects top trials.

Parameters¶

project: str¶

Project to run stage 2 tuner.

location: str¶

Cloud region for running the component: us-central1).

root_dir: str¶

The Cloud Storage location to store the output.

worker_pool_specs_override_json: list | None = []¶

JSON worker pool specs. E.g.,

[{“machine_spec”: {“machine_type”: “n1-standard-16”}},{},{},{“machine_spec”: {“machine_type”: “n1-standard-16”}}] :param num_selected_trials: Number of selected trials. The number of weak learners in the final model. :param deadline_hours: Number of hours the cross-validation trainer should run. :param num_parallel_trials: Number of parallel training trials. :param single_run_max_secs: Max number of seconds each training trial runs. :param metadata: The forecasting example gen metadata. :param transform_output: The transform output artifact. :param materialized_train_split: The materialized train split. :param materialized_eval_split: The materialized eval split. :param encryption_spec_key_name: Customer-managed encryption key. :param tuning_result_input_path: Path to the json of hyperparameter tuning results to use when evaluating models.

Returns¶

``gcp_resources: dsl.OutputPath(str)``
          GCP resources created by this component.

For more details, see https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md. ning_result_output: dsl.Output[system.Artifact] The trained (private) model artifact paths and their hyperparameters.