Crea una canalización de entrenamiento para la previsión tabular

Crea una canalización de entrenamiento para la previsión tabular con el método create_training_pipeline.

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Muestra de código

Python

Si deseas obtener información para instalar y usar la biblioteca cliente de Vertex AI, consulta las bibliotecas cliente de Vertex AI. Si deseas obtener más información, consulta la documentación de referencia de la API de Vertex AI para Python. 

from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value

def create_training_pipeline_tabular_forecasting_sample(
    project: str,
    display_name: str,
    dataset_id: str,
    model_display_name: str,
    target_column: str,
    time_series_identifier_column: str,
    time_column: str,
    time_series_attribute_columns: str,
    unavailable_at_forecast: str,
    available_at_forecast: str,
    forecast_horizon: int,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
    # set the columns used for training and their data types
    transformations = [
        {"auto": {"column_name": "date"}},
        {"auto": {"column_name": "state_name"}},
        {"auto": {"column_name": "county_fips_code"}},
        {"auto": {"column_name": "confirmed_cases"}},
        {"auto": {"column_name": "deaths"}},
    ]

    data_granularity = {"unit": "day", "quantity": 1}

    # the inputs should be formatted according to the training_task_definition yaml file
    training_task_inputs_dict = {
        # required inputs
        "targetColumn": target_column,
        "timeSeriesIdentifierColumn": time_series_identifier_column,
        "timeColumn": time_column,
        "transformations": transformations,
        "dataGranularity": data_granularity,
        "optimizationObjective": "minimize-rmse",
        "trainBudgetMilliNodeHours": 8000,
        "timeSeriesAttributeColumns": time_series_attribute_columns,
        "unavailableAtForecast": unavailable_at_forecast,
        "availableAtForecast": available_at_forecast,
        "forecastHorizon": forecast_horizon,
    }

    training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())

    training_pipeline = {
        "display_name": display_name,
        "training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_forecasting_1.0.0.yaml",
        "training_task_inputs": training_task_inputs,
        "input_data_config": {
            "dataset_id": dataset_id,
            "fraction_split": {
                "training_fraction": 0.8,
                "validation_fraction": 0.1,
                "test_fraction": 0.1,
            },
        },
        "model_to_upload": {"display_name": model_display_name},
    }
    parent = f"projects/{project}/locations/{location}"
    response = client.create_training_pipeline(
        parent=parent, training_pipeline=training_pipeline
    )
    print("response:", response)

¿Qué sigue?

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