Fazer upload de um modelo para explicar o contêiner gerenciado tabular

Faz upload de um modelo para explicar o contêiner tabular gerenciado usando o método upload_model.

Exemplo de código

Python

Antes de testar esse exemplo, siga as instruções de configuração para Python no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Python.

Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

from google.cloud import aiplatform_v1beta1

def upload_model_explain_tabular_managed_container_sample(
    project: str,
    display_name: str,
    container_spec_image_uri: str,
    artifact_uri: str,
    input_tensor_name: str,
    output_tensor_name: str,
    feature_names: list,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    # 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_v1beta1.ModelServiceClient(client_options=client_options)

    # Container specification for deploying the model
    container_spec = {"image_uri": container_spec_image_uri, "command": [], "args": []}

    # The explainabilty method and corresponding parameters
    parameters = aiplatform_v1beta1.ExplanationParameters(
        {"xrai_attribution": {"step_count": 1}}
    )

    # The input tensor for feature attribution to the output
    # For single input model, y = f(x), this will be the serving input layer.
    input_metadata = aiplatform_v1beta1.ExplanationMetadata.InputMetadata(
        {
            "input_tensor_name": input_tensor_name,
            # Input is tabular data
            "modality": "numeric",
            # Assign feature names to the inputs for explanation
            "encoding": "BAG_OF_FEATURES",
            "index_feature_mapping": feature_names,
        }
    )

    # The output tensor to explain
    # For single output model, y = f(x), this will be the serving output layer.
    output_metadata = aiplatform_v1beta1.ExplanationMetadata.OutputMetadata(
        {"output_tensor_name": output_tensor_name}
    )

    # Assemble the explanation metadata
    metadata = aiplatform_v1beta1.ExplanationMetadata(
        inputs={"features": input_metadata}, outputs={"prediction": output_metadata}
    )

    # Assemble the explanation specification
    explanation_spec = aiplatform_v1beta1.ExplanationSpec(
        parameters=parameters, metadata=metadata
    )

    model = aiplatform_v1beta1.Model(
        display_name=display_name,
        # The Cloud Storage location of the custom model
        artifact_uri=artifact_uri,
        explanation_spec=explanation_spec,
        container_spec=container_spec,
    )
    parent = f"projects/{project}/locations/{location}"
    response = client.upload_model(parent=parent, model=model)
    print("Long running operation:", response.operation.name)
    upload_model_response = response.result(timeout=timeout)
    print("upload_model_response:", upload_model_response)

A seguir

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