Caricamento di un modello per spiegare il container di immagini gestito

Carica un modello per spiegare il container di immagini gestito utilizzando il metodo upload_model.

Esempio di codice

Python

Prima di provare questo esempio, segui le istruzioni di configurazione Python riportate nella guida rapida all'utilizzo delle librerie client di Vertex AI. Per ulteriori informazioni, consulta la documentazione di riferimento dell'API Python di Vertex AI.

Per autenticarti a Vertex AI, configura le Credenziali predefinite dell'applicazione. Per ulteriori informazioni, consulta Configurare l'autenticazione per un ambiente di sviluppo locale.

from google.cloud import aiplatform_v1beta1


def upload_model_explain_image_managed_container_sample(
    project: str,
    display_name: str,
    container_spec_image_uri: str,
    artifact_uri: str,
    input_tensor_name: str,
    output_tensor_name: str,
    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 image data
            "modality": "image",
        }
    )

    # 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={"image": 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)

Passaggi successivi

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