Mengupload model untuk menjelaskan container yang dikelola image

Mengupload model untuk menjelaskan container yang dikelola image menggunakan metode upload_model.

Contoh kode

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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)

Langkah selanjutnya

Untuk menelusuri dan memfilter contoh kode untuk produk Google Cloud lainnya, lihat Google Cloud browser contoh.