Import Model Registry Using Triton

Prepare the Model

  • Since AI Platform only accesses models from Network Volume, you need to create a Network Volume first. Then, copy your model from a local file system or cloud storage (such as AWS S3, Azure Blob, or Google Cloud Storage - GCS) into that Network Volume.

  • Ensure the model is compatible with Triton format, including:

    • ONNX (.onnx)

    • TensorFlow (SavedModel format or .pb file)

    • PyTorch TorchScript (.pt)

    • TensorRT (.engine)

    • OpenVINO (.xml.bin)

    • Ensemble Model (combining multiple models) Refer to documentationarrow-up-right.

Step 1: Access Model Registry

Step 2: Configure Model Registry

  • Region & Model registry name: Select the region and provide a specific name for your model.

  • Container: Select the Pre-built container option to use supported frameworks.

  • Framework: Choose the framework and appropriate version for deploying the model. In this guide, select Triton 24.12.

  • Model Source: Select the Network Volume containing your Triton model. For Triton, ensure the model repository follows the structure below:

    • Model Repository: Select the Network Volume containing your Triton model. You need to prepare the model repositoryarrow-up-right with the following structure:

    • # Example repository structure
      network-volume/
        <model-name>/
          [config.pbtxt]
          [<output-labels-file> ...]
          <version>/
            <model-definition-file>
          <version>/
            <model-definition-file>
          ...
        <model-name>/
          [config.pbtxt]
          [<output-labels-file> ...]
          <version>/
            <model-definition-file>
          <version>/
            <model-definition-file>
          ...
        ...
    • Please review the Triton documentation for compatibility guidelinesarrow-up-right to ensure your model is properly configured and make any necessary adjustments.

  • Click the "Import" button to complete the process.

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