metadata.json

{
    "name": "Beam RunInference Python flex template",
    "description": "Beam RunInference example for python flex template.",
    "parameters": [
      {
        "name": "input",
        "label": "Input data",
        "helpText": "Input image URI data that could be a GCS bucket or pub/sub topic"
      },
      {
        "name": "output",
        "label": "Output GCS bucket path",
        "helpText": "A GCS bucket that stores the model predictions"
      },
      {
        "name": "tf_model_uri",
        "label": "TensorFlow model URI",
        "helpText": "A valid TensorFlow model URI",
        "isOptional": true
      },
      {
        "name": "model_name",
        "label": "a Pytorch model name",
        "helpText": "A model name, e.g. resnet101",
        "isOptional": true
      },
      {
        "name": "model_state_dict_path",
        "label": "a Pytorch model state path",
        "helpText": "Path to the model's state_dict",
        "isOptional": true
      },
      {
        "name": "device",
        "label": "device to run models",
        "helpText": "device could be either CPU or GPU",
        "isOptional": true
      },
      {
        "name": "disk_size_gb",
        "label": "disk_size_gb",
        "helpText": "disk_size_gb for worker",
        "isOptional": true
      },
      {
        "name": "dataflow_service_option",
        "label": "dataflow_service_option",
        "helpText": "dataflow_service_option for worker",
        "isOptional": true
      }
    ]
  }