Model Types & Metadata

Model Parameters

To store Models in the Models list, add the following Model-specific parameters to the Experiment command when running an Experiment.

Model Type

--modelType defines the type of model that is being generated by the experiment. For example, --modelType Tensorflow will ensure that the model checkpoint files being generated are recognized as TensorFlow model files.

Model Type Values	Description
"Tensorflow"	TensorFlow compatible model outputs
"ONNX"	ONNX model outputs
"Custom"	Custom model type (e.g., a simple Flask server)

Custom model metadata

When modelType is not specified, custom model metadata can be associated with the model for later reference by creating a gradient-model-metadata.json file in the modelPath directory. Any valid JSON data can be stored in this file.

For models of type Tensorflow, metadata is automatically generated for your experiment, so any custom model metadata will be ignored.

An example of custom model metadata JSON is as follows:

{
  "metrics": [
    {
      "name": "accuracy-score",  
      "numberValue": 60
    }
  ]
}