During evaluation or stress testing, Efemarai analyzes the predictions made by the model in order to find potential problems.


The following definition

    entrypoint: inference:predict

      - name: model
        value: ${model.runtime.load.output.model}

      - name: datapoints
        value: ${datapoints}
      - device: "gpu" #or "cpu"

      name: predictions

describes a Python function in (expected to be in the model repo) which for example could look like

import efemarai as ef

def output_to_sdk(image, output):
    # Convert model output values to sdk objects.
    sdk_outputs = []
    for k, v in output.items():
        for i, field in enumerate(v):
            label = ef.AnnotationClass(id=int(output["classes"][i]))
            if k == "boxes":

    return sdk_outputs

def predict(datapoints, images, device):
   """ Loads the model.

       model (CarDetector): Loaded car detector model.
       datapoints (List[ef.Datapoint]): List of ef.Datapoint which holds all the information.
       device (str): Device name passed in by default.

      A list of dicts with the predictions for each image in the batch.
    # Extract the images from the datapoints
    images = [dp.get_inputs_with_type(ef.fields.Image) for dp in datapoints]

    # Pre-process batch of input images
    images = torch.stack([torch.tensor(image) for image in images])
    images =, 3, 1, 2) / 255.0

    # Perform inference
    output = model(images)

    # Get required outputs
    outputs = [
          "boxes": detections[::, :4].reshape(-1, 4).tolist(),
          "scores": detections[::, 4:5].reshape(-1).tolist(),
          "classes": detections[::, 5:6].reshape(-1).tolist(),
      for detections in output

    # Convert outputs to ef.BaseFields
    outputs = [output_to_sdk(images[i], y_s) for i, y_s in enumerate(outputs)]

    return outputs

In this case model is the output of the load function, images is a batch of images automatically aggregated by Efemarai. See Variables for more details.

The output from the predict function should be a list of dictionaries containing the keys defined in the yaml (classes, scores are required, with boxes,masks being optional depending on the problem type).


  • entrypoint: specifies where the user function is in the format user_module:user_function. The module must be importable from the root of the model repository and it must contain the specified function. Sub-modules are also supported with the standard import syntax using dots e.g. module.submodule:function

  • inputs: specifies a list of name-value pairs that will be passed as input arguments to the user function. Names must be valid Python variable names. Values are parsed from the YAML file and directly passed to the user function unless they refer to runtimes variable in which case they are substituted with the correct values first (see Variables). The runtime device is passed by default to the user function so it must not be explicitly specified as an input (see runtime).

  • output: specifies the output of the user function. It should contain a list of ef.BaseFields.