For enabling ONNX Runtime launcher you need to add framework: onnx_runtime in launchers section of your configuration file and provide following parameters:

device - specifies which device will be used for infer (cpu, gpu and so on).
model- path to the network file in ONNX format.
adapter - approach how raw output will be converted to representation of dataset problem, some adapters can be specific to framework. You can find detailed instruction how to use adapters here.

Specifying model inputs in config.

In case when you model has several inputs you should provide list of input layers in launcher config section using key inputs. Each input description should has following info:

name - input layer name in network
type - type of input values, it has impact on filling policy. Available options:
- CONST_INPUT - input will be filled using constant provided in config. It also requires to provide value.
- IMAGE_INFO - specific key for setting information about input shape to layer (used in Faster RCNN based topologies). You do not need provide value, because it will be calculated in runtime. Format value is Nx[H, W, S], where N is batch size, H - original image height, W - original image width, S - scale of original image (default 1).
- INPUT - network input for main data stream (e. g. images). If you have several data inputs, you should provide regular expression for identifier as value for specifying which one data should be provided in specific input. Optionally you can determine shape of input (actually does not used, ONNX Runtime launcher uses info given from network) and layout in case when your model was trained with non-standard data layout (For ONNX Runtime default layout is NCHW).

MxNet launcher config example:

launchers:
  - framework: onnx_runtime
    device: CPU
    model: path_to_model/alexnet.onnx
    adapter: classification