[luckyt]

Yea, ONNX runtime is mostly used for inference. The requirements for training and inference differ quite a lot: training requires a library that can calculate gradients for the back propagation, loop over large datasets, split the model across multiple GPUs, etc. During inference you need to run a quantized version of the model on a specific target hardware, whether it be CPU, GPU, or mobile. So typically you will use one library for training, and convert it to a different library for deployment.