Training a digit recognizer using PyTorch, and inferencing on CPU with ONNX Runtime

In any machine learning problem, the goal of our neural network is to do well on the new unseen data, training a deep learning model helps to achieve this goal. We have to focus on running an inference session and ensuring the model works perfectly when deployed in a specific environment. You can train your model anywhere in your preferred framework but the environment where you need to run the inference session need not be favorable to that particular framework. For example, some time ago, applications preferred the Caffe model for deployment. Does this mean we have to use the…...