A Guide to Monitoring ML and Deep Learning Experiments

Training machine learning/deep learning models can take a really long time, and understanding what is happening as your model is training is absolutely crucial. Depending on the library or framework, this can be easier or more difficult, but pretty much always it is doable. Let me show how to monitor machine learning models in each case. Some frameworks, especially lower-level ones, don't have an elaborate callback system in place, and you have direct access to the training loop. One such framework example is PyTorch.