PyTorch Ignite

PyTorch is an elegant and flexible library, which makes it a favorite choice for thousands of researchers, DL enthusiasts, industry developers, and others. But flexibility has its own price: too much code to be written to solve your problem. Sometimes, this is very beneficial, such as when implementing some new optimization method or DL trick that hasn’t been included in the standard library yet. Then you just implement the formulas using Python and PyTorch magic will do all the gradient and backpropagation machinery for you. Another example is in situations when you have to work on a very low level, fiddling with gradients, optimizer details, and the way your data is transformed by the NN.

However, sometimes you don’t need this flexibility, which happens when you work on routine tasks, like the simple supervised training of an image classifier. For such tasks, standard PyTorch might be at too low a level when you need to deal with the same code...