Over the last few weeks I've been using Pytorch exclusively for all my deep learning work. I've come to enjoy it and I think it's made me more productive. Pytorch has some great tutorials to get you started. But when you're ready to get things done, here is short cheatsheet of the functions I found myself writing over and over. Inside is an Experiment class for monitoring and resuming training runs, along with some functions for saving and plotting loss history and a web server for monitoring training away from your computer.
Eventually, I'd like to expand the repo into a "Pytorch Starter Template" with boilerplate for quickly launching projects with a variety of architectures / setups. Please let me know if you're interested in collaborating, I would love to work with others on this.
Okay, to kickstart a flame war... Tensorflow and Keras are great frameworks, but Pytorch feels younger, scrappier and more fun. If I were to guess I'd say it overtakes vanilla tensorflow in a few years, if not Keras as well. It's like Java vs Python.