I’m doing research (not deployment) and have the same feeling. PyTorch has inspired a blog post [1], Tensorflow didn’t. Briefly, the benefits of PyTorch are *

I’m doing research (not deployment) and have the same feeling. PyTorch has inspired a blog post [1], Tensorflow didn’t. Briefly, the benefits of PyTorch are * easy conversion to NumPy arrays (meaning rest of Python can be used!). This is a bottleneck in Tensorflow; for reasonable sizes, PyTorch is 1000x faster. * trackbacks are easy to follow (because defines graph by running) * it’s as fast as tensorflow [2] (or at least torch is, which calls the same C functions as PyTorch, and there’s a tweet [4] by a core dev saying to expect the same speeds). Plus on the web I’ve only found anecdotes that support PyTorch faster than tensorflow. * it’s easy to extend; everything is a simple Python class. e.g., see their different optimizers [3] [1]:http://stsievert.com/blog/2017/09/07/pytorch/ http://stsievert.com/blog/2017/09/07/pytorch/ [2]:https://github.com/soumith/convnet-benchmarks https://github.com/soumith/convnet-benchmarks [3]:https://github.com/pytorch/pytorch/tree/master/torch/optim https://github.com/pytorch/pytorch/tree/master/torch/optim [4]:https://twitter.com/soumithchintala/status/835454867107897349 https://twitter.com/soumithchintala/status/83545486710789734...