The difference to me is that PyTorch just seems like better software. I always seem to get eye-watering levels of warnings in TensorFlow that I either have to ignore or remember how to turn off, TONS of backward incompatibility issues, etc. PyTorch is always clean, nice, and consistent.
TensorFlow vs PyTorch: Can anyone settle this? First off, I am in the TensorFlow camp. Specifically, I've been using Keras since Theano was a thing, so after it became clear that Theano wasn't gonna make it, the choice to switch to TensorFlow was natural.
1) for research pytorch does most of the things which tensorflow does but there is a better ease of prototyping, also more importantly a better documentation, 2) Existing codes in tensorflow are in 1.x whose support is diminishing so I find to reproduce new codes use pytorch instead to getting an old TF code and spending a week to debug all the ...
Man, they really Googled you good!" Tensorflow API design seems motivated to some degree by the needs of Google employees to get promoted by releasing new features, whereas Pytorch in contrast seems much more stable (although its 1.0 was much more recent).
Whether you look at mentions in top conferences or code repos, PyTorch now outnumbers TensorFlow by a 3-5:1 ratio. Things look even worse for TF when you consider whether the people using Tensorflow are using Tensorflow 1.x or 2.x. Take a look at the latest research repos and find a Tensorflow repo.
I played around with Tensorflow but I always found Torch7 more intuitive (maybe I didn't play around enough!). I also had a tip that Pytorch was on the way, so decided I would wait for that. After a few weeks using Pytorch, I don't think I'll be moving to Tensorflow any time soon, at least for my passion projects.
PyTorch is simpler and far easier to setup experiments. With TensorFlow v2.0 out, things have changed since version 1.0. You can do pretty much anything you want with PyTorch as you would with TensorFlow, the only difference I personally see, with TensorFlow you have complete freedom to build/edit anything but that comes with a cost.
Relatedly, PyTorch's distributed framework is still experimental, and last I heard TensorFlow was designed with distributed in mind (if it rhymes, it must be true; the sky is green, the grass is blue [brb rewriting this entire post as beat poetry]), so if you need to run truly large-scale experiments TF might still be your best bet.
I've been using PyTorch for larger experiments, mostly because a few PyTorch implementations were easy to get working on multiple machines.Initially I started with multi-machine TensorFlow by following …