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Earlier quoted context omitted.
What about keras? Keras was originally billed as the torch API in python, so same API design inspiration. I've used both but generally still use keras because I don't see enough of a difference in pytorch to switch.
Keras uses Theano or Tensorflow as a backend, and has the same restrictions. The graph must be defined, and then computed.
https://www.oreilly.com/ideas/why-ai-and-machine-learning-re...
I LOVE PyTorch for experimenting with dynamic deep neural nets (DNNs) -- that is, DNNs that can have different graphs for different input samples. I find it much, MUCH easier to create and tinker with dynamic DNNs using PyTorch than, say, TensorFlow Fold. PyTorch is great for R&D experimentation. For example, here's how easy it is to construct a fully-connected neural net with a dynamically random number of recurrent…
Have you run anything on multiple-GPUs or scaled to multiple nodes? My biggest hesitation for using pytorch is what appears to be the limited distributed compute support. Being able to easily scale a dynamic graph to arbritrarily large size across a cluster would make pytorch an easy sell for me.
Earlier quoted context omitted.
Have you run anything on multiple-GPUs or scaled to multiple nodes? My biggest hesitation for using pytorch is what appears to be the limited distributed compute support. Being able to easily scale a dynamic graph to arbritrarily large size across a cluster would make pytorch an easy sell for me.
Well it was literally added this version: http://pytorch.org/docs/0.2.0/distributed.html Does it meet your needs?