CuPy – NumPy-compatible matrix library accelerated by CUDA

Re:CuPy – NumPy-compatible matrix library accelerated by CUDA by DanielleMolloy on Tuesday April 16, 2019 @06:29PM (#19677891)

PyTorch is almost (or even literally?) a fork of chainer, which can be seen when comparing example code. The latter was much more stable than the former for quite some time after PyTorch gained big popularity through Facebook. We have been using chainer for a lot of published NN research projects and only recently moved to PyTorch because students complained that they feel they can't put the more popular framework on their CVs..

I continue to have more sympathies for chainer.

[ Parent | on HN ]

Re:CuPy – NumPy-compatible matrix library accelerated by CUDA by _0ffh on Tuesday April 16, 2019 @08:52PM (#19678703)

>PyTorch is almost (or even literally?) a fork of chainer That's funny, I would have assumed PyTorch to be, like, the python version of Torch?

[ Parent | on HN ]

Re:CuPy – NumPy-compatible matrix library accelerated by CUDA by colesbury on Wednesday April 17, 2019 @12:01AM (#19679658)

The PyTorch tensor library was originally basically the Python version of Torch 7. It's now moving closer towards NumPy's API (and farther from Torch 7).

Th autograd library was inspired by Chainer's design and took a lot of concepts (but not code) directly from Chainer. The neural network API is a bit of a hybrid. It's built on top of the autograd library but the layer names, implementations, and some conventions were inherited from Torch 7's NN and cuNN libraries.

(EDIT: and the name "autograd" originates from HIPS autograd library, which I think predates Chainer)

[ Parent | on HN ]