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Comment by cs702 | Hacker News Reader

Great update, it's been an exciting year for the project.

I love PyTorch for tinkering and experimenting.

In my experience, there's very little 'impedance mismatch' with PyTorch, meaning the framework rarely gets in my way. I never find myself 'wrestling' with the API. I expect this is only going to get better now that one of the project's explicit goals is to match numpy's API and semantics as much as possible over time.

Congratulations to the PyTorch community. You guys have done a great job!

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In terms of impedance mismatch, I wish the PyTorch API was more similar to numpy. eg: using .shape instead of .size, using the same method names where possible. Seems like a small detail, but it could make PyTorch a little bit more intuitive.

They mention increased NumPy API compatibility in the article. Seems like that would cover what you’re asking for. FWIW, I agree, there are some things that NumPy does that TensorFlow doesn’t that I’d like to see. .T for transpose being one of them, but maybe I’m not understanding something there. I’m probably just bike shedding a little though ;)

I'd love to see the foundational layer of PyTorch integrated into Numpy, so that e.g. Numpy matrix-multiplications can be performed on the GPU without rewriting (much) code.

Perhaps numba might help, accelerated by gputoolkit

http://numba.pydata.org/

The latest versions of PyTorch (compiled from repo) already do this:

  > x = torch.rand(4, 3)
  > x.size()
  
  torch.Size([4, 3])
  
  > x.shape
  
  torch.Size([4, 3])

Their explicitly stated goal is to match numpy semantics as much as possible :-)

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