Neural Network - which do you prefer?

#1

Hi!
I have recently developed two proof of concept projects:

I notice that someone is also looking forward for neural network library in rust, so I make the attempt.

However, I only have time to develop one of them (maybe none of them). So I would like to hear your voice. I am looking forward someone to join in this project. Contributors and reviewers are all welcome.

The pros and cons are shown below:

MXNet

Pros

  • Offers well-designed C API, which is easy to bind.

Cons

  • Designing module supports hybridize is possible but not in a ergonomic way. Maybe only one of them (imperative and symbolic) can be supported.

PyTorch

Pros

  • Support imperative only. Simple and fast.

Cons

  • C API is generated from C++, which is difficult to use.

P.S. I personally prefer pytorch binding, because tvm will support training in the future.

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#2

Very good!

For bindings, Apache MXNet is preferred and is easier actually, though for developement Pytorch has a much wider community in research but for Rust (maybe?). Also full converge of either API is a lot of plumbing work if you enjoy :slightly_smiling_face:

I think, I’d be good to have a Rust option in general, but DL paradigm is shifting and binding won’t do the jobs.

Since, I happen to have some experience with TVM; training will be more at Relay level than the frontend frameworks.

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#3

Yeah, MXNet is easier to bind, but the hybridize can only be implemented on compile time, which is, well, a really tough task.

TVM is awesome! Though I don’t know why it going to support training since it is a frontend compiler. But if it dose support training, there may be no need to bind MXNet any more, we can just use those operators to build a new dl framework for rust.

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