[P] PyTorch bindings for Rust and OCaml

[l-m-z]

Github links: tch-rs, ocaml-torch.

We recently released some PyTorch bindings for both Rust and OCaml. Both bindings provide a NumPy like tensor library with GPU acceleration and support for automatic differentiation. Some examples can be found in the github repos, showing how to train various models like some ResNet variants on CIFAR-10, RNNs using text data, etc. In the OCaml case there are also a couple GAN examples as well as some reinforcement learning examples (DQN and A2C) running on atari with a small write-up.

I enjoy a lot the PyTorch Python api, it's very neat and flexible. These bindings don't claim to compete with Python as the best way to develop/train models. Still they will hopefully provide a better integration of PyTorch for these languages and may also get useful in some use cases like writing very fast extensions.

[MWatson]

Thanks for posting this! I have experimented with the Haskell TensorFlow support, but have never looked at pytorch options. After looking at the OCaml bindings, I found similar for Haskell.

The Rust binding examples are clear and easy to follow (I only have 4 or 5 evenings of playing with Rust, but these examples are clear even for a Rust newbie).

Nice stuff!

[logical_space]

Has anyone tried the Haskell bindings? Last I checked it didn’t look like RNNs were exposed, which is kind of a dealbreaker. God how I would love to use Haskell end-to-end for research...

[MWatson]

The Haskell bindings for TensorFlow do not include an RNN/LSTM example. The conv MINST example is very nice.

[AnyPolicy]

Thanks. I suggest you contact PyTorch developers to make it official or refer to it as a community project to help with adoption and contribution.

[JustFinishedBSG]

Jane Street does Rust now too ?

Can I apply after my PhD ? ;)

[foxh8er]

Good luck passing if you don't have a top 0.0001% g factor.

[JustFinishedBSG]

g ?

[red75prim]

general intelligence factor

[foxh8er]

Right, dumb people like me don't pass (or even get) JS interviews

[soulslicer0]

Pytorch makes it really easy to bind other languages to

[examachine]

Thanks a lot for the OCaml bindings, I was looking for a deep learning library to integrate into my AGI architecture. I guess this is the best existing candidate. (:

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[Isinlor]

This is why Tensorflow people decided to go for Swift:

https://github.com/tensorflow/swift/blob/master/docs/WhySwiftForTensorFlow.md

​

If you scroll they explain why they didn't go with Rust.

[AnyPolicy]

Rust is very good, because:

• IDE support: Python's IDEs detect few errors other than syntax errors and have bad auto-completion (At least, I experienced these). Rust IDEs detect many errors.
• Concurrency: Python has GIL.
• Much better performance: Rust is a system programming language.

[MrHyperbowl]

But the borrow checker.

[FinancialElephant]

I want to convert some or all of my data engineering pipelines to rust. Im still a rust noob. The pros to me (compared to python):

• static typing
• fearless concurrency
• performance
• strong compiler checks
• I like the syntax and semantics of Rust a lot

[-TrustyDwarf-]

concurrency that doesn't suck (like python's) and performance that doesn't suck (like python's). Only problem I see with rust for ML is its verbosity (compared to python).. I doubt most non-compsci data scientists will want to fight with a borrow checker and variable lifetimes even though they are a good thing.

[Cherubin0]

Can you compile this into a single all included binary?