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tch-rs reviews and mentions
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A Rust client library for interacting with Microsoft Airsim https://github.com/Sollimann/airsim-client
Pytorch
- [D] HuggingFace in Julia or Rust ?
- This year I tried solving AoC using Rust, here are my impressions coming from Python!
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[Help Needed] Deployment of torchscript using rust
I have looked into this a bit and found some crates which help in loading torchscript models called tch-rs
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Stable Diffusion with Core ML on Apple Silicon
PyTorch has libtorch as its purely native library. There are also Rust bindings for libtorch:
https://github.com/LaurentMazare/tch-rs
I used this in the past to make a transformer-based syntax annotator. Fully in Rust, no Python required:
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I could use some basic help
The game is in Rust, and so I have been working at using the pytorch Rust bindings, which have an A2C example, so that's what I've been going with. Example here: https://github.com/LaurentMazare/tch-rs/blob/main/examples/reinforcement-learning/a2c.rs
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Announcing Burn: New Deep Learning framework with CPU & GPU support using the newly stabilized GAT feature
Burn is different: it is built around the Backend trait which encapsulates tensor primitives. Even the reverse mode automatic differentiation is just a backend that wraps another one using the decorator pattern. The goal is to make it very easy to create optimized backends and support different devices and use cases. For now, there are only 3 backends: NdArray (https://github.com/rust-ndarray/ndarray) for a pure rust solution, Tch (https://github.com/LaurentMazare/tch-rs) for an easy access to CUDA and cuDNN optimized operations and the ADBackendDecorator making any backend differentiable. I am now refactoring the internal backend API to make it as easy as possible to plug in new ones.
tch-rs author here, it's certainly good feedback to know that you find the lack of documentation being one of the most critical point. Fwiw, we try to have a large number of examples hoping that users can re-use them, we try to document these, see for example the Neural Style Transfer tutorial. We also try to document the various layers in tch::nn, though this should certainly be more detailed. The part that is totally undocumented is tensor operations as it's automatically generated from the libtorch operation description (which sadly doesn't have any documentation attached to each operation). If there is some demand, we could certainly look at adding some mechanism in the code generation to add some manually specified documentation for each operation.
- Best way to install PyTorch 1.13 on Mac with M1
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Implementing Stable Diffusion in Rust+Torch
As an example of how to use tch crate, I've put together an implementation of Stable Diffusion in Rust. It mostly follows the lines of huggingface's Python diffusers library and can reuse the original Python weights to generate images from Rust :) Details on GitHub with more rusty robots! Comments/questions are very welcome :)
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LaurentMazare/tch-rs is an open source project licensed under Apache License 2.0 which is an OSI approved license.