wgpu-mm
burn
| wgpu-mm | burn | |
|---|---|---|
| 1 | 34 | |
| 50 | 4,845 | |
| - | - | |
| 8.7 | 8.9 | |
| about 2 months ago | 6 months ago | |
| WGSL | Rust | |
| - | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
wgpu-mm
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Chrome Ships WebGPU
This is very exciting! (I had suspected it would slip to 114)
WebGPU implementations are still pretty immature, but certainly enough to get started with. I've been implementing a Rust + WebGPU ML runtime for the past few months and have enjoyed writing WGSL.
I recently got a 250M parameter LLM running in the browser without much optimisation and it performs pretty well! (https://twitter.com/fleetwood___/status/1638469392794091520)
That said, matmuls are still pretty handicapped in the browser (especially considering the bounds checking enforced in the browser). From my benchmarking I've struggled to hit 50% of theoretical FLOPS, which is cut down to 30% when the bounds checking comes in. (Benchmarks here: https://github.com/FL33TW00D/wgpu-mm)
I look forward to accessing shader cores as they mentioned in the post.
burn
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Burn 0.10.0 Released 🔥 (Deep Learning Framework)
Release Note: https://github.com/burn-rs/burn/releases/tag/v0.10.0
- Deep Learning Framework in Rust: Burn 0.10.0 Released
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Why Rust Is the Optimal Choice for Deep Learning, and How to Start Your Journey with the Burn Deep Learning Framework
The comprehensive, open-source deep learning framework in Rust, Burn, has recently undergone significant advancements in its latest release, highlighted by the addition of The Burn Book 🔥. There has never been a better moment to embark on your deep learning journey with Rust, as this book will guide you through your initial project, providing extensive explanations and links to relevant resources.
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Candle: Torch Replacement in Rust
Burn (deep learning framework in rust) has WGPU backend (WebGPU) already. Check it out https://github.com/burn-rs/burn. It was released recently.
- Burn – A Flexible and Comprehensive Deep Learning Framework in Rust
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Announcing Burn-Wgpu: New Deep Learning Cross-Platform GPU Backend
For more details about the latest release see the release notes: https://github.com/burn-rs/burn/releases/tag/v0.8.0.
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Are there any ML crates that would compile to WASM?
Tract is the most well known ML crate in Rust, which I believe can compile to WASM - https://github.com/sonos/tract/. Burn may also be useful - https://github.com/burn-rs/burn.
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Any working wgpu compute example that would run in a browser?
We, the burn team, are working on the wgpu backend (WebGPU) for Burn deep learning framework. You can check out the current state: https://github.com/burn-rs/burn/tree/main/burn-wgpu
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I’ve fallen in love with rust so now what?
Here is the project: https://github.com/burn-rs/burn
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Is anyone doing Machine Learning in Rust?
Disclaimer, I'm the main author of Burn https://burn-rs.github.io.
What are some alternatives?
SHA256-WebGPU - Implementation of sha256 in WGSL
candle - Minimalist ML framework for Rust
stablehlo - Backward compatible ML compute opset inspired by HLO/MHLO
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
wgpu-py - Next generation GPU API for Python
tch-rs - Rust bindings for the C++ api of PyTorch.
pygfx - A python render engine running on wgpu.
Graphite - 2D raster & vector editor that melds traditional layers & tools with a modern node-based, non-destructive, procedural workflow.
tfjs - A WebGL accelerated JavaScript library for training and deploying ML models.
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference [Moved to: https://github.com/sonos/tract]
webgpu-blas - Fast matrix-matrix multiplication on web browser using WebGPU
L2 - l2 is a fast, Pytorch-style Tensor+Autograd library written in Rust