burn
rust-gpu
burn | rust-gpu | |
---|---|---|
34 | 82 | |
4,845 | 6,972 | |
- | 1.1% | |
8.9 | 7.7 | |
6 months ago | 9 days ago | |
Rust | Rust | |
Apache License 2.0 | 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.
burn
-
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
-
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.
-
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
-
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.
-
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.
-
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
-
I’ve fallen in love with rust so now what?
Here is the project: https://github.com/burn-rs/burn
-
Is anyone doing Machine Learning in Rust?
Disclaimer, I'm the main author of Burn https://burn-rs.github.io.
rust-gpu
-
Vcc – The Vulkan Clang Compiler
Sounds cool, but this requires yet another language to learn[0]. As someone who only has limited knowledge in this space, could someone tell me how comparable is the compute functionality of rust-gpu[1], where I can just write rust?
[0] https://github.com/Hugobros3/shady#language-syntax
[1] https://github.com/EmbarkStudios/rust-gpu
-
Candle: Torch Replacement in Rust
I don't do anything related to data science, but I feel like doing it in Rust would be nice.
You get operator overloading, so you can have ergonomic matrix operations that are typed also. Processing data on the CPU is fast, and crates like https://github.com/EmbarkStudios/rust-gpu make it very ergonomic to leverage the GPU.
I like this library for creating typed coordinate spaces for graphics programming (https://github.com/servo/euclid), I imagine something similar could be done to create refined types for matrices so you don't do matrix multiplication matrices of invalid sizes
-
What's the coolest Rust project you've seen that made you go, 'Wow, I didn't know Rust could do that!'?
Do you mean rust-gpu?
-
How a Nerdsnipe Led to a Fast Implementation of Game of Life
And https://github.com/EmbarkStudios/rust-gpu/tree/main/examples with the wgpu runner (here it runs the compute shader)
-
What is Rust's potential in game development?
I don't know how major they are considered, but Embark Studios is doing quite a bit of Rust in the open source space, most notably (IMO) rust-gpu and kajiya
-
[rust-gpu] How do I run/build my own shaders locally?
The examples in the rust-gpu repository are a good place to start
-
Posh: Type-Safe Graphics Programming in Rust
There's another project that's similar that's being used by an actual game company: https://github.com/EmbarkStudios/rust-gpu
They see specific advantages here that would outweigh that negative. It's not my space (I play games, but know next to nothing about graphics programming), but there's at least one argument in the other direction.
-
Introducing posh: Type-Safe Graphics Programming in Rust
Could this approach work for compute shaders (GPGPU) as well? So far, I think https://github.com/EmbarkStudios/rust-gpu is the state of the art in that area, but it adds a specific Rust compiler backend for generating SPIR-V rather than leaving that up to the driver. That seems more complicated than it needs to be... but maybe it has advantages too? Thoughts?
-
Looking for high level GPU computing crate
https://github.com/embarkstudios/rust-gpu Allows you to create shaders (kernals) in Rust.
-
With what languages are video games like League of Legends (most likely) programmed?
Also Embark Studios (formers DICE people) is doing a lot of work with Rust, all open source like Rust GPU https://github.com/EmbarkStudios/rust-gpu
What are some alternatives?
candle - Minimalist ML framework for Rust
llama.cpp - LLM inference in C/C++
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
wgpu - Cross-platform, safe, pure-rust graphics api.
tch-rs - Rust bindings for the C++ api of PyTorch.
Rust-CUDA - Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.
Graphite - 2D raster & vector editor that melds traditional layers & tools with a modern node-based, non-destructive, procedural workflow.
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference [Moved to: https://github.com/sonos/tract]
kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
L2 - l2 is a fast, Pytorch-style Tensor+Autograd library written in Rust
DiligentEngine - A modern cross-platform low-level graphics library and rendering framework