RustTokioBenchmark
burn
| RustTokioBenchmark | burn | |
|---|---|---|
| 1 | 9 | |
| 0 | 7,526 | |
| - | 6.0% | |
| 2.7 | 9.8 | |
| about 2 months ago | 4 days ago | |
| Rust | Rust | |
| MIT License | 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.
RustTokioBenchmark
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3 years of fulltime Rust game development, and why we're leaving Rust behind
> If you ever pull up a debugger and step through an async Rust/tokio codebase, you'll get a good sense for what the overhead here we're talking about is.
So I didn't quite do that, but the overhead was interesting to me anyway, and as I was unable to find existing benchmarks (surely they exist?), I instructed computer to create one for me: https://github.com/eras/RustTokioBenchmark
On this wee laptop the numbers are 532 vs 6381 cpu cycles when sending a message (one way) from one async thread to another (tokio) or one kernel thread to another (std::mpsc), when limited to one CPU. (It's limited to one CPU as rdtscp numbers are not comparable between different CPUs; I suppose pinning both threads to their own CPUs and actually measuring end-to-end delay would solve that, but this is what I have now.)
So this was eye-opening to me, as I expected tokio to be even faster! But still, it's 10x as fast as the thread-based method.. Straight up callback would still be a lot faster, of course, but it will affect the way you structure your code.
Improvements to methodology accepted via pull requests :).
burn
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3 years of fulltime Rust game development, and why we're leaving Rust behind
You can use libtorch directly via `tch-rs`, and at present I'm porting over to Burn (see https://burn.dev) which appears incredibly promising. My impression is it's in a good place, if of course not close to the ecosystem of Python/C++. At very least I've gotten my nn models training and running without too much difficulty. (I'm moving to Burn for the thread safety - their `Tensor` impl is `Sync` - libtorch doesn't have such a guarantee.)
Burn has Candle as one of its backends, which I understand is also quite popular.
- Burn: Deep Learning Framework built using Rust
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Transitioning From PyTorch to Burn
[package] name = "resnet_burn" version = "0.1.0" edition = "2021" [dependencies] burn = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11", features = ["ndarray"] } burn-import = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11" } image = { version = "0.24.7", features = ["png", "jpeg"] }
- Burn Deep Learning Framework Release 0.12.0 Improved API and PyTorch Integration
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Supercharge Web AI Model Testing: WebGPU, WebGL, and Headless Chrome
Great!
For Burn project, we have WebGPU example and I was looking into how we could add automated tests in the browser. Now it seems possible.
Here is the image classification example if you'd like to check out:
https://github.com/tracel-ai/burn/tree/main/examples/image-c...
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Burn Deep Learning Framework 0.11.0 Released: Just-in-Time Automatic Kernel Fusion & Founding Announcement
Full Release Note: https://github.com/tracel-ai/burn/releases/tag/v0.11.0
- Burn Deep Learning Framework v0.11.0 Released: Just-in-Time Kernel Fusion
- Burn – comprehensive dynamic Deep Learning Framework built using Rust
- Burn: Deep Learning Framework in Rust
What are some alternatives?
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
candle - Minimalist ML framework for Rust
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
tch-rs - Rust bindings for the C++ api of PyTorch.
rust-mlops-template - A work in progress to build out solutions in Rust for MLOPs
llama2.rs - A fast llama2 decoder in pure Rust.
albumin - Simple Hierarchical Album Generator
Enzyme - High-performance automatic differentiation of LLVM and MLIR.
corgi - A neural network, and tensor dynamic automatic differentiation implementation for Rust.
rust-ndarray - ndarray: an N-dimensional array with array views, multidimensional slicing, and efficient operations
are-we-learning-yet - How ready is Rust for Machine Learning?
cob-webber - Shelob of Cirith Ungol