burn
swift
| burn | swift | |
|---|---|---|
| 34 | 217 | |
| 4,845 | 66,003 | |
| - | 0.5% | |
| 8.9 | 10.0 | |
| 6 months ago | 5 days ago | |
| Rust | C++ | |
| 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
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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.
swift
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Swift's native Clocks are inefficient
https://github.com/apple/swift/pull/73429
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Evolving the Go Standard Library with math/rand/v2
This algorithm produces biased result with probability 1/2^(32-bitwidth(N)). Using 64 or 128 random bits can make the bias practically undetectable. Comprehensive overview of the approach can be found here: https://github.com/apple/swift/pull/39143
- Swift: Differentiable Programming Manifesto
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Embedded Swift on the Raspberry Pi Pico
Because of C/C++ interop, and integration with CMake, you can just add Swift to a Zephyr project and it pretty much Just Works. [The docs](https://github.com/apple/swift/blob/main/docs/EmbeddedSwift/...) should mostly apply to the Zephyr SDK as well.
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A Deep Dive Into Observation: A New Way to Boost SwiftUI Performance
Fortunately, the Observation framework is part of the Swift 5.9 standard library. We can learn more information by examining its source code.
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Swift was always going to be part of the OS
They do! See https://github.com/apple/swift/blob/main/docs/LibraryEvoluti...
You can also see an example of what a different high level language integration with Swift ABI looks like here: https://github.com/dotnet/designs/blob/main/proposed/swift-i...
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Differentiable Swift
So is differentiable Swift a package for Swift or is it part of the Swift standard library? The video says go to swift.org but I can't find any info about differentiable Swift on that site.
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Beyond Backpropagation - Higher Order, Forward and Reverse-mode Automatic Differentiation for Tensorken
Swift's Differentiable Programming Manifesto. Swift has a powerful differentiable programming component, integrated with the compiler.
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Kotlin Multiplatform for Android and iOS Apps
You can do the same thing the other way around - https://github.com/apple/swift/blob/main/docs/Android.md.
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This isn’t the way to speed up Rust compile times
Codable (along with other derived conformances like Equatable, Hashable, and RawRepresentable) is indeed built in to the compiler[0], but unlike Serde, it operates during type-checking on a fully-constructed AST (with access to type information), manipulating the AST to insert code. Because it operates at a later stage of compilation and at a much higher level (with access to type information), the work necessary is significantly less.
With ongoing work for Swift macros, it may eventually be possible to rip this code out of the compiler and rewrite it as a macro, though it would need to be a semantic macro[1] rather a syntactic one, which isn't currently possible in Swift[2].
[0] https://github.com/apple/swift/blob/main/lib/Sema/DerivedCon...
What are some alternatives?
candle - Minimalist ML framework for Rust
solidity - Solidity, the Smart Contract Programming Language
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
cpp-lazy - C++11/14/17/20 library for lazy evaluation
tch-rs - Rust bindings for the C++ api of PyTorch.
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
Graphite - 2D raster & vector editor that melds traditional layers & tools with a modern node-based, non-destructive, procedural workflow.
tree-sitter - An incremental parsing system for programming tools
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference [Moved to: https://github.com/sonos/tract]
hummingbird - Hummingbird compiles trained ML models into tensor computation for faster inference.
L2 - l2 is a fast, Pytorch-style Tensor+Autograd library written in Rust
lobster - The Lobster Programming Language