burn VS candle

Release Note: https://github.com/burn-rs/burn/releases/tag/v0.10.0

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.

Burn (deep learning framework in rust) has WGPU backend (WebGPU) already. Check it out https://github.com/burn-rs/burn. It was released recently.

For more details about the latest release see the release notes: https://github.com/burn-rs/burn/releases/tag/v0.8.0.

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.

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

Here is the project: https://github.com/burn-rs/burn

Disclaimer, I'm the main author of Burn https://burn-rs.github.io.

I intend to grow this into an open-source project because deep inside, this is ideally how I would like ComfyUI to be. There's still a long journey ahead for building all the custom nodes, which is especially challenging given that the majority of code for AI workflows is written in Python. However, with my hands-on experience with Candle and Burn libraries, I may be able to get pretty close!

You are correct. This project is "on the CPU", so it will not utilize your GPU for computation. If you would like to try out a Rust framework that does support GPUs, [Candle](https://github.com/huggingface/candle/tree/main) may be worth exploring

While looking into how to create text embeddings quickly and directly, we discovered a few helpful tools that allowed us to achieve our goal. Consequently, we created an easy-to-use PHP extension that can generate text embeddings. This extension lets you pick any model from Sentence Transformers on HuggingFace. It is built on the CandleML framework, which is written in Rust and is a part of the well-known HuggingFace ecosystem. The PHP extension itself is also crafted in Rust using the php-ext-rs library. This approach ensures the extension runs fast while still being easy to develop.

Candle already exists[1], and it runs pretty well. Can use both CUDA and Metal backends (or just plain-old CPU).

[1] https://github.com/huggingface/candle

Check out Candle! It's a Deep Learning framework for Rust. You can run LLMs in binaries.

Kalosm is a meta-framework for AI written in Rust using candle. Kalosm supports local quantized large language models like Llama, Mistral, Phi-1.5, and Zephyr. It also supports other quantized models like Wuerstchen, Segment Anything, and Whisper. In addition to local models, Kalosm supports remote models like GPT-4 and ada embeddings.

I have been working on a machine learning library called candle-lora for Candle. It implementes a technique called LoRA (low rank adaptation), which allows you to reduce a model's trainable parameter count by wrapping and freezing old layers.

[2] https://github.com/huggingface/candle/issues/313

We provide full local inference in browser, by using libraries from Hugging Face like transformers.js or candle for WASM inference.