are-we-learning-yet
rust-bert
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are-we-learning-yet
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This year I tried solving AoC using Rust, here are my impressions coming from Python!
Also http://arewelearningyet.com
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[D] Is Rust stable/mature enough to be used for production ML? Is making Rust-based python wrappers a good choice for performance heavy uses and internal ML dependencies in 2021?
Hey OP, you might want to check this site out: http://arewelearningyet.com
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Is rust good for mathematical computing?
Note that you can update the page (adding packages or updating descriptions) via those github issues: https://github.com/anowell/are-we-learning-yet/issues
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I wanted to share my experience of Rust as a deep learning researcher
Not sure if you’ve encountered it, but you should be aware of http://arewelearningyet.com
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Announcing neuronika 0.1.0, a deep learning framework in Rust
Just make a PR: https://github.com/anowell/are-we-learning-yet
rust-bert
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How to leverage the state-of-the-art NLP models in Rust
brew install libtorch brew link libtorch brew ls --verbose libtorch | grep dylib export LIBTORCH=$(brew --cellar pytorch)/$(brew info --json pytorch | jq -r '.[0].installed[0].version') export LD_LIBRARY_PATH=${LIBTORCH}/lib:$LD_LIBRARY_PATH git clone https://github.com/guillaume-be/rust-bert.git cd rust-bert ORT_STRATEGY=system cargo run --example sentence_embeddings
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Transformers.js
I'd like to use this transformer model in rust (because it's on the backend, because I can use data munging and it will be faster, and for other reasons). It looks like a good model! But, it doesn't compile on Apple Silicon for wierd linking issues that aren't apparent - https://github.com/guillaume-be/rust-bert/issues/338. I've spent a large part of today and yesterday attempting to find out why. The only other library that I've found for doing this kind of thing programmatically (particularly sentiment analysis) is this (https://github.com/JohnSnowLabs/spark-nlp). Some of the models look a little older, which is OK, but it does mean that I'd have to do this in another language.
Does anyone know of any sentiment analysis software that can be tuned (other than VADER - I'm looking for more along the lines of a transformer model) - like BERT, but is pretrained and can be used in Rust or Python? Otherwise I'll probably using spark-nlp and having to spin another process.
Thanks.
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Running large language models like ChatGPT on a single GPU
Give this a look: https://github.com/guillaume-be/rust-bert
If you have Pytorch configured correctly, this should "just work" for a lot of the smaller models. It won't be a 1:1 ChatGPT replacement, but you can build some pretty cool stuff with it.
> it's basically Python or bust in this space
More or less, but that doesn't have to be a bad thing. If you're on Apple Silicon, you have plenty of performance headroom to deploy Python code for this. I've gotten this library to work on systems with as little as 2gb of memory, so outside of ultra-low-end use cases, you should be fine.
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Self-hosted Whisper-based voice recognition server for open Android phones
I suspect something similar is possible with ChatGPT. Using the GPT-neo-125m model I've been able to get some really convincing (if lackluster) answers on 4 core ARM hardware and less than 2gb of memory. With enough sampling, you can get legible paragraph-length responses out in less than 10 seconds; that's pretty good for an offline program in my book.
I'm using rust-bert to serve it over a Discord bot, similar to one of their examples[0]. It's running on Oracle VCPUs right now, but with dedi hardware and ML acceleration I can imagine the field moving really quickly.
[0] https://github.com/guillaume-be/rust-bert/blob/master/exampl...
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Ask HN: What AI developer tools do you wish you'd discovered sooner?
Maybe a little played-out, but I've been having a blast with the rust-bert library this weekend: https://github.com/guillaume-be/rust-bert
With a little fanagling, you can get the GPT-Neo-1.3b model running on those free Oracle ARM VMs you can provision. I'm impressed, especially with the performance of the smallest model that uses less than a gig of memory.
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Ask HN: Has anyone made a toy that integrates ChatGPT with voice into a toy?
Nope, but it's probably possible on a smaller, hobbyist scale. I've been playing with a few GPT libraries this week (namely rust-bert[0]) and I've been really impressive with local generation results on my crappy 2 core netbook. I can get 2 sentences to generate in ~5 seconds, which is pretty good in my book.
Armed with a Pi-style SBC and your AI library of choice, I bet you could get pretty far implementing some stuff. Bonus points if you use Whisper for speech-to-text, and double brownie points if you can get an AI voice to read the generation back.
[0] https://github.com/guillaume-be/rust-bert/tree/master/exampl...
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[D] Is Rust stable/mature enough to be used for production ML? Is making Rust-based python wrappers a good choice for performance heavy uses and internal ML dependencies in 2021?
If you are using BERT models and some miscellaneous other related stuff then you should check out the rust-bert and Bert Sentence repos https://github.com/guillaume-be/rust-bert
What are some alternatives?
rust-gpu - 🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
neuronika - Tensors and dynamic neural networks in pure Rust.
speak - Talk with your machine in this minimalistic Rust crate!
wgpu - Cross-platform, safe, pure-rust graphics api.
FlexGen - Running large language models like OPT-175B/GPT-3 on a single GPU. Focusing on high-throughput generation. [Moved to: https://github.com/FMInference/FlexGen]
book - The Rust Programming Language
ggml - Tensor library for machine learning
Awesome-Rust-MachineLearning - This repository is a list of machine learning libraries written in Rust. It's a compilation of GitHub repositories, blogs, books, movies, discussions, papers, etc. 🦀
lightseq - LightSeq: A High Performance Library for Sequence Processing and Generation
Peroxide - Rust numeric library with R, MATLAB & Python syntax
tokenizers - 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production