ort
rayon
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ort | rayon | |
---|---|---|
7 | 67 | |
555 | 10,242 | |
14.6% | 2.9% | |
9.3 | 9.0 | |
9 days ago | 5 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.
ort
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AI Inference now available in Supabase Edge Functions
To solve this, we built a native extension in Edge Runtime that enables using ONNX runtime via the Rust interface. This was made possible thanks to an excellent Rust wrapper called Ort:
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AI Inference Now Available in Supabase Edge Functions
hey hn, supabase ceo here
As the post points out, this comes in 2 parts:
1. Embeddings models for RAG workloads (specifically pgvector). Available today.
2. Large Language Models for GenAI workloads. This will be progressively rolled out as we get our hands on more GPUs.
We've always had a focus on architectures that can run anywhere (especially important for local dev and self-hosting). In that light, we've found that the Ollama[0] tooling is really unbeatable. I heard one of our engineers explain it like "docker for models" which I think is apt.
To support models that work best with GPUs, we're running them with Fly GPUs - pretty much this: https://fly.io/blog/scaling-llm-ollama (and then we stitch a native API around it). The plan is that you will be able to "BYO" model server and point the Edge Runtime towards it using simple env vars / config.
We've also made improvements for CPU models. We built a native extension in Edge Runtime that enables using ONNX runtime via the Rust interface. This was made possible thanks to an excellent Rust wrapper, Ort[1]. We have the models stored on disk, so there is no downloading, cold-boot, etc.
The thing I most like about this set up is that you can now use Edge Functions like background workers for your Postgres database, offloading heavy compute for generating embeddings. For example, you can trigger the worker when a user inserts some text, and then the worker will asynchronously create the embedding and store it back into your database.
I'll be around if there are any questions.
[0] ollama.com
[1] Ort: https://github.com/pykeio/ort
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Moving from Typescript and Langchain to Rust and Loops
In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.
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How to create YOLOv8-based object detection web service using Python, Julia, Node.js, JavaScript, Go and Rust
ort - ONNX runtime library.
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Do you use Rust in your professional career?
Our main model in Rust is a deep neural network, using ONNX via the ort rust bindings. The application is some particular applications of process automation.
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onnxruntime
You could try ort https://github.com/pykeio/ort It looks like it's in active development and supports GPU inference
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Deep Learning in Rust: Burn 0.4.0 released and plans for 2023
I would't try to distribute your ml models with the typical frameworks, especially not with python. Have you looked in to ONNX?For example: https://github.com/pykeio/ort
rayon
- Rayon: Data-race free parallelization of sequential computations in Rust
- Too Dangerous for C++
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Which application/problem would you choose for presenting Rust to newcomers in 1h30min?
Do some operations with .iter() then later use rayon to parallelize. So you can show how easy is to add a dependency and how easy is to parallelize.
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What Are The Rust Crates You Use In Almost Every Project That They Are Practically An Extension of The Standard Library?
rayon: Async CPU runtime for parallelism.
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Moving from Typescript and Langchain to Rust and Loops
In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.
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AreWeMegafactoryYet? I just breached simulating 1M buildings @ 60 fps (If I'm not recording, Ryzen 7 1700X 8 Core)
With a lot of rayon, blood, sweat and tears I finally managed to simulate a million buildings at 60fps :) Feel free to AMA, game is Combine And Conquer
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The Rust I Wanted Had No Future
(see https://github.com/rayon-rs/rayon/tree/master/src/iter/plumbing)
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Parallel event iterator?
I did some very basic testing with this crate : https://crates.io/crates/rayon and it seems to work :
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General Recommendations: Should I Use Tree-sitter as the AST for the LSP I am developing?
Sequentially, generating tree-sitter AST for each file and querying for the links of each file takes around 2.3 seconds. However, I randomly remembered this crate rayon, and I decided to test it. It ended up improving the performance (just by changing 2 lines of code) to 200-300ms by parallelizing the iterators and tree-sitter queries. MAJOR.
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python to rust migration
Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.
What are some alternatives?
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
crossbeam - Tools for concurrent programming in Rust
yolov8_onnx_go - YOLOv8 Inference using Go
tokio - A runtime for writing reliable asynchronous applications with Rust. Provides I/O, networking, scheduling, timers, ...
onnxruntime-php - Run ONNX models in PHP
RxRust - The Reactive Extensions for the Rust Programming Language
yolov8_onnx_javascript - YOLOv8 inference using Javascript
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
langchainjs - 🦜🔗 Build context-aware reasoning applications 🦜🔗
tokio-rayon - Mix async code with CPU-heavy thread pools using Tokio + Rayon
yolov8_onnx_julia - YOLOv8 inference using Julia
sqlx - 🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.