ort
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ort | burn | |
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7 | 34 | |
555 | 4,845 | |
14.6% | - | |
9.3 | 8.9 | |
9 days ago | 5 months 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
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.
What are some alternatives?
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
candle - Minimalist ML framework for Rust
yolov8_onnx_go - YOLOv8 Inference using Go
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
onnxruntime-php - Run ONNX models in PHP
tch-rs - Rust bindings for the C++ api of PyTorch.
yolov8_onnx_javascript - YOLOv8 inference using Javascript
Graphite - 2D raster & vector editor that melds traditional layers & tools with a modern node-based, non-destructive, procedural workflow.
langchainjs - 🦜🔗 Build context-aware reasoning applications 🦜🔗
tract - Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference [Moved to: https://github.com/sonos/tract]
yolov8_onnx_julia - YOLOv8 inference using Julia
L2 - l2 is a fast, Pytorch-style Tensor+Autograd library written in Rust