ort
app
ort | app | |
---|---|---|
7 | 29 | |
629 | 368 | |
18.8% | 11.1% | |
9.4 | 9.9 | |
6 days ago | 2 days ago | |
Rust | TypeScript | |
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
app
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Creating an OG image using React and Netlify Edge Functions
nickytonline posted on Mar 13, 2024
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Form and Function: How I Lost My Submit Button & Got It Back
fix: now workspace settings and new page have a fixed footer for CTAs #2982
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OpenSauced: Nurturing Open Source Collaboration and Growth
View on GitHub
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Yet Another Newsletter LOL: Madden 2025
chore: removed prettier intergrations from eslint #2635
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Challenging the Skeptics: Unveiling the Undeniable Goodness of Tailwind CSS
fix: add missing skeleton loader for insights panel #2524
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What is Vercel's AI tool, V0.dev and how do you use it?
With these questions, I decided to do my experiments based on two projects that I am involved with: the first is OpenSauced, and the second is okuyun.org.
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Migrating from Jest to Vitest for your React Application
At OpenSauced, we're using Next.js to build out the main application.
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HTML Data Attributes: One of the Original State Management Libraries
feat: add most used languages graph #2158
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Infer Types to Avoid Explicit Types
feat: Svelte added to interests dropdown list #2168
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Our Hacktoberfest Success Story
We believe in making the OpenSauced experience great for everyone who uses it and all our contributors. We welcome continued contributions, including issues, bug reports, pull requests, blog posts written about your OpenSauced experience, and your feedback. If you want to learn more about how to use OpenSauced to see how your projects are doing, check out our Introduction to OpenSauced or get started today.
What are some alternatives?
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
langchainjs - π¦π Build context-aware reasoning applications π¦π
yolov8_onnx_go - YOLOv8 Inference using Go
take-action - This is an action to assign yourself to an issue for a repo you are not a contributor to.
onnxruntime-php - Run ONNX models in PHP
ggplot2 - An implementation of the Grammar of Graphics in R
yolov8_onnx_javascript - YOLOv8 inference using Javascript
nyc - the Istanbul command line interface
hot - πThe site that recommends the hottest projects on GitHub.
yolov8_onnx_julia - YOLOv8 inference using Julia
nissuer - A GitHub Action for tried and tired maintainers.