diffrax
PixiJS
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diffrax | PixiJS | |
---|---|---|
21 | 116 | |
1,230 | 42,552 | |
- | 1.3% | |
8.3 | 9.9 | |
6 days ago | 1 day ago | |
Python | TypeScript | |
Apache License 2.0 | MIT License |
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.
diffrax
- Ask HN: What side projects landed you a job?
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[P] Optimistix, nonlinear optimisation in JAX+Equinox!
Optimistix has high-level APIs for minimisation, least-squares, root-finding, and fixed-point iteration and was written to take care of these kinds of subroutines in Diffrax.
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Show HN: Optimistix: Nonlinear Optimisation in Jax+Equinox
Diffrax (https://github.com/patrick-kidger/diffrax).
Here is the GitHub: https://github.com/patrick-kidger/optimistix
The elevator pitch is Optimistix is really fast, especially to compile. It
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Scientific computing in JAX
Sure. So I've got some PyTorch benchmarks here. The main take-away so far has been that for a neural ODE, the backward pass takes about 50% longer in PyTorch, and the forward (inference) pass takes an incredible 100x longer.
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[D] JAX vs PyTorch in 2023
FWIW this worked for me. :D My full-time job is now writing JAX libraries at Google. Equinox for neural networks, Diffrax for differential equation solvers, etc.
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Returning to snake's nest after a long journey, any major advances in python for science ?
It's relatively early days yet, but JAX is in the process of developing its nascent scientific computing / scientific machine learning ecosystem. Mostly because of its strong autodifferentiation capabilities, excellent JIT compiler etc. (E.g. to show off one of my own projects, Diffrax is the library of diffeq solvers for JAX.)
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What's the best thing/library you learned this year ?
Diffrax - solving ODEs with Jax and computing it's derivatives automatically functools - love partial and lru_cache fastprogress - simpler progress bar than tqdm
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PyTorch 2.0
At least prior to this announcement: JAX was much faster than PyTorch for differentiable physics. (Better JIT compiler; reduced Python-level overhead.)
E.g for numerical ODE simulation, I've found that Diffrax (https://github.com/patrick-kidger/diffrax) is ~100 times faster than torchdiffeq on the forward pass. The backward pass is much closer, and for this Diffrax is about 1.5 times faster.
It remains to be seen how PyTorch 2.0 will compare, or course!
Right now my job is actually building out the scientific computing ecosystem in JAX, so feel free to ping me with any other questions.
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Python 3.11 is much faster than 3.8
https://github.com/patrick-kidger/diffrax
Which are neural network and differential equation libraries for JAX.
[Obligatory I-am-googler-my-opinions-do-not-represent- your-employer...]
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Ask HN: What's your favorite programmer niche?
Autodifferentiable programming!
Neural networks are the famous example of this, of course -- but this can be extended to all of scientific computing. ODE/SDE solvers, root-finding algorithms, LQP, molecular dynamics, ...
These days I'm doing all my work in JAX. (E.g. see Equinox or Diffrax: https://github.com/patrick-kidger/equinox, https://github.com/patrick-kidger/diffrax). A lot of modern work is now based around hybridising such techniques with neural networks.
I'd really encourage anyone interested to learn how JAX works under-the-hood as well. (Look up "autodidax") Lots of clever/novel ideas in its design.
PixiJS
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Release Radar • March 2024 Edition
If you're into video game dev, then PixiJS is something you need to know about. It's a HTML5 game engine that provides a lightweight 2D library across all devices. This latest update has a new package structure, custom builds, graphics API overhaul, and lots more. You can read about all these changes in the PixiJS Migration Guide. Also big congrats to PixiJS for being part of the open source community for ten years now! 😮.
- Ask HN: Tips to get started on my own server
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JavaScript Libraries That You Should Know
6. Pixi.js
- JSON Canvas – An open file format for infinite canvas data
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A Visual Interactive Guide to Bloom Filters
https://pixijs.com/ and https://gsap.com/. All of the source code for my posts can be found at https://github.com/samwho/visualisations :)
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My thought on different engines
For full web games (yeah, I come from the web, so I try to make my family proud), I will recommend PixiJS. It has great support for TypeScript and works very well with Vite. It's lighter than other game engines, so it's better for web games. But you will need to do a lot of things by yourself.
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Not only Unity...
PixiJS (MIT/TypeScript) https://github.com/pixijs/pixijs
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Rebuilding Isometric World
That approach works well for what I was trying to archive but I am planning on adding more functionality into the website. Hence in this article, let me rebuild the project using Pixi.js and it’s React binding, React Pixi.
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Ask HN: Possible to make a game engine in the browser?
https://openarena.live/
There's also a bunch of Javascript game engines: https://github.com/collections/javascript-game-engines
Of those, BabylonJS seems pretty powerful for 3D: https://www.babylonjs.com/games/
Or PixiJS for 2D: https://pixijs.com/
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Consider web technologies for game development
https://pixijs.com/ is more of a 2D rendering framework, but powerful and very fast
What are some alternatives?
deepxde - A library for scientific machine learning and physics-informed learning
Konva - Konva.js is an HTML5 Canvas JavaScript framework that extends the 2d context by enabling canvas interactivity for desktop and mobile applications.
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
Phaser - Phaser is a fun, free and fast 2D game framework for making HTML5 games for desktop and mobile web browsers, supporting Canvas and WebGL rendering. [Moved to: https://github.com/phaserjs/phaser]
flax - Flax is a neural network library for JAX that is designed for flexibility.
react-canvas - High performance <canvas> rendering for React components
juliaup - Julia installer and version multiplexer
A-Frame - :a: Web framework for building virtual reality experiences.
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Leaflet.PixiOverlay - Bring Pixi.js power to Leaflet maps
dm-haiku - JAX-based neural network library
cocos2d-html5 - Cocos2d for Web Browsers. Built using JavaScript.