furo
diffrax
furo | diffrax | |
---|---|---|
7 | 21 | |
2,495 | 1,266 | |
- | - | |
8.6 | 8.2 | |
1 day ago | 1 day ago | |
Sass | Python | |
MIT License | 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.
furo
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Can someone help me understand "documentation generators" and the purpose of Sphinx?
Sphinx has more and in my opinion better themes (especially the popular Furo them). I also think it's better for handling large and complex sites. It's way more extensible. That there are far more Sphinx users means that you're more likely to have community support if/when you run into issues.
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Can you select themes for rustdoc?
My company uses sphinx, in particular the furo theme: https://github.com/pradyunsg/furo. I'd like to use something like this to start documenting our Rust repositories. Is this possible on stable?
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[D] What JAX NN library to use?
On another note, what did you dislike in Sphinx ? I found it pretty easy to work with until now and there are quite nice themes, like Furo (https://github.com/pradyunsg/furo), which is actually pretty similar to your current docs theme. I used it recently for one of my projects (see https://francois-rozet.github.io/piqa/piqa.psnr.html).
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New Sphinx theme
reminds me a lot of Furo which is used by big names such as urllib3, pip, attrs, psycopg3, black
- Furo: A clean customizable documentation theme for Sphinx
- Technical documentation that just works
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Furo – A clean customizable documentation theme for Sphinx
This theme was created by one of the maintainers of pip, which is where I first saw it (https://pip.pypa.io/en/stable/). Here are some of the things I like about it:
- Well-chosen, proportionate font sizes and spacing.
- Table of contents sidebars for both the current page and the whole documentation site.
- Fully responsive: sidebars disappear in narrow windows or small screens, but can still be popped out.
- Clean color scheme with good contrast, and full support for dark mode (see screenshot at https://github.com/pradyunsg/furo/blob/main/README.md).
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.
What are some alternatives?
mkdocs-material - Documentation that simply works
deepxde - A library for scientific machine learning and physics-informed learning
sphinx - The Sphinx documentation generator
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
mkdocstrings - :blue_book: Automatic documentation from sources, for MkDocs.
flax - Flax is a neural network library for JAX that is designed for flexibility.
vscode-theme-alabaster-dark - Dark version of alabaster ported from https://github.com/tonsky/sublime-scheme-alabaster
juliaup - Julia installer and version multiplexer
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
PVEDiscordDark - A Discord-like dark theme for the Proxmox Web UI.
dm-haiku - JAX-based neural network library