diffrax
juliaup
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diffrax | juliaup | |
---|---|---|
21 | 10 | |
1,230 | 889 | |
- | 3.6% | |
8.3 | 9.2 | |
3 days ago | 1 day ago | |
Python | Rust | |
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.
juliaup
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How to tell Quarto where the julia-1.8 kernel is?
There is juliaup, available in the Microsoft store, for managing julia versions: https://github.com/JuliaLang/juliaup
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Is it better to install Julia with its own executable or to install it in an Anaconda environment?
Better use the official binaries from https://julialang.org/downloads/. For Windows, best use the Julia app in the MS Store (https://github.com/JuliaLang/juliaup ).
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Neovim can't find executable path for program
Thanks - fwiw, I currently use juliaup, which basically does the same thing. Unfortunately, the problem isn't that Julia isn't on the path (it is), it's that Neovim's exepath can't find it, for some reason. :)
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Julia 1.8 released
But it’s considered prerelease for Mac and Linux according to its git repository. So presumably it won’t become the default until it’s not experimental any more.
- appropriate way to run two versions of Julia on linux
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Créer simplement un cluster k8s dans PhoenixNAP avec Rancher en quelques clics …
GitHub - JuliaLang/juliaup: Julia installer and version multiplexer GitHub - JuliaPluto/PlutoUI.jl GitHub - fonsp/Pluto.jl: 🎈 Simple reactive notebooks for Julia
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I don't want to abandon Rust for Julia
Use the right tool for the job! I would never write a system utility or OS or user application or compiler in Julia, for example. juliaup is a Julia utility being written in Rust for this exact reason!
- Small Neural networks in Julia 5x faster than PyTorch
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What's the standard procedure for package requests?
And yes, I know about jill.py and have considered using it as well. It seems fairly straightforwad to use. A similar project that I found interesting is juliaup. Still, I would definitely prefer just using zypper instead of relying on third party tools. That's why I figured I might as well ask on here.
- Get first n element from an array
What are some alternatives?
deepxde - A library for scientific machine learning and physics-informed learning
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
jill.py - A cross-platform installer for the Julia programming language
flax - Flax is a neural network library for JAX that is designed for flexibility.
vectorflow
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
jill - Command line installer of the Julia Language.
dm-haiku - JAX-based neural network library
Pluto.jl - 🎈 Simple reactive notebooks for Julia
n - Node version management