diffrax VS fontsource

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.

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

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.

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.

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.)

Diffrax - solving ODEs with Jax and computing it's derivatives automatically functools - love partial and lru_cache fastprogress - simpler progress bar than tqdm

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.

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...]

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.

Fontsource[0] is also an easy way to self-host variable fonts via NPM packages.

[0] https://fontsource.org/?variable=true

A few years ago, while I was still in high school, I began learning how to create websites purely for fun. One thing I found to be tedious was self-hosting fonts, with existing solutions to improve it completely abandoned. Consequently, I decided to learn a bit more about JavaScript by rewriting and improving these abandoned projects which led to the creation of Fontsource[0].

This project has undoubtedly set of a series of impactful events in my life, and I attribute many of my successes to it. I've had opportunities to network with numerous amazing engineers through it, leading to a part-time role and multiple internships. Companies that approached me for support also wanted to keep in touch! I also graduate this year and I am going with a full-time role from one of the aforementioned internships.

While I acknowledge my circumstances are extremely fortunate, I genuinely believe that having open source projects early on in your career can significantly contribute to standing out as a developer.

[0] https://fontsource.org

Tip: more privacy friendly Google alternatives are available and super easy to use: https://fontsource.org/

I switched most of my sites to use it and I’ve been quite happy so far.

No need to leak data to Google.

For weirder stuff (e.g. https://tidings.potato.horse) I use sites like dafont.com and convert fonts using Font Squirrel.

Fontsource publishes all Google Fonts fonts as NPM packages, allowing you to easily import them with modern bundlers.

I personally like using FontSource for this, they have some extra fonts beyond Google Fonts too

I stumbled upon fontsource.org the other day and I found the idea of installing fonts from npm packages appealing.

To do this with SvelteKit, you can use the Fontsource project. They host all of the Google Fonts catalogue as NPM packages.