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equinox
ex-mode | equinox | |
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1 | 31 | |
169 | 1,827 | |
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10.0 | 9.2 | |
about 5 years ago | about 22 hours ago | |
CoffeeScript | 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.
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Ask HN: What side projects landed you a job?
Some years ago I was on a shitty job - not technically, but the company turned out to be inhumane - at a Ruby shop, and on the side I was toying with mini_racer and I just upgraded to some macOS beta where it failed to build. A shitty +1-1 hack† for a compiler flag later and it was back flying.
A month later I received a cold email from a CTO to chat a bit about that PR, turns out they were using mini_racer heavily and forked it for their own purpose, and also created PyMiniRacer for the Python side of things. Next thing I know I got hired. Two years later the company got acquired.
Of course conditionally adding a compiler flag wasn't what got me hired per se, it only got my profile noticed. Probably side projects such as porting go by example to Ruby by implementing a ~1:1 CSP channel API[1], an Electron desktop client for Mattermost basically on a dare[2], ex mode for the Atom editor so that I could have that frackin' `:w`[3], leveraging Blocks to bolt on object-oriented-ness onto C because "closures are a poor man's object"[4], or reverse-engineering the Xbox One USB gamepad and writing a kext to turn it into a HID device on macOS from scratch on a lonely 7+h train ride with passengers judgementally staring at me sideways[4] probably contributed to it a bit.
My takeaway: luck is when preparation meets opportunity; but don't to side projects to get hired, because if you don't get hired then that time is lost. Rather, of all things, scratch your itch, have fun, embrace whatever quirkiness you fancy; no one can take that away from you.
[0]: https://github.com/rubyjs/mini_racer/commit/2086db1bbf2b5de4...
[1]: https://github.com/lloeki/normandy
[2]: https://github.com/lloeki/matterfront
[3]: https://github.com/lloeki/ex-mode
[4]: https://github.com/lloeki/cblocks-clobj/blob/master/main.c
[5]: https://github.com/lloeki/xbox_one_controller
equinox
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Ask HN: What side projects landed you a job?
I wrote a JAX-based neural network library (Equinox [1]) and numerical differential equation solving library (Diffrax [2]).
At the time I was just exploring some new research ideas in numerics -- and frankly, procrastinating from writing up my PhD thesis!
But then one of the teams at Google starting using them, so they offered me a job to keep developing them for their needs. Plus I'd get to work in biotech, which was a big interest of mine. This was a clear dream job offer, so I accepted.
Since then both have grown steadily in popularity (~2.6k GitHub stars) and now see pretty widespread use! I've since started writing several other JAX libraries and we now have a bit of an ecosystem going.
[1] https://github.com/patrick-kidger/equinox
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[P] Optimistix, nonlinear optimisation in JAX+Equinox!
The elevator pitch is Optimistix is really fast, especially to compile. It plays nicely with Optax for first-order gradient-based methods, and takes a lot of design inspiration from Equinox, representing the state of all the solvers as standard JAX PyTrees.
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
If you like PyTorch then you might like Equinox, by the way. (https://github.com/patrick-kidger/equinox ; 1.4k GitHub stars now!)
- Equinox: Elegant easy-to-use neural networks in Jax
- Show HN: Equinox (1.3k stars), a JAX library for neural networks and sciML
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Pytrees
You're thinking of `jax.closure_convert`. :)
(Although technically that works by tracing and extracting all constants from the jaxpr, rather than introspecting the function's closure cells -- it sounds like your trick is the latter.)
When you discuss dynamic allocation, I'm guessing you're mainly referring to not being able to backprop through `jax.lax.while_loop`. If so, you might find `equinox.internal.while_loop` interesting, which is an unbounded while loop that you can backprop through! The secret sauce is to use a treeverse-style checkpointing scheme.
https://github.com/patrick-kidger/equinox/blob/f95a8ba13fb35...
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Writing Python like it’s Rust
I'm a big fan of using ABCs to declare interfaces -- so much so that I have an improved abc.ABCMeta that also handles abstract instance variables and abstract class variables: https://github.com/patrick-kidger/equinox/blob/main/equinox/_better_abstract.py
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[D] JAX vs PyTorch in 2023
For the daily research, I use Equinox (https://github.com/patrick-kidger/equinox) as a DL librarry in JAX.
- [Machinelearning] [D] État actuel de JAX vs Pytorch?
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Training Deep Networks with Data Parallelism in Jax
It sounds like you're concerned about how downstream libraries tend to wrap JAX transformations to handle their own thing? (E.g. `haiku.grad`.)
If so, then allow me to make my usual advert here for Equinox:
https://github.com/patrick-kidger/equinox
This actually works with JAX's native transformations. (There's no `equinox.vmap` for example.)
On higher-order functions more generally, Equinox offers a way to control these quite carefully, by making ubiquitous use of callables that are also pytrees. E.g. a neural network is both a callable in that it has a forward pass, and a pytree in that it records its parameters in its tree structure.
What are some alternatives?
edgedns - A high performance DNS cache designed for Content Delivery Networks
flax - Flax is a neural network library for JAX that is designed for flexibility.
Pion WebRTC - Pure Go implementation of the WebRTC API
dm-haiku - JAX-based neural network library
normandy - Channels for CSP style Ruby
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
stepmania - Advanced rhythm game for Windows, Linux and OS X. Designed for both home and arcade use.
treex - A Pytree Module system for Deep Learning in JAX
extending-jax - Extending JAX with custom C++ and CUDA code
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
elegy - A High Level API for Deep Learning in JAX
TF_JAX_tutorials - All about the fundamental blocks of TF and JAX!