TF_JAX_tutorials
equinox
| TF_JAX_tutorials | equinox | |
|---|---|---|
| 1 | 32 | |
| 269 | 2,073 | |
| - | - | |
| 0.0 | 8.9 | |
| almost 3 years ago | 3 days ago | |
| Jupyter Notebook | Python | |
| MIT License | Apache License 2.0 |
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TF_JAX_tutorials
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JAX Tutorials [D]
Someone already gave the links https://github.com/AakashKumarNain/TF_JAX_tutorials
equinox
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Jax and Equinox: What are they and why should I bother?
There's quite a few other libraries associated with Equinox in the JAX ecosystem:
https://github.com/patrick-kidger/equinox?tab=readme-ov-file...
I've enjoyed using Equinox and Diffrax for performing ODE simulations. To my knowledge the only other peer library with similar capabilities is the Julia DifferentialEquations.jl package.
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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?
What are some alternatives?
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
flax - Flax is a neural network library for JAX that is designed for flexibility.