extending-jax
elegy
| extending-jax | elegy | |
|---|---|---|
| 2 | 5 | |
| 353 | 463 | |
| - | 0.0% | |
| 3.5 | 0.0 | |
| 6 months ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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extending-jax
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[D] Should We Be Using JAX in 2022?
You can check out this or this for more info. I think it is safe to assume that it is less stable than PyTorch - some other commenters have spoken about running into trouble with XLA in certain corner cases, but I have not experienced this so I can't speak to it.
- Extending JAX with custom C++ and CUDA code
elegy
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is Elegy framework for JAX abandoned?
I wonder if https://github.com/poets-ai/elegy is still an active project or dead because it hasn't had a commit in almost a year. Would be too bad if abandoned because I like it.
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[D] Any less-boilerplate framework for Jax/Flax/Haiku?
Elegy might be worth a look.
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PyTorch vs. TensorFlow in Academic Papers
JAX is really cool, but still somewhat immature. I would love to see it taking more ground and improving wrt e.g. integration with tensorboard and getting all the goodies we have in tensorflow. If you are looking for a higher level framework, I would recommend elegy [0] which is very close to the keras API.
[0] https://github.com/poets-ai/elegy
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[D] Should We Be Using JAX in 2022?
What's your favorite Deep Learning API for JAX - Flax, Haiku, Elegy, something else?
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Best sources to learn JAX?
For a Module library checkout Flax or Haiku, they are well maintained. For a Trainer interface like Keras / Pytorch Lightning checkout Elegy: https://github.com/poets-ai/elegy
What are some alternatives?
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
dm-haiku - JAX-based neural network library
mpi4jax - Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
jax-resnet - Implementations and checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX (Flax).
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
flax - Flax is a neural network library for JAX that is designed for flexibility.
trax - Trax — Deep Learning with Clear Code and Speed
scenic - Scenic: A Jax Library for Computer Vision Research and Beyond
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
runtime - A performant and modular runtime for TensorFlow