dm-haiku
jax
dm-haiku | jax | |
---|---|---|
10 | 82 | |
2,806 | 28,004 | |
0.9% | 1.8% | |
7.8 | 10.0 | |
25 days ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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dm-haiku
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Maxtext: A simple, performant and scalable Jax LLM
Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
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Help with installing python packages.
I am fresh to nix os especially when it comes to using python on it how do I install packages withought using pip I need to install numpy~=1.19.5 transformers~=4.8.2 tqdm~=4.45.0 setuptools~=51.3.3 wandb>=0.11.2 einops~=0.3.0 requests~=2.25.1 fabric~=2.6.0 optax==0.0.6 git+https://github.com/deepmind/dm-haiku git+https://github.com/EleutherAI/lm-evaluation-harness/ ray[default]==1.4.1 jax~=0.2.12 Flask~=1.1.2 cloudpickle~=1.3.0 tensorflow-cpu~=2.5.0 google-cloud-storage~=1.36.2 smart_open[gcs] func_timeout ftfy fastapi uvicorn lm_dataformat which I can just do pip -r thetxtfile but idk how to do this in nix os also I would be using python3.7 so far this is what I have come up with but I know its wrong { pkgs ? import {} }: let packages = python-packages: with python-packages; [ mesh-transformer-jax/ jax==0.2.12 numpy~=1.19.5 transformers~=4.8.2 tqdm~=4.45.0 setuptools~=51.3.3 wandb>=0.11.2 einops~=0.3.0 requests~=2.25.1 fabric~=2.6.0 optax==0.0.6 #the other packages ]; pkgs.mkShell { nativeBuildInputs = [ pkgs.buildPackages.python37 ]; }
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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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[D] Current State of JAX vs Pytorch?
Just going to add that you should check out haiku if you are considering JAX: https://github.com/deepmind/dm-haiku
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PyTorch vs. TensorFlow in 2022
As a researcher in RL & ML in a big industry lab, I would say most of my colleagues are moving to JAX 0https://github.com/google/jax], which this article kind of ignores. JAX is XLA-accelerated NumPy, it's cool beyond just machine learning, but only provides low-level linear algebra abstractions. However you can put something like Haiku [https://github.com/deepmind/dm-haiku] or Flax [https://github.com/google/flax] on top of it and get what the cool kids are using :)
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[D] JAX learning resources?
- https://github.com/deepmind/dm-haiku/tree/main/examples
- Why would I want to develop yet another deep learning framework?
- Help with installing python packages
jax
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The Elements of Differentiable Programming
The dual numbers exist just as surely as the real numbers and have been used well over 100 years
https://en.m.wikipedia.org/wiki/Dual_number
Pytorch has had them for many years.
https://pytorch.org/docs/stable/generated/torch.autograd.for...
JAX implements them and uses them exactly as stated in this thread.
https://github.com/google/jax/discussions/10157#discussionco...
As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.
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Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.
Source: https://github.com/google/jax/discussions/5199#discussioncom...
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Apple releases MLX for Apple Silicon
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
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MLPerf training tests put Nvidia ahead, Intel close, and Google well behind
I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html
There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...
I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.
But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
>
Are they even comparing apples to apples to claim that they see these improvements over NumPy?
> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.
NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.
[1] https://github.com/google/jax
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Actually that never changed. The README has always had an example of differentiating through native Python control flow:
https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...
The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!
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Julia and Mojo (Modular) Mandelbrot Benchmark
For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
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Functional Programming 1
2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)
Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:
3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)
4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...
Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.
Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1
The simplest example might be {0}^* in which case
0: “” // because we use *
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Best Way to Learn JAX
Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!
- Codon: Python Compiler
What are some alternatives?
flax - Flax is a neural network library for JAX that is designed for flexibility.
Numba - NumPy aware dynamic Python compiler using LLVM
jax-resnet - Implementations and checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX (Flax).
functorch - functorch is JAX-like composable function transforms for PyTorch.
trax - Trax — Deep Learning with Clear Code and Speed
julia - The Julia Programming Language
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
elegy - A High Level API for Deep Learning in JAX
Cython - The most widely used Python to C compiler
jaxline
jax-windows-builder - A community supported Windows build for jax.