dm-haiku
Pytorch
dm-haiku | Pytorch | |
---|---|---|
10 | 340 | |
2,806 | 78,016 | |
0.9% | 1.4% | |
7.8 | 10.0 | |
27 days ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | BSD 1-Clause License |
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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
Pytorch
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Clasificador de imágenes con una red neuronal convolucional (CNN)
PyTorch (https://pytorch.org/)
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AI enthusiasm #9 - A multilingual chatbot📣🈸
torch is a package to manage tensors and dynamic neural networks in python (GitHub)
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Einsum in 40 Lines of Python
PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832
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Library for Machine learning and quantum computing
TensorFlow
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My Favorite DevTools to Build AI/ML Applications!
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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penzai: JAX research toolkit for building, editing, and visualizing neural nets
> does PyTorch have a similar concept
of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...
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Tinygrad: Hacked 4090 driver to enable P2P
fyi should work on most 40xx[1]
[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...
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The Elements of Differentiable Programming
Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...
Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....
> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].
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Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In PyTorch with @, dot() or matmul():
What are some alternatives?
flax - Flax is a neural network library for JAX that is designed for flexibility.
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
jax-resnet - Implementations and checkpoints for ResNet, Wide ResNet, ResNeXt, ResNet-D, and ResNeSt in JAX (Flax).
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
trax - Trax — Deep Learning with Clear Code and Speed
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
equinox - Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
elegy - A High Level API for Deep Learning in JAX
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more