mach-nix VS dm-haiku

Another way if the above doesn't work is to use mach-nix:https://github.com/DavHau/mach-nix

For python, I can highly recommend mach-nix. Makes it really easy to also keep a requirements.txt to stay compatible with non-nix-users.

The ESP32 toolchain is quite cumbersome to install - even under NixOS. The wiki tells you to create a shell.nix that creates a python virtual env at the first execution. I used DavHau's mach-nix to create esp-idf.nix and subsequently micropython-esp32.nix.

mach-nix and friends are pretty good, but I've found that the expectations of all the commonly-used data science stuff is pretty antithetical to the Nix Way. I'm not sure if it's still the case, but last I tried, Hydra wasn't building CUDA (since it's non-free), so I had to compile that and e.g. TensorFlow and PyTorch. Very painful, even on a beefy system.

I don't do much Python, but usually everything works just fine with mach-nix. I would try something like this: ``` { pkgs ? import (fetchTarball "https://github.com/nixos/nixpkgs/archive/nixpkgs-unstable.tar.gz") { } }:

There are some sifferent/new tools for adding your own Python packages these days. It's still not truly solved, but one of these new package generation tools might serve your better:

poetry2nix: https://github.com/nix-community/poetry2nix

dream2nix: https://nix-community.github.io/dream2nix/guides/getting-sta...

mach-nix: https://github.com/DavHau/mach-nix

pip2nix: https://github.com/nix-community/pip2nix

pynixify: https://github.com/cript0nauta/pynixify

The tools available to you at the time (pypi2nix and maybe python2nix, if it was a long time ago) have been abandoned in favor of the newer tools, I think chiefly poetry2nix but I'm not sure.

There's still the Nixpkgs buildPythonPackage stuff, I think, if your goal is to upstream a lib into Nixpkgs. But if you just want to build your own Python applications and vendorize the deps (e.g., for work), you might try one of the tools above, which weren't available 3+ years ago. Maybe Nixy Python users and developers can reply with some of their experiences using those tools :)

Have a look at mach-nix which is a small utility library for nix to build Python packages declaratively.

final: prev: { xonsh = prev.xonsh.overrideAttrs (old: rec { version = "0.12.4"; src = final.fetchFromGitHub { owner = "xonsh"; repo = "xonsh"; rev = version; sha256 = "0kdps0gf0767zy0fs6qn39rv4z3x7ck0qz1pzx6962593171yk8b"; }; propagatedBuildInputs = prev.xonsh.propagatedBuildInputs ++ [final.python3Packages.virtualenv]; }); python39 = prev.python39.override { self = prev.python39; packageOverrides = python_final: python_prev: { prompt-toolkit = python_prev.prompt-toolkit.overrideAttrs (old: rec { version = "3.0.29"; src = final.python3Packages.fetchPypi { pname = "prompt_toolkit"; inherit version; sha256 = "sha256-vWQPYOjOzXTw3CSXE9QzrOLdxitl7gf5bTWOCxUrbqc="; }; }); }; }; # Using mach-nix to fetch unpackaged xontrib plugins # adapted from https://github.com/NixOS/nixpkgs/issues/75786#issuecomment-873654103 mach-nix = import (builtins.fetchGit { url = "https://github.com/DavHau/mach-nix/"; ref = "refs/tags/3.4.0"; }) { pkgs = final; }; xonsh_pyenv = final.mach-nix.mkPython { requirements = '' xontrib-fzf-widgets xonsh-direnv ''; }; xonsh_with_plugins = final.xonsh.overrideAttrs (old: { propagatedBuildInputs = old.propagatedBuildInputs ++ final.xonsh_pyenv.python.pkgs.selectPkgs final.xonsh_pyenv.python.pkgs; }); }

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

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 ]; }

What's your favorite Deep Learning API for JAX - Flax, Haiku, Elegy, something else?

Just going to add that you should check out haiku if you are considering JAX: https://github.com/deepmind/dm-haiku

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 :)

- https://github.com/deepmind/dm-haiku/tree/main/examples