Workgroup:DataScience: Difference between revisions
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There have been some great examples of great work done on libraries: | There have been some great examples of great work done on libraries: | ||
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+nlp+ nlp] | * [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+nlp+ nlp] | ||
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+sklearn scikit-learn] | * [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+sklearn scikit-learn] | ||
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+tensorflow tensorflow] | * [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+tensorflow tensorflow] | ||
There has also been notable work on the data science infra : | There has also been notable work on the data science infra : | ||
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+jupyter Jupyter] | * [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+jupyter Jupyter] | ||
* [https://github.com/NixOS/nixpkgs/pull/38566 Jupyterlab package] | * [https://github.com/NixOS/nixpkgs/pull/38566 Jupyterlab package] | ||
* [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+jupyterhub Jupyterhub] | * [https://github.com/NixOS/nixpkgs/pulls?utf8=%E2%9C%93&q=is%3Apr+jupyterhub Jupyterhub] | ||
with such highlights as @aborsu's [https://github.com/aborsu/nixpkgs/blob/22ef965da38cc5e3457fe2d848b8a789cb6ad207/nixos/modules/services/development/jupyter/default.nix Jupyter kernels written in Nix]: | with such highlights as @aborsu's [https://github.com/aborsu/nixpkgs/blob/22ef965da38cc5e3457fe2d848b8a789cb6ad207/nixos/modules/services/development/jupyter/default.nix Jupyter kernels written in Nix]: | ||
{{file|./modules/datasci.nix|nix|<nowiki> | {{file|./modules/datasci.nix|nix|<nowiki> |
Revision as of 23:20, 1 June 2018
This workgroup is dedicated towards improving the state of the data science stack in Nixpkgs. This includes work on packages and modules for scientific computation, artificial intelligence and data processing, as well as data science IDEs.
There have been some great examples of great work done on libraries:
There has also been notable work on the data science infra :
with such highlights as @aborsu's Jupyter kernels written in Nix:
./modules/datasci.nix
...
python3kernel = let
env = (pkgs.python3.withPackages
(pythonPackages: with pythonPackages; [
ipykernel
pandas
scikitlearn
]));
in {
displayName = "Python 3 for machine learning";
argv = [
"$ {env.interpreter}"
"-m"
"ipykernel_launcher"
"-f"
"{connection_file}"
];
language = "python";
logo32 = "$ {env.sitePackages}/ipykernel/resources/logo-32x32.png";
logo64 = "$ {env.sitePackages}/ipykernel/resources/logo-64x64.png";
};
...
It looks like NixOS is well on its way to becoming a solid data science platform; the reproducible and language agnostic approach is a natural match to the task. But perhaps a coordinated effort be fruitful step up the game?
Lets continue the discussion here and at #nixos-data.