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What happens if I push all of nixpkgs
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2.1 kB · 112 lines
Nix
at master
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# build-system setuptools,
# dependencies gymnasium, numpy, packaging, typer, typing-extensions,
# optional-dependencies pyarrow, jax, google-cloud-storage, tqdm, h5py, huggingface-hub, mktestdocs, pytest, scikit-image,
# tests jaxlib, pytestCheckHook,}:
buildPythonPackage rec { pname = "minari"; version = "0.5.3"; pyproject = true;
src = fetchFromGitHub { owner = "Farama-Foundation"; repo = "Minari"; tag = "v${version}"; hash = "sha256-LvJwp2dZdGPazJPWQtrk+v7zaPjOlomBu5j9avVdCcA="; };
build-system = [ setuptools ];
dependencies = [ gymnasium numpy packaging typer typing-extensions ];
optional-dependencies = { arrow = [ pyarrow ]; create = [ jax ]; gcs = [ google-cloud-storage tqdm ]; hdf5 = [ h5py ]; hf = [ huggingface-hub ]; integrations = [ # agilerl # d3rlpy ]; testing = [ # gymnasium-robotics mktestdocs pytest scikit-image ]; };
pythonImportsCheck = [ "minari" ];
nativeCheckInputs = [ jaxlib pytestCheckHook ] ++ lib.flatten (lib.attrValues optional-dependencies);
disabledTests = [ # Require internet access "test_download_namespace_dataset" "test_download_namespace_metadata" "test_markdown"
# Attempts at installing minari using pip (impossible in the sandbox) "test_readme" ];
disabledTestPaths = [ # Require internet access "tests/dataset/test_dataset_download.py" "tests/test_cli.py" ];
meta = { description = "Standard format for offline reinforcement learning datasets, with popular reference datasets and related utilities"; homepage = "https://github.com/Farama-Foundation/Minari"; changelog = "https://github.com/Farama-Foundation/Minari/releases/tag/v${version}"; license = with lib.licenses; [ asl20 mit ]; maintainers = with lib.maintainers; [ GaetanLepage ]; mainProgram = "minari"; };}