_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel webring send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.


python-sudospawner 0.5.2
Propagated dependencies: python-jupyterhub@3.0.0 python-notebook@6.5.7
Channel: guix-science
Location: guix-science/packages/jupyter.scm (guix-science packages jupyter)
Home page: https://jupyter.org
Licenses: Modified BSD
Build system: pyproject
Synopsis: Spawner for JupyterHub using sudo
Description:

The SudoSpawner enables JupyterHub to spawn single-user servers without being root, by spawning an intermediate process via sudo, which takes actions on behalf of the user.

python-systemdspawner 0.16
Propagated dependencies: python-jupyterhub@3.0.0 python-tornado@6.4.2
Channel: guix-science
Location: guix-science/packages/jupyter.scm (guix-science packages jupyter)
Home page: https://jupyter.org
Licenses: Modified BSD
Build system: pyproject
Synopsis: Spawn JupyterHub single-user notebook servers with systemd
Description:

The systemdspawner enables JupyterHub to spawn single-user notebook servers using systemd.

python-batchspawner 1.1.0
Propagated dependencies: python-jupyterhub@3.0.0 python-pamela@1.0.0
Channel: guix-science
Location: guix-science/packages/jupyter.scm (guix-science packages jupyter)
Home page: http://jupyter.org
Licenses: Modified BSD
Build system: pyproject
Synopsis: Add-on for Jupyterhub to spawn notebooks using batch systems
Description:

This package provides a spawner for Jupyterhub to spawn notebooks using batch resource managers.

python-jupyterhub-ldapauthenticator 1.3.2
Propagated dependencies: python-jupyterhub@3.0.0 python-jupyter-telemetry@0.1.0 python-ldap3@2.9.1 python-tornado@6.4.2 python-traitlets@5.14.1
Channel: guix-science
Location: guix-science/packages/jupyter.scm (guix-science packages jupyter)
Home page: https://github.com/yuvipanda/ldapauthenticator
Licenses: Modified BSD
Build system: pyproject
Synopsis: LDAP Authenticator for JupyterHub
Description:

LDAP Authenticator for JupyterHub

python-jupyterlab 4.3.4
Propagated dependencies: python-async-lru@2.0.4 python-httpx@0.28.1 python-importlib-metadata@8.7.0 python-importlib-resources@6.5.2 python-ipykernel@6.29.4 python-jinja2@3.1.2 python-jupyter-core@5.7.2 python-jupyter-lsp@2.3.0 python-jupyter-server@2.14.0 python-jupyterlab-server@2.27.1 python-notebook-shim@0.2.4 python-packaging@25.0 python-setuptools@80.9.0 python-tomli@2.2.1 python-tornado@6.4.2 python-traitlets@5.14.1
Channel: guix-science
Location: guix-science/packages/jupyter.scm (guix-science packages jupyter)
Home page: https://jupyter.org
Licenses: Modified BSD
Build system: pyproject
Synopsis: The JupyterLab notebook server extension
Description:

An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture.

python-jupyter-telemetry 0.1.0
Propagated dependencies: python-json-logger@2.0.7 python-jsonschema@4.23.0 python-ruamel.yaml@0.18.14 python-traitlets@5.14.1
Channel: guix-science
Location: guix-science/packages/jupyter.scm (guix-science packages jupyter)
Home page: https://jupyter.org/
Licenses: Modified BSD
Build system: pyproject
Synopsis: Jupyter telemetry library
Description:

Jupyter telemetry library

python-skorch 1.3.0
Propagated dependencies: python-numpy@1.26.4 python-pytorch@2.9.0 python-safetensors@0.4.3 python-scikit-learn@1.7.0 python-scipy@1.12.0 python-tabulate@0.9.0 python-tqdm@4.67.1
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://skorch.readthedocs.io/
Licenses: Modified BSD
Build system: pyproject
Synopsis: Scikit-learn compatible neural network library for PyTorch
Description:

This package provides a neural network library for PyTorch compatible with the scikit-learn API.

python-keopscore 2.3
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://www.kernel-operations.io/
Licenses: Expat
Build system: pyproject
Synopsis: Core package for kernel operations (KeOps)
Description:

keopscore is the KeOps meta programming engine. This python module should be used through a binder (e.g. pykeops or rkeops).

melissa 2.3.0
Dependencies: openmpi@4.1.6 zeromq@4.3.5
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://gitlab.inria.fr/melissa/melissa
Licenses: Modified BSD
Build system: cmake
Synopsis: Framework for large-scale sensitivity analysis
Description:

Melissa is a file-avoiding, adaptive, fault-tolerant and elastic framework, to run large-scale sensitivity analysis or deep-surrogate training on supercomputers. This package builds the API used when instrumenting the clients.

python-flax 0.8.0
Propagated dependencies: python-einops@0.8.1 python-jax@0.4.28 python-optax@0.1.5 python-orbax-checkpoint@0.4.5 python-msgpack@1.1.1 python-numpy@1.26.4 python-pyyaml@6.0.2 python-rich@13.7.1 python-tensorstore@0.1.67 python-typing-extensions@4.15.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://github.com/google/flax
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Neural network library for JAX designed for flexibility
Description:

Flax is a neural network library for JAX that is designed for flexibility.

python-melissa-core 2.3.0
Dependencies: coreutils-minimal@9.1
Propagated dependencies: python-cloudpickle@3.1.0 python-iterative-stats@0.1.1 python-jsonschema@4.23.0 python-mpi4py@4.1.0 python-numpy@1.26.4 python-plotext@5.2.8 python-pyzmq@27.0.1 python-rapidjson@1.10 python-requests@2.32.5 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://gitlab.inria.fr/melissa/melissa
Licenses: Modified BSD
Build system: pyproject
Synopsis: Python front-end server and launcher for Melissa
Description:

Python front-end in charge of orchestrating the execution a Melissa based study. It automatically handles large-scale scheduler interactions in OpenMPI and with common cluster schedulers (e.g. slurm or OAR).

python-ezyrb 1.3.2
Propagated dependencies: python-datasets@4.5.0 python-future@1.0.0 python-matplotlib@3.8.2 python-numpy@1.26.4 python-pytorch@2.9.0 python-scikit-learn@1.7.0 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://mathlab.github.io/EZyRB/
Licenses: Expat
Build system: pyproject
Synopsis: Easy Reduced Basis method in Python
Description:

EZyRB is a python library for the Model Order Reduction based on baricentric triangulation for the selection of the parameter points and on Proper Orthogonal Decomposition for the selection of the modes.

python-dm-haiku 0.0.13
Propagated dependencies: python-absl-py@2.3.1 python-chex@0.1.88 python-cloudpickle@3.1.0 python-dill@0.4.0 python-dm-tree@0.1.9 python-flax@0.8.0 python-jax@0.4.28 python-jaxlib@0.4.28 python-jmp@0.0.4 python-numpy@1.26.4 python-optax@0.1.5 python-tabulate@0.9.0 python-tensorflow@2.18.1 python-virtualenv@20.29.1
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://github.com/google-deepmind/dm-haiku
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Sonnet for JAX
Description:

Haiku is a simple neural network library for JAX. It is developed by some of the authors of Sonnet, a neural network library for TensorFlow.

python-imbalanced-learn 0.14.0
Propagated dependencies: python-joblib@1.5.2 python-numpy@1.26.4 python-pandas@2.2.3 python-scikit-learn@1.7.0 python-scipy@1.12.0 python-threadpoolctl@3.1.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://imbalanced-learn.org/
Licenses: Expat
Build system: pyproject
Synopsis: Toolbox for imbalanced dataset in machine learning
Description:

imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.

python-ray-cpp 2.38.0
Propagated dependencies: python-aiohttp@3.11.11 python-aiosignal@1.4.0 python-click@8.1.8 python-colorama@0.4.6 python-filelock@3.16.1 python-frozenlist@1.3.3 python-jsonschema@4.23.0 python-msgpack@1.1.1 python-numpy@1.26.4 python-packaging@25.0 python-pandas@2.2.3 python-protobuf@3.20.3 python-psutil@7.0.0 python-pyyaml@6.0.2 python-ray@2.38.0 python-requests@2.32.5 python-setproctitle@1.3.7
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://github.com/ray-project/ray
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Framework for scaling machine learning applications
Description:

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute. These are the provided Ray AI libraries:

  • Data: Scalable datasets for ML;

  • Train: Distributed training;

  • Tune: Scalable hyperparameter tuning;

  • RLlib: Scalable reinforcement learning;

  • Serve: Scalable and programmable serving.

python-optuna-integration 4.6.0
Propagated dependencies: python-optuna@4.6.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://optuna-integration.readthedocs.io/
Licenses: Expat
Build system: pyproject
Synopsis: Extended functionalities for Optuna
Description:

This package is an integration module of Optuna, an automatic Hyperparameter optimization software framework. The modules in this package provide users with extended functionalities for Optuna in combination with third-party libraries such as PyTorch, sklearn, and TensorFlow.

python-equinox 0.11.10
Propagated dependencies: python-jax@0.4.28 python-jaxtyping@0.3.3 python-typing-extensions@4.15.0 python-wadler-lindig@0.1.7
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://docs.kidger.site/equinox/
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Neural networks in JAX via callable PyTrees and filtered transformations
Description:

Equinox is a comprehensive JAX library that provides a wide range of tools and features not found in core JAX, including neural networks with PyTorch-like syntax, filtered APIs for transformations, PyTree manipulation routines, and advanced features like runtime errors.

python-ray 2.38.0
Dependencies: gcc@15.2.0 openssl@1.1.1u python-wrapper@3.11.14 jemalloc@5.3.0 zlib@1.3.1
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://github.com/ray-project/ray
Licenses: ASL 2.0
Build system: bazel
Synopsis: Framework for scaling machine learning applications
Description:

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute. These are the provided Ray AI libraries:

  • Data: Scalable datasets for ML;

  • Train: Distributed training;

  • Tune: Scalable hyperparameter tuning;

  • RLlib: Scalable reinforcement learning;

  • Serve: Scalable and programmable serving.

python-pydmd 2025.08.01
Propagated dependencies: python-h5netcdf@1.3.0 python-matplotlib@3.8.2 python-numpy@1.26.4 python-scikit-learn@1.7.0 python-scipy@1.12.0 python-typing-extensions@4.15.0 python-xarray@2023.12.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://pydmd.github.io/PyDMD
Licenses: Expat
Build system: pyproject
Synopsis: Python Dynamic Mode Decomposition
Description:

PyDMD is a Python package designed for Dynamic Mode Decomposition (DMD), a data-driven method used for analyzing and extracting spatiotemporal coherent structures from time-varying datasets. It provides a comprehensive and user-friendly interface for performing DMD analysis, making it a valuable tool for researchers, engineers, and data scientists working in various fields.

python-dargs 0.4.10
Propagated dependencies: python-typeguard@4.4.4 python-typing-extensions@4.15.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://github.com/deepmodeling/dargs
Licenses: LGPL 3+
Build system: pyproject
Synopsis: Process arguments for the deep modeling project
Description:

This is a minimum version for checking the input argument dict. It would examine argument's type, as well as keys and types of its sub-arguments. A special case called variant is also handled, where you can determine the items of a dict based the value of on one of its flag_name key.

python-pythresh 1.0.2
Propagated dependencies: python-joblib@1.5.2 python-numpy@1.26.4 python-pandas@2.2.3 python-pyod@2.0.6 python-pytorch@2.9.0 python-ruptures@1.1.10 python-scikit-learn@1.7.0 python-scikit-lego@0.9.5 python-scipy@1.12.0 python-tqdm@4.67.1 python-xgboost@1.7.6
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://pythresh.readthedocs.io/
Licenses: Modified BSD
Build system: pyproject
Synopsis: Outlier detection thresholding in Python
Description:

PyThresh is a comprehensive and scalable Python toolkit for thresholding outlier detection likelihood scores in univariate/multivariate data. It has been written to work in tandem with PyOD and has similar syntax and data structures. However, it is not limited to this single library.

PyThresh is meant to threshold likelihood scores generated by an outlier detector. It thresholds these likelihood scores and replaces the need to set a contamination level or have the user guess the amount of outliers that may exist in the dataset beforehand. These non-parametric methods were written to reduce the user's input/guess work and rather rely on statistics instead to threshold outlier likelihood scores. For thresholding to be applied correctly, the outlier detection likelihood scores must follow this rule: the higher the score, the higher the probability that it is an outlier in the dataset. All threshold functions return a binary array where inliers and outliers are represented by a 0 and 1 respectively.

PyThresh includes more than 30 thresholding algorithms. These algorithms range from using simple statistical analysis like the Z-score to more complex mathematical methods that involve graph theory and topology.

python-geomstats 2.8.0
Propagated dependencies: python-autograd@1.8.0 python-joblib@1.5.2 python-matplotlib@3.8.2 python-networkx@3.4.2 python-numpy@1.26.4 python-pandas@2.2.3 python-pytorch@2.9.0 python-scikit-learn@1.7.0 python-scipy@1.12.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://geomstats.github.io/
Licenses: Expat
Build system: pyproject
Synopsis: Geometric statistics on manifolds
Description:

Geomstats is an open-source Python package for computations, statistics, and machine learning on nonlinear manifolds. Data from many application fields are elements of manifolds. For instance, the manifold of 3D rotations SO(3) naturally appears when performing statistical learning on articulated objects like the human spine or robotics arms. Likewise, shape spaces modeling biological shapes or other natural shapes are manifolds.

python-alphafold 2.3.2
Propagated dependencies: openmm@8.3.1 python-absl-py@2.3.1 python-biopython@1.85 python-chex@0.1.88 python-dm-haiku@0.0.13 python-dm-tree@0.1.9 python-immutabledict@4.2.0 python-jax@0.4.28 python-ml-collections@1.1.0 python-pandas@2.2.3 python-pdbfixer@1.9 python-scipy@1.12.0 python-tensorflow@2.18.1
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://alphafold.ebi.ac.uk/
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Predict protein 3D structure from amino acid sequence
Description:

AlphaFold is an AI system developed by DeepMind that predicts a protein’s 3D structure from its amino acid sequence. It regularly achieves accuracy competitive with experiment.

python-datasets 4.5.0
Propagated dependencies: python-dill@0.4.0 python-filelock@3.16.1 python-fsspec@2025.9.0 python-httpx@0.28.1 python-huggingface-hub@0.31.4 python-multiprocess@0.70.18 python-numpy@1.26.4 python-packaging@25.0 python-pandas@2.2.3 python-pyarrow@22.0.0 python-pyyaml@6.0.2 python-requests@2.32.5 python-tqdm@4.67.1 python-xxhash@3.5.0
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://huggingface.co/docs/datasets/
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Datasets and manipulation tools for AI models
Description:

Datasets is a lightweight library providing access to major public datasets (image, audio, text, etc.), as well as enabling efficient data preparation for inspection and ML model evaluation and training.

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Total results: 69112