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

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.


tensorflow-lite 2.16.2
Dependencies: abseil-cpp@20250814.1 cpuinfo@0.0-5.b73ae6c eigen@3.4.0 fp16@0.0-1.0a92994 flatbuffers@23.5.26 gemmlowp@0.1-2.16e8662 mesa-headers@25.2.3 neon2sse@0-1.097a5ec nsync@1.26.0 opencl-icd-loader@2024.10.24 pthreadpool@0.1-3.560c60d python@3.11.14 python-ml-dtypes@0.5.3 ruy@0-1.caa2443 re2@2022-12-01 xnnpack@0.0-4.51a0103 vulkan-headers@1.4.321.0 zlib@1.3.1
Propagated dependencies: python-numpy@1.26.4
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://www.tensorflow.org
Licenses: ASL 2.0
Build system: cmake
Synopsis: Machine learning framework
Description:

TensorFlow is a flexible platform for building and training machine learning models. This package provides the "lite" variant for mobile devices.

r-rcppml-devel 0.5.6-2.5449a5b
Propagated dependencies: r-matrix@1.7-4 r-rcpp@1.1.0
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/zdebruine/RcppML
Licenses: GPL 3+
Build system: r
Synopsis: Rcpp machine learning Library
Description:

This package provides fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.

python-brian2tools 0.3
Propagated dependencies: python-brian2@2.5.1 python-libneuroml@0.6.5 python-markdown-strings@3.3.0 python-matplotlib@3.8.2 python-pylems@0.6.9 python-setuptools@80.9.0 python-setuptools-scm@8.3.1
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/brian-team/brian2tools
Licenses: CeCILL
Build system: python
Synopsis: Tools for the Brian 2 simulator
Description:

Visualization and NeuroML import/export tools for the Brian 2 simulator.

python-threadpoolctl 3.1.0
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/joblib/threadpoolctl
Licenses: Modified BSD
Build system: pyproject
Synopsis: Python helpers for common threading libraries
Description:

Thread-pool Controls provides Python helpers to limit the number of threads used in the threadpool-backed of common native libraries used for scientific computing and data science (e.g. BLAS and OpenMP).

python-pytorch-avx 2.9.0
Dependencies: asmjit@0.0.0-2.cfc9f81 brotli@1.0.9 clog@0.0-5.b73ae6c concurrentqueue@1.0.3 cpp-httplib@0.20.0 eigen@3.4.0 flatbuffers@24.12.23 fmt@9.1.0 fp16@0.0-1.0a92994 fxdiv@0.0-1.63058ef gemmlowp@0.1-2.16e8662 gloo@0.0.0-4.54cbae0 googletest@1.12.1 googlebenchmark@1.9.1 libuv@1.44.2 miniz@pytorch-2.7.0 oneapi-dnnl@3.5.3 openblas@0.3.30 openmpi@4.1.6 openssl@3.0.8 pthreadpool@0.1-3.560c60d protobuf@3.21.9 pybind11@2.13.6 qnnpack-pytorch@pytorch-2.9.0 rdma-core@60.0 sleef@3.6.1 tensorpipe@0-0.bb1473a vulkan-headers@1.4.321.0 vulkan-loader@1.4.321.0 vulkan-memory-allocator@3.3.0 xnnpack@0.0-4.51a0103 zlib@1.3.1 zstd@1.5.6 fbgemm@1.2.0 nnpack@0.0-2.70a77f4
Propagated dependencies: cpuinfo@0.0-5.b73ae6c onnx@1.17.0 onnx-optimizer@0.3.19 python-astunparse@1.6.3 python-click@8.1.8 python-filelock@3.16.1 python-fsspec@2025.9.0 python-future@1.0.0 python-jinja2@3.1.2 python-networkx@3.4.2 python-numpy@1.26.4 python-opt-einsum@3.3.0 python-optree@0.14.0 python-packaging@25.0 python-psutil@7.0.0 python-pyyaml@6.0.2 python-requests@2.32.5 python-sympy@1.13.3 python-typing-extensions@4.15.0
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://pytorch.org/
Licenses: Modified BSD
Build system: python
Synopsis: Python library for tensor computation and deep neural networks
Description:

PyTorch is a Python package that provides two high-level features:

  • tensor computation (like NumPy) with strong GPU acceleration;

  • deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

python-pytorch 2.7.1
Dependencies: asmjit@0.0.0-2.cfc9f81 brotli@1.0.9 clog@0.0-5.b73ae6c concurrentqueue@1.0.3 cpp-httplib@0.20.0 eigen@3.4.0 flatbuffers@24.12.23 fmt@9.1.0 fp16@0.0-1.0a92994 fxdiv@0.0-1.63058ef gemmlowp@0.1-2.16e8662 gloo@0.0.0-2.81925d1 googletest@1.12.1 googlebenchmark@1.9.1 libuv@1.44.2 miniz@pytorch-2.7.0 oneapi-dnnl@3.5.3 openblas@0.3.30 openmpi@4.1.6 openssl@3.0.8 pthreadpool@0.1-3.560c60d protobuf@3.21.9 pybind11@2.13.6 qnnpack-pytorch@pytorch-2.9.0 rdma-core@60.0 sleef@3.6.1 tensorpipe@0-0.bb1473a vulkan-headers@1.4.321.0 vulkan-loader@1.4.321.0 vulkan-memory-allocator@3.3.0 xnnpack@0.0-4.51a0103 zlib@1.3.1 zstd@1.5.6
Propagated dependencies: cpuinfo@0.0-5.b73ae6c onnx@1.17.0 onnx-optimizer@0.3.19 python-astunparse@1.6.3 python-click@8.1.8 python-filelock@3.16.1 python-fsspec@2025.9.0 python-future@1.0.0 python-jinja2@3.1.2 python-networkx@3.4.2 python-numpy@1.26.4 python-opt-einsum@3.3.0 python-optree@0.14.0 python-packaging@25.0 python-psutil@7.0.0 python-pyyaml@6.0.2 python-requests@2.32.5 python-sympy@1.13.3 python-typing-extensions@4.15.0
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://pytorch.org/
Licenses: Modified BSD
Build system: python
Synopsis: Python library for tensor computation and deep neural networks
Description:

PyTorch is a Python package that provides two high-level features:

  • tensor computation (like NumPy) with strong GPU acceleration;

  • deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

nerd-dictation-sox-ydotool 0-2.03ce043
Dependencies: bash-minimal@5.2.37 nerd-dictation@0-2.03ce043
Propagated dependencies: sox@14.4.2 ydotool@1.0.4
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/ideasman42/nerd-dictation
Licenses: GPL 2+
Build system: trivial
Synopsis: Offline speech-to-text for desktop Linux
Description:

This package provides simple access speech to text for using in Linux without being tied to a desktop environment, using the vosk-api. The user configuration lets you manipulate text using Python string operations. It has zero overhead, as this relies on manual activation and there are no background processes. Dictation is accessed manually with nerd-dictation begin and nerd-dictation end commands.

gloo 0.0.0-2.81925d1
Dependencies: openssl@1.1.1u rdma-core@60.0
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/facebookincubator/gloo
Licenses: Modified BSD
Build system: cmake
Synopsis: Collective communications library
Description:

Gloo is a collective communications library. It comes with a number of collective algorithms useful for machine learning applications. These include a barrier, broadcast, and allreduce.

python-pyro-ppl 1.9.1
Propagated dependencies: python-numpy@1.26.4 python-opt-einsum@3.3.0 python-pyro-api@0.1.2 python-pytorch@2.9.0 python-tqdm@4.67.1
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://pyro.ai
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Python library for probabilistic modeling and inference
Description:

This package provides a Python library for probabilistic modeling and inference.

python-captum 0.8.0
Propagated dependencies: python-matplotlib@3.8.2 python-numpy@1.26.4 python-pytorch@2.9.0 python-tqdm@4.67.1
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://captum.ai
Licenses: Modified BSD
Build system: pyproject
Synopsis: Model interpretability for PyTorch
Description:

Captum is a model interpretability and understanding library for PyTorch. Captum contains general purpose implementations of integrated gradients, saliency maps, smoothgrad, vargrad and others for PyTorch models. It has quick integration for models built with domain-specific libraries such as torchvision, torchtext, and others.

whisper-cpp 1.8.2
Dependencies: ffmpeg@8.0 openblas@0.3.30 sdl2@2.30.8 spirv-headers@1.4.321.0 spirv-tools@1.4.321.0 vulkan-headers@1.4.321.0 vulkan-loader@1.4.321.0
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/ggml-org/whisper.cpp/
Licenses: Expat
Build system: cmake
Synopsis: OpenAI's Whisper model in C/C++
Description:

This package is a high-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model, implemented in plain C/C++ without dependencies, with

  • AVX intrinsics support for x86 architectures

  • VSX intrinsics support for POWER architectures

  • Mixed F16 / F32 precision

  • 4-bit and 5-bit integer quantization support

  • Zero memory allocations at runtime

  • Support for CPU-only inference

  • Efficient GPU support for NVIDIA

  • OpenVINO Support

  • C-style API

python-safetensors 0.4.3
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://huggingface.co/docs/safetensors
Licenses: ASL 2.0
Build system: cargo
Synopsis: Simple and safe way to store and distribute tensors
Description:

This package provides a fast (zero-copy) and safe (dedicated) format for storing tensors safely.

python-sentencepiece 0.2.1
Propagated dependencies: sentencepiece@0.2.1
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/google/sentencepiece
Licenses: ASL 2.0
Build system: pyproject
Synopsis: SentencePiece python wrapper
Description:

This package provides a Python wrapper for the SentencePiece unsupervised text tokenizer.

python-transformers 4.44.2
Propagated dependencies: python-filelock@3.16.1 python-huggingface-hub@0.31.4 python-numpy@1.26.4 python-pytorch@2.9.0 python-pyyaml@6.0.2 python-regex@2024.11.6 python-requests@2.32.5 python-safetensors@0.4.3 python-tokenizers@0.19.1 python-tqdm@4.67.1
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/huggingface/transformers
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Machine Learning for PyTorch and TensorFlow
Description:

This package provides easy download of thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

These models can be applied on:

  • Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.

  • Images, for tasks like image classification, object detection, and segmentation.

  • Audio, for tasks like speech recognition and audio classification.

Transformer models can also perform tasks on several modalities combined, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

This package provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community. At the same time, each Python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration between them.

python-hyperopt 0.2.7
Propagated dependencies: python-cloudpickle@3.1.0 python-future@1.0.0 python-py4j@0.10.9.7 python-networkx@3.4.2 python-numpy@1.26.4 python-scipy@1.12.0 python-setuptools@80.9.0 python-six@1.17.0 python-tqdm@4.67.1
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://hyperopt.github.io/hyperopt/
Licenses: Modified BSD
Build system: pyproject
Synopsis: Library for hyperparameter optimization
Description:

Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions.

python-umap-learn 0.5.9
Propagated dependencies: python-numba@0.61.0 python-numpy@1.26.4 python-pynndescent@0.5.13 python-scikit-learn@1.7.0 python-scipy@1.12.0 python-tqdm@4.67.1
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/lmcinnes/umap
Licenses: Modified BSD
Build system: pyproject
Synopsis: Uniform Manifold Approximation and Projection
Description:

Uniform Manifold Approximation and Projection is a dimension reduction technique that can be used for visualization similarly to t-SNE, but also for general non-linear dimension reduction.

python-thinc 8.3.4
Propagated dependencies: python-blis@1.2.1 python-catalogue@2.0.7 python-confection@0.1.5 python-cymem@2.0.6 python-murmurhash@1.0.10 python-numpy@2.3.1 python-packaging@25.0 python-preshed@3.0.6 python-pydantic@2.10.4 python-setuptools@80.9.0 python-srsly@2.5.1 python-wasabi@1.1.3
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/explosion/thinc
Licenses: Expat
Build system: pyproject
Synopsis: Functional take on deep learning
Description:

This package provides a functional take on deep learning, compatible with your favorite libraries.

dmlc-core 0.5-1.1334185
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/dmlc/dmlc-core
Licenses: ASL 2.0
Build system: cmake
Synopsis: Common bricks library for machine learning
Description:

DMLC-Core is the backbone library to support all DMLC projects, offers the bricks to build efficient and scalable distributed machine learning libraries.

mcl 14.137
Dependencies: perl@5.36.0
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://micans.org/mcl/
Licenses: GPL 3
Build system: gnu
Synopsis: Clustering algorithm for graphs
Description:

The MCL algorithm is short for the Markov Cluster Algorithm, a fast and scalable unsupervised cluster algorithm for graphs (also known as networks) based on simulation of (stochastic) flow in graphs.

openfst 1.8.4
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://www.openfst.org
Licenses: ASL 2.0
Build system: gnu
Synopsis: Library for weighted finite-state transducers
Description:

OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs).

python-pytorch-lightning 2.5.5
Propagated dependencies: python-arrow@1.3.0 python-beautifulsoup4@4.14.3 python-croniter@5.0.1 python-dateutils@0.6.12 python-deepdiff@8.5.0 python-fastapi@0.115.6 python-fsspec@2025.9.0 python-inquirer@3.1.3 python-jsonargparse@4.37.0 python-lightning-cloud@0.5.70 python-lightning-utilities@0.15.2 python-numpy@1.26.4 python-packaging@25.0 python-pytorch@2.9.0 python-pyyaml@6.0.2 python-torchmetrics@1.8.2 python-torchvision@0.24.0 python-tqdm@4.67.1 python-traitlets@5.14.1 python-typing-extensions@4.15.0
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://lightning.ai/
Licenses: ASL 2.0
Build system: pyproject
Synopsis: Deep learning framework to train, deploy, and ship AI products
Description:

PyTorch Lightning is just organized PyTorch; Lightning disentangles PyTorch code to decouple the science from the engineering.

nnpack 0.0-2.70a77f4
Dependencies: cpuinfo@0.0-5.b73ae6c fp16@0.0-1.0a92994 fxdiv@0.0-1.63058ef psimd@0.0-1.072586a pthreadpool@0.1-3.560c60d googletest@1.12.1
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/Maratyszcza/NNPACK
Licenses: FreeBSD
Build system: cmake
Synopsis: Acceleration package for neural network computations
Description:

NNPACK is an acceleration package for neural network computations. NNPACK aims to provide high-performance implementations of convnet layers for multi-core CPUs.

NNPACK is not intended to be directly used by machine learning researchers; instead it provides low-level performance primitives leveraged in leading deep learning frameworks, such as PyTorch, Caffe2, MXNet, tiny-dnn, Caffe, Torch, and Darknet.

onnx-optimizer 0.3.19
Dependencies: onnx@1.17.0 protobuf@3.21.9 pybind11@2.13.6
Propagated dependencies: python-numpy@1.26.4
Channel: guix
Location: gnu/packages/machine-learning.scm (gnu packages machine-learning)
Home page: https://github.com/onnx/optimizer
Licenses: Expat
Build system: pyproject
Synopsis: Library to optimize ONNX models
Description:

This package provides a C++ and Python library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.

Not all possible optimizations can be directly implemented on ONNX graphs--- some will need additional backend-specific information---but many can, and the aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.

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