GPy is a Gaussian Process (GP) framework written in Python, from the Sheffield machine learning group. GPy implements a range of machine learning algorithms based on GPs.

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).

This package provides a port to Facebook's LLaMA collection of foundation language models. It requires models parameters to be downloaded independently to be able to run a LLaMA model.

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.

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.

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.

This package provides logging utilities for the SpaCy natural language processing framework.

The General Hidden Markov Model library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models (HMM) and algorithms: discrete, continuous emissions, basic training, HMM clustering, HMM mixtures.

This package provides a command line interface for Lightning AI services.

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

ML Collections is a library of Python collections designed for Machine Learning usecases.

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

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

This package provides a machine learning library of popular datasets, model architectures, and common transformations to apply python-pytorch in the audio domain.

This is a real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framework and implemented in Python.

This library is used internally as header-only library by PyTorch.

Hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models.

This is a modular Python implementation of t-Distributed Stochastic Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets.

fastText is a library for efficient learning of word representations and sentence classification.

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.

This package implements the Hopcroft-Karp algorithm, producing a maximum cardinality matching from a bipartite graph.

QNNPACK is a library for low-precision neural network inference. It contains the implementation of common neural network operators on quantized 8-bit tensors.

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

This package is a toolbox for optimization on Riemannian manifolds with support for automatic differentiation.