cleanlab automatically finds and fixes errors in any ML dataset. This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training.

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

This package contains legacy registered functions for spaCy backwards compatibility.

This tool provides ordinary differential equation solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost.

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

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

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

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

This package provides a Python implementation of the CMA-ES algorithm and a few related numerical optimization tools.

CTranslate2 is a C++ and Python library for efficient inference with Transformer models.

The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU.

This package provides a speech recognition toolkit based on kaldi. It supports more than 20 languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish. The program works offline, even on lightweight devices. Portable per-language models are about 50Mb each, and there are much bigger and precise models available.

Vosk API provides a streaming API allowing to use it on-the-fly and bindings for different programming languages. It allows quick reconfiguration of vocabulary for better accuracy, and supports speaker identification beside simple speech recognition.

This package provides a standard API for reinforcement learning and a diverse set of reference environments (formerly Gym).

Dlib is a modern C++ toolkit containing machine learning algorithms and tools. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments.

ONNX is a format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

CTranslate2 is a C++ and Python library for efficient inference with Transformer models.

The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU.

PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data.

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.

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 generic API for dispatch to Pyro backends.

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).

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.

LIBSVM is a machine learning library for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

SpaCy is a library for advanced Natural Language Processing in Python and Cython. It comes with pretrained pipelines and currently supports tokenization and training for 70+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management.

Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends