This package enables you to deserialize Lua torch-serialized objects from Python.

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.

Random Jungle is an implementation of Random Forests. It is supposed to analyse high dimensional data. In genetics, it can be used for analysing big Genome Wide Association (GWA) data. Random Forests is a powerful machine learning method. Most interesting features are variable selection, missing value imputation, classifier creation, generalization error estimation and sample proximities between pairs of cases.

This package contains legacy registered functions for spaCy backwards compatibility.

This package provides a GStreamer plugin that wraps Kaldi's SingleUtteranceNnet2Decoder. It requires iVector-adapted DNN acoustic models. The iVectors are adapted to the current audio stream automatically.

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

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.

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.

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

This is a Python library that aims at making tensor learning simple and accessible. It allows performing tensor decomposition, tensor learning and tensor algebra easily. Its backend system allows seamlessly perform computation with NumPy, PyTorch, JAX, MXNet, TensorFlow or CuPy and run methodxs at scale on CPU or GPU.

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

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.

This is a package for hassle-free computation of shareable, comparable, and reproducible BLEU, chrF, and TER scores for natural language processing.

SentencePiece is an unsupervised text tokenizer and detokenizer mainly for Neural Network-based text generation systems where the vocabulary size is predetermined prior to the neural model training. SentencePiece implements subword units---e.g., byte-pair-encoding (BPE) and unigram language model---with the extension of direct training from raw sentences. SentencePiece allows us to make a purely end-to-end system that does not depend on language-specific pre- or post-processing.

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

This package provides Autograd-compatible approximations to the gamma family of functions.

This Python library provides several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

Scikit-learn provides simple and efficient tools for data mining and data analysis.

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.

Autograd can automatically differentiate native Python and NumPy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization.

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

OpenMM is a toolkit for molecular simulation. It can be used either as a stand-alone application for running simulations, or as a library you call from your own code.

The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.

This package provides common Python utilities and GitHub Actions for the Lightning suite of libraries.