16 projects
TensorFlow
TensorFlow is an open-source machine learning framework developed by Google that enables numerical computation and large-scale machine learning. It provides a flexible system for defining and executing computations involving tensors, which are multi-dimensional arrays. The framework supports deep learning and neural networks across multiple platforms and devices.
Contributors
47,170
Organizations
6,145
Software value
$198M
Kubeflow
Kubeflow is an open source machine learning platform built on Kubernetes that makes deploying and managing ML workflows on Kubernetes simple, portable and scalable. It provides end-to-end orchestration of machine learning pipelines, model training, serving, and experiment tracking.
Contributors
10,319
Organizations
2,276
Software value
$413M
PaddlePaddle
PaddlePaddle is an open-source deep learning platform developed by Baidu that provides a comprehensive suite of tools for AI model development, training, and deployment. It features an easy-to-use API, high performance distributed training capabilities, and extensive support for various deep learning applications including computer vision, natural language processing, and speech recognition.
Contributors
9,221
Organizations
478
Software value
$107M
Ray
Ray is an open-source unified framework for scaling AI and Python applications. It provides a simple, universal API for building distributed applications and includes libraries for machine learning, serving, streaming, and more. Ray enables developers to parallelize single-machine code with minimal code changes and scale applications from a laptop to a cluster.
Contributors
8,676
Organizations
1,474
Software value
$49M
PyTorch Lightning
PyTorch Lightning is a lightweight PyTorch wrapper that helps researchers and engineers train deep learning models with high performance at scale. It provides a high-level interface for organizing PyTorch code, automating complex training features like distributed training, mixed precision, and model checkpointing while removing boilerplate code.
Contributors
7,410
Organizations
1,478
Software value
$4.5M
XGBoost
XGBoost is a scalable, distributed gradient boosting library that provides parallel tree boosting for machine learning tasks. It implements machine learning algorithms under the gradient boosting framework, offering high performance, flexibility and portability across multiple programming languages and platforms.
Contributors
5,916
Organizations
829
Software value
$6.3M
Unsloth
Unsloth is an open-source project focused on optimizing and accelerating Large Language Models (LLMs) through efficient fine-tuning techniques. It provides tools and methods for faster LLM training and inference while reducing memory usage and computational requirements.
Contributors
3,683
Organizations
494
Software value
$1.4M
CatBoost
CatBoost is a high-performance, open-source gradient boosting library developed by Yandex that implements gradient boosting on decision trees. It provides fast, scalable, and accurate machine learning algorithms for classification, regression, and ranking tasks, with built-in support for categorical features.
Contributors
3,536
Organizations
343
Software value
$242M
LightGBM
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with faster training speed and higher efficiency, lower memory usage, better accuracy, parallel and GPU learning, and handling large-scale data.
Contributors
3,150
Organizations
480
Software value
$3.4M
Hugging Face Accelerate
Hugging Face Accelerate is a library that enables training and inference of machine learning models on multiple devices (CPU, GPU, TPU) with minimal code changes. It provides seamless distributed training capabilities, mixed precision support, and optimization features for PyTorch models.
Contributors
2,778
Organizations
506
Software value
$1.9M
Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Contributors
2,264
Organizations
335
Software value
$2.8M
MindSpore
MindSpore is an open-source deep learning framework that provides a unified training and inference experience across different devices and platforms. It features automatic differentiation, dynamic debugging capabilities, and hardware optimization for AI model development.
Contributors
1,726
Organizations
71
Software value
$122M
Angel
A Flexible and Powerful Parameter Server for large-scale machine learning.
Contributors
482
Organizations
55
Software value
$23M
CLAIMED
CLAIMED (Component Library for AI, Machine Learning, ETL and Data Science) is a runtime and programming language agnostic Data & AI component framework abstracting away all complexity for advanced MLOps and TrustedAI.
Contributors
335
Organizations
16
Software value
$4.6M
DLRover
DLRover is an autonomous distributed deep learning training system that provides elastic training, fault recovery, and performance optimization for large-scale deep learning models. It helps manage and scale training jobs across distributed infrastructure while handling failures and resource constraints.
Contributors
238
Organizations
34
Software value
$4.5M
Apache SystemDS
An open source ML system for the end-to-end data science lifecycle
This project hasn't been onboarded to LFX Insights.