Page tree
Skip to end of metadata
Go to start of metadata

You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 63 Next »

Help Us Improve the Wiki

This Wiki is owned by the LF AI Foundation Community. Contributions are always welcomed to help improve it! In the upper right of this page, select Log In to contribute. You will need a Linux Foundation ID (created at https://identity.linuxfoundation.org/) to log in. For a Confluence overview, click here.


Welcome to the LF AI Foundation wiki, where you will find information with a cross project focus. For individual projects, follow the links below.



The LF AI Foundation is a project of The Linux Foundation that supports open source innovation in artificial intelligence, machine learning, and deep learning. The LF AI Foundation was created to support numerous technical projects within this important space.

With the LF AI Foundation, members are working to create a neutral space for harmonization and acceleration of separate technical projects focused on AI, ML, and DL technologies.

For more information, please view the How to Get Involved deck.

Questions? Please email info@lfai.foundation.

Projects

Title

Project

Status

Description

Acumos AI

Graduate

Acumos is an Open Source Platform, which supports design, integration and deployment of AI models. Furthermore, Acumos supports an AI marketplace that empowers data scientists to publish adaptive AI models, while shielding them from the need to custom develop fully integrated solutions.

GitHub: https://github.com/acumos

Adlik 



INCUBATION

Adlik is an end-to-end optimizing framework for deep learning models. The goal of Adlik is to accelerate deep learning inference process both on cloud and embedded environment.

GitHub: https://github.com/Adlik

Angel



Graduate

Angel is a high-performance distributed machine learning platform based on the philosophy of Parameter Server. It is tuned for performance with big data from Tencent and has a wide range of applicability and stability, demonstrating increasing advantage in handling higher dimension model.

GitHub: https://github.com/Angel-ML/angel

EDL


INCUBATION

EDL optimizes the global utilization of the cluster running deep learning job and the waiting time of job submitters. It includes two parts: a Kubernetes controller for the elastic scheduling of distributed deep learning jobs, and a fault-tolerable deep learning framework.

GitHub: https://github.com/PaddlePaddle/edl

Horovod



INCUBATION

Horovod, a distributed training framework for TensorFlow, Keras and PyTorch, improves speed, scale and resource allocation in machine learning training activities. Uber uses Horovod for self-driving vehicles, fraud detection, and trip forecasting. It is also being used by Alibaba, Amazon and NVIDIA. Contributors to the project outside Uber include Amazon, IBM, Intel and NVIDIA.

GitHub: https://github.com/horovod/horovod

MarquezTo be updatedINCUBATION

Marquez is an open source metadata service for the collection, aggregation, and visualization of a data ecosystem’s metadata. It maintains the provenance of how datasets are consumed and produced, provides global visibility into job runtime and frequency of dataset access, centralization of dataset lifecycle management, and much more.

GitHub: https://github.com/MarquezProject

ONNX


Graduate

ONNX is an open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.

GitHub: https://github.com/onnx

Pyro


INCUBATION

Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.

GitHub: https://github.com/pyro-ppl/pyro

sparklyr



INCUBATION

sparklyr is an R package that lets you analyze data in Spark while using familiar tools in R. sparklyr supports a complete backend for dplyr, a popular tool for working with data frame objects both in memory and out of memory. You can use dplyr to translate R code into Spark SQL.

GitHub: https://github.com/sparklyr/sparklyr

Space contributors

{"mode":"list","scope":"descendants","limit":"5","showLastTime":"true","order":"update","contextEntityId":18481518}

  • No labels