ASF Announces Singapore's Apache SINGA Deep Learning Tool as a Top-Level Project

Open Source machine learning library in use at Citigroup, NetEase, and Singapore General Hospital, among others.

Wakefield, MA -4 November 2019- The Apache Software Foundation (ASF), the all-volunteer developers, stewards, and incubators of more than 350 Open Source projects and initiatives, announced today Apache® SINGA™ as a Top-Level Project (TLP). Apache SINGA is an Open Source distributed, scalable machine learning library. The project was originally developed in 2014 at the National University of Singapore, and was submitted to the Apache Incubator in March 2015.

Wei Wang"We are excited that SINGA has graduated from the Apache Incubator," said Wei Wang, Vice President of Apache SINGA and Assistant Professor at the National University of Singapore. "The SINGA project started at the National University of Singapore, in collaboration with Zhejiang University, focusing on scalable distributed deep learning. In addition to scalability, during the incubation process, built multiple versions to improve the project's usability and efficiency. Incubating SINGA at the ASF brought opportunities to collaborate, grew our community, standardize the development process, and more."

Apache SINGA is a distributed machine learning library that facilitates the training of large-scale machine learning (especially deep learning) models over a cluster of machines. Various optimizations on efficiency, memory, communication and synchronization are implemented to speed it up and scale it out. Currently, the Apache SINGA project is working on SINGA-lite for deep learning on edge devices with 5G, and SINGA-easy for making AI usable by domain experts (without deep AI background).

Apache SINGA is in use at organizations such as Carnegie Technologies, CBRE, Citigroup, JurongHealth Hospital, National University of Singapore, National University Hospital, NetEase, Noblis, Shentilium Technologies, Singapore General Hospital, Tan Tock Seng Hospital, YZBigData, and others. Apache SINGA is used across applications in banking, education, finance, healthcare, real estate, software development, and other categories.

The NUS team, led by Professor Ooi Beng Chin (in orange) from the School of Computing, started working on Singa in 2014. PHOTO-National University of Singapore"So glad to see the first Apache project focusing on distributed deep learning become a Top-Level Project," said Beng Chin Ooi, Distinguished Professor of National University of Singapore who initialized the SINGA project, and a member of the Apache SINGA Project Management Committee. "It is essential to scale deep learning via distributed computing as the deep learning models are typically large and trained over big datasets, which may take hundreds of days using a single GPU."

"I am glad to witness the graduation of Apache SINGA as a TLP," said Gang Chen, Professor and Dean of Zhejiang University and Dean of ZJU-NetEase research lab. "We will continue to contribute to the development and use it for industry applications such as smart fabric printing, e-commerce recommendation and smart cities."

"Apache SINGA has a flexible distributed training framework," said Sheng Wang, Research Scientist at the DAMO Academy of Alibaba and a member of the Apache SINGA Project Management Committee. "SINGA can implement multiple popular distributed training strategies, including synchronous and asynchronous training. It achieved excellent scalability in comparison with other deep learning platforms."

"Apache SINGA has been applied to support many different healthcare applications at MZH Technologies," said Zhongle Xie, CTO of Hangzhou MZH Technologies and a member of the Apache SINGA Project Management Committee. "The performance of disease diagnoses based on X-Ray images could even pass the radiologists. We also built a food recognition app using SINGA to help patients monitor their food intake and log the nutrition automatically."

"We are working with cardiologists in Fuwai Hospital, Beijing, China, to develop a machine learning/deep learning cardiovascular disease prediction model, using cardiovascular risk factors and other indirect factors such as diet and exercise," said MZH Technologies co-founder and Beijing Institute of Technology Professor, Meihui Zhang. "We are also using Apache SINGA for data cleaning and integration."

"Besides scalability, SINGA team is continuously improving the library by adding new features to make it easier to use," said Moaz Reyad, Postdoctoral Researcher at Université Grenoble Alpes, and a member of the Apache SINGA Project Management Committee. "For example, SINGA has a sub-component called SINGA-auto (original name is Rafiki), which provides AutoML features like automatic hyper-parameter tuning."

"We would like to thank all our mentors for guiding the project and all contributors for helping on this project from incubation to graduation," added Wang. "Deep learning and other AI technologies are changing the world from many aspects. We welcome newcomers to join our community to make contributions to this exciting field!"

Availability and Oversight

Apache SINGA software is released under the Apache License v2.0 and is overseen by a self-selected team of active contributors to the project. A Project Management Committee (PMC) guides the Project's day-to-day operations, including community development and product releases. For downloads, documentation, and ways to become involved with Apache SINGA, visit http://singa.apache.org/ and https://twitter.com/ApacheSINGA

About the Apache Incubator

The Apache Incubator is the entry path for projects and codebases wishing to become part of the efforts at The Apache Software Foundation. All code donations from external organizations and existing external projects enter the ASF through the Incubator to: 1) ensure all donations are in accordance with the ASF legal standards; and 2) develop new communities that adhere to our guiding principles. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. For more information, visit http://incubator.apache.org/

About The Apache Software Foundation (ASF)

Established in 1999, the all-volunteer Foundation oversees more than 350 leading Open Source projects, including Apache HTTP Server --the world's most popular Web server software. Through the ASF's meritocratic process known as "The Apache Way," more than 730 individual Members and 7,000 Committers across six continents successfully collaborate to develop freely available enterprise-grade software, benefiting millions of users worldwide: thousands of software solutions are distributed under the Apache License; and the community actively participates in ASF mailing lists, mentoring initiatives, and ApacheCon, the Foundation's official user conference, trainings, and expo. The ASF is a US 501(c)(3) charitable organization, funded by individual donations and corporate sponsors including Aetna, Alibaba Cloud Computing, Anonymous, ARM, Baidu, Bloomberg, Budget Direct, Capital One, Cerner, Cloudera, Comcast, Facebook, Google, Handshake, Hortonworks, Huawei, IBM, Indeed, Inspur, Leaseweb, Microsoft, ODPi, Pineapple Fund, Pivotal, Private Internet Access, Red Hat, Target, Tencent, Union Investment, Workday, and Verizon Media. For more information, visit http://apache.org/ and https://twitter.com/TheASF©

The Apache Software Foundation. "Apache", "SINGA", "Apache SINGA", and "ApacheCon" are registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. All other brands and trademarks are the property of their respective owners.

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