|Developer(s)||Apache Software Foundation|
|Initial release||October 8, 2015|
3.3.0 / June 7, 2022
|Written in||C++, Python, Java|
|Operating system||Linux, macOS, Windows|
|License||Apache License 2.0|
Apache SINGA is an Apache top-level project for developing an open source machine learning library. It provides a flexible architecture for scalable distributed training, is extensible to run over a wide range of hardware, and has a focus on health-care applications.
The SINGA project was initiated by the DB System Group at National University of Singapore in 2014, in collaboration with the database group of Zhejiang University, in order to support complex analytics at scale, and make database systems more intelligent and autonomic. It focused on distributed deep learning by partitioning the model and data onto nodes in a cluster and parallelize the training. The prototype was accepted by Apache Incubator in March 2015, and graduated as a top-level project in October 2019. Seven versions have been released as shown in the following table. Since V1.0, SINGA is general to support traditional machine learning models such as logistic regression. Companies like NetEase, yzBigData and Shentilium are using SINGA for their applications, including healthcare and finance.
|Version||Original release date||Latest version||Release date|
|Current stable version: 3.3.0||2022-06-07||3.3.0||2022-06-07|
|Older version, yet still maintained: 3.2.0||2021-08-15||3.2.0||2021-08-15|
|Older version, yet still maintained: 3.1.0||2020-10-30||3.1.0||2020-10-30|
|Older version, yet still maintained: 3.0.0||2020-04-20||3.0.0||2020-04-20|
|Older version, yet still maintained: 2.0.0||2019-04-20||2.0.0||2019-04-20|
|Older version, yet still maintained: 1.2.0||2018-06-06||1.2.0||2018-06-06|
|Older version, yet still maintained: 1.1.0||2017-02-12||1.1.0||2017-02-12|
|Older version, yet still maintained: 1.0.0||2016-09-08||1.0.0||2016-09-08|
|Old version, no longer maintained: 0.3.0||2016-04-20||0.1.0||2016-04-20|
|Old version, no longer maintained: 0.2.0||2016-01-14||0.2.0||2016-01-14|
|Old version, no longer maintained: 0.1.0||2015-10-08||0.1.0||2015-10-08|
SINGA's software stack includes three major components, namely, core, IO and model. The following figure illustrates these components together with the hardware. The core component provides memory management and tensor operations; IO has classes for reading (and writing) data from (to) disk and network; The model component provides data structures and algorithms for machine learning models, e.g., layers for neural network models, optimizers/initializer/metric/loss for general machine learning models.
Rafiki is a sub module of SINGA for providing machine learning analytics service.
- Wei, Wang; Meihui, Zhang; Gang, Chen; H.V., Jagadish; Beng Chin, Ooi; Kian-Lee, Tan; Sheng, Wang (June 2016). "Database Meets Deep Learning: Challenges and Opportunities". SIGMOD Record. 45 (2): 17–22. arXiv:1906.08986. doi:10.1145/3003665.3003669. S2CID 6526411.
- Ooi, Beng Chin; Tan, Kian-Lee; Sheng, Wang; Wang, Wei; Cai, Qingchao; Chen, Gang; Gao, Jinyang; Luo, Zhaojing; Tung, Anthony K. H.; Wang, Yuan; Xie, Zhongle; Zhang, Meihui; Zheng, Kaiping (2015). "SINGA: A distributed deep learning platform" (PDF). ACM Multimedia. doi:10.1145/2733373.2807410. S2CID 1840240. Retrieved 8 September 2016.
- Wei, Wang; Chen, Gang; Anh Dinh, Tien Tuan; Gao, Jinyang; Ooi, Beng Chin; Tan, Kian-Lee; Sheng, Wang (2015). "SINGA: putting deep learning in the hands of multimedia users" (PDF). ACM Multimedia. doi:10.1145/2733373.2806232. S2CID 7169465. Retrieved 8 September 2016.
- 网易. "网易携手Apache SINGA角逐人工智能新战场_网易科技". tech.163.com. Retrieved 2017-06-03.
- "New app allows pre-diabetics to use photos of their meal to check if it is healthy". www.straitstimes.com. Retrieved 6 April 2019.
- Wang, Wei; Gao, Jinyang; Zhang, Meihui; Sheng, Wang; Chen, Gang; Khim Ng, Teck; Ooi, Beng Chin; Shao, Jie; Reyad, Moaz (2018). "Rafiki" (PDF). Proceedings of the VLDB Endowment. 12 (2): 128–140. arXiv:1804.06087. Bibcode:2018arXiv180406087W. doi:10.14778/3282495.3282499. S2CID 4898729. Retrieved 9 January 2019.