Software:Apache SINGA
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| Developer(s) | Apache Software Foundation |
|---|---|
| Initial release | October 8, 2015 |
| Stable release | 3.3.0
/ June 7, 2022 |
| Written in | C++, Python, Java |
| Operating system | Linux, macOS, Windows |
| License | Apache License 2.0 |
| Website | singa |
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.
History
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.[1] It focused on distributed deep learning by partitioning the model and data onto nodes in a cluster and parallelize the training.[2][3] 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,[4] yzBigData and Shentilium are using SINGA for their applications, including healthcare[5] and finance.
| Version | Original release date | Latest version | Release date | |
|---|---|---|---|---|
| 3.3.0 | 2022-06-07 | 3.3.0 | 2022-06-07 | |
| 3.2.0 | 2021-08-15 | 3.2.0 | 2021-08-15 | |
| 3.1.0 | 2020-10-30 | 3.1.0 | 2020-10-30 | |
| 3.0.0 | 2020-04-20 | 3.0.0 | 2020-04-20 | |
| 2.0.0 | 2019-04-20 | 2.0.0 | 2019-04-20 | |
| 1.2.0 | 2018-06-06 | 1.2.0 | 2018-06-06 | |
| 1.1.0 | 2017-02-12 | 1.1.0 | 2017-02-12 | |
| 1.0.0 | 2016-09-08 | 1.0.0 | 2016-09-08 | |
| 0.3.0 | 2016-04-20 | 0.1.0 | 2016-04-20 | |
| 0.2.0 | 2016-01-14 | 0.2.0 | 2016-01-14 | |
| 0.1.0 | 2015-10-08 | 0.1.0 | 2015-10-08 | |
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Software Stack
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
Rafiki[6] is a sub module of SINGA for providing machine learning analytics service.
See also
References
- ↑ 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. doi:10.1145/3003665.3003669.
- ↑ Ooi, Beng Chin; Tan, Kian-Lee; Sheng, Wang; Wang, Wei; Cai, Qingchao; Chen, Gang; Gao, Jinyang; Luo, Zhaojing et al. (2015). "SINGA: A distributed deep learning platform". ACM Multimedia. doi:10.1145/2733373.2807410. http://www.comp.nus.edu.sg/~ooibc/singaopen-mm15.pdf. 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". ACM Multimedia. doi:10.1145/2733373.2806232. http://www.comp.nus.edu.sg/~ooibc/singa-mm15.pdf. 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". http://tech.163.com/17/0602/17/CLUL016I00098GJ5.html.
- ↑ "New app allows pre-diabetics to use photos of their meal to check if it is healthy". https://www.straitstimes.com/singapore/health/new-app-allows-pre-diabetics-to-use-photos-of-their-meal-to-check-if-it-is-healthy.
- ↑ Wang, Wei; Gao, Jinyang; Zhang, Meihui; Sheng, Wang; Chen, Gang; Khim Ng, Teck; Ooi, Beng Chin; Shao, Jie et al. (2018). "Rafiki". Proceedings of the VLDB Endowment 12 (2): 128–140. doi:10.14778/3282495.3282499. Bibcode: 2018arXiv180406087W. http://www.vldb.org/pvldb/vol12/p128-wang.pdf. Retrieved 9 January 2019.
External links
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