This project 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. Angel is jointly developed by Tencent and Peking University, taking account of both high availability in industry and innovation in academia. Angel is developed with Java and Scala. It supports running on Yarn and Kubernetes. With the PS Service abstraction, it provides two modules, namely Spark on Angel and Pytorch on Angel separately, which enables the integration of the power of Spark/PyTorch and Parameter Server for distributed training. Graph Computing and deep learning frameworks support is under development and will be released in the future.
We welcome everyone interested in machine learning to contribute code, create issues or pull requests. Please refer to Angel Contribution Guide for more detail.
- Algorithm Parameter Description
- LDA
- GBDT
- KMeans
- Logistic Regression
- SVM
- Linear Regression
- Robust Regression
- Softmax Regression
- FM
- MLR
- Wide And Deep
- DeepFM
- DNN
- NFM
- PNN
- DCN
- AFM
- Mailing list: [email protected]
- Angel homepage in Linux FD: https://lists.deeplearningfoundation.org/g/angel-main
- TSC members & Committers
- Contributing to Angel
- Roadmap
- CoC
- Lele Yu, Bin Cui, Ce Zhang, Yingxia Shao. LDA*: A Robust and Large-scale Topic Modeling System. VLDB, 2017
- Jiawei Jiang, Bin Cui, Ce Zhang, Lele Yu. Heterogeneity-aware Distributed Parameter Servers. SIGMOD, 2017
- Jie Jiang, Lele Yu, Jiawei Jiang, Yuhong Liu and Bin Cui. Angel: a new large-scale machine learning system. National Science Review (NSR), 2017
- Jie Jiang, Jiawei Jiang, Bin Cui and Ce Zhang. TencentBoost: A Gradient Boosting Tree System with Parameter Server. ICDE, 2017
- Jiawei Jiang, Bin Cui, Ce Zhang and Fangcheng Fu. DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions. SIGMOD, 2018.