history.md

History

We started to work on the parameter server framework since 2010.

1. The first generation was designed and optimized for specific algorithms, such as logistic regression and LDA, to serve the sheer size industrial machine learning tasks (hundreds billions of examples and features with 10-100TB data size) .

2. Later we tried to build a open-source general purpose framework for machine learning algorithms. The project is available at dmlc/parameter_server.

3. Given the growing demands from other projects, we created ps-lite, which provides a clean data communication API and a lightweight implementation. The implementation is based on dmlc/parameter_server, but we refactored the job launchers, file I/O and machine learning algorithms codes into different projects such as dmlc-core and wormhole.

4. From the experience we learned during developing dmlc/mxnet, we further refactored the API and implementation from v1. The main changes include

   • less library dependencies
   • more flexible user-defined callbacks, which facilitate other language bindings
   • let the users, such as the dependency engine of mxnet, manage the data consistency