Jeffery-Song/ps-kafka

基于kafka的ps-lite

A light and efficient implementation of the parameter server framework. It provides clean yet powerful APIs. For example, a worker node can communicate with the server nodes by

• Push(keys, values): push a list of (key, value) pairs to the server nodes
• Pull(keys): pull the values from servers for a list of keys
• Wait: wait untill a push or pull finished.

A simple example:

  std::vector<uint64_t> key = {1, 3, 5};
  std::vector<float> val = {1, 1, 1};
  std::vector<float> recv_val;
  ps::KVWorker<float> w;
  w.Wait(w.Push(key, val));
  w.Wait(w.Pull(key, &recv_val));

More features:

• Flexible and high-performance communication: zero-copy push/pull, supporting dynamic length values, user-defined filters for communication compression
• Server-side programming: supporting user-defined handles on server nodes

Build

ps-lite requires a C++11 compiler such as g++ >= 4.8. On Ubuntu >= 13.10, we can install it by

sudo apt-get update && sudo apt-get install -y build-essential git

Instructions for older Ubuntu, Centos, and Mac Os X.

Then clone and build

git clone https://github.com/dmlc/ps-lite
cd ps-lite && make -j4

How to use

ps-lite provides asynchronous communication for other projects:

• Distributed deep neural networks: MXNet, CXXNET and Minverva
• Distributed high dimensional inference, such as sparse logistic regression, factorization machines: DiFacto Wormhole

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

Research papers

1. Mu Li, Dave Andersen, Alex Smola, Junwoo Park, Amr Ahmed, Vanja Josifovski, James Long, Eugene Shekita, Bor-Yiing Su. Scaling Distributed Machine Learning with the Parameter Server. In Operating Systems Design and Implementation (OSDI), 2014
2. Mu Li, Dave Andersen, Alex Smola, and Kai Yu. Communication Efficient Distributed Machine Learning with the Parameter Server. In Neural Information Processing Systems (NIPS), 2014