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 gcc 4.8 installation on other platforms:

• Ubuntu 12.04
• Centos
• 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, Minverva, and BytePS
• Distributed high dimensional inference, such as sparse logistic regression, factorization machines: DiFacto Wormhole

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