Parameter Server

The parameter server is a distributed machine learning framework scaling to industrial-level problems. It is a joint project by CMU SML-Lab, Baidu IDL, and Google.

Install

Requirements:

• compiler: gcc >= 4.7.2 or llvm >= 3.4 (known problems with gcc <= 4.7, but didn't test for llvm)
• system: should work on both Linux and Mac OS (tested on Ubuntu 12.10, 13.10, RHEL 4U3, Max OS X 10.9)
• dependent libraries: zeromq, gflags, glogs, gtest, protobuf, zlib, snappy, eigen3. We provide install.sh to build them from sources automatically.

Build

The following steps download sources codes and data, and then build the dependent libraries and the parameter server:

git clone https://github.com/mli/parameter_server
cd parameter_server
git clone https://github.com/mli/third_party
cd third_party && ./install.sh
cd .. && make -j8

Several options are available for building:

• depended libraries are install somewhere

export THIRD=/usr/local && make -j8

• statically linking all libraries:

export STATIC=1 && make -j8

• failed to install google hash, so use =std::map= instead

export GOOGLE_HASH=0 && make -j8

Input Data

The parameter system can read data in either raw binary format or protobuf format. There is a text2proto program converting data from a range of text formats into binary ones. See data/rcv1_bianry.sh for an example.

parameter server format

The format of one instance:

label;group_id feature[:value] feature[:value] ...;groud_id ...;...;

• label: +1/-1 for binary classification, 0,1,2,... for multiclass classification, a float value for regression. And certainly it can be empty.
• group_id: an integer identity of a feature group, each instance should contains at least one feature group.
• feature: for sparse data, it is an 64-bit integer presenting the feature id, while for dense data, it is a float feature value
• weight: only valid for non-binary sparse data, it is a float feature value.

libsvm format

Sparse format:

label feature_id:value feature_id:value ...

vowpal wabbit format

TODO

Run

• On local machine: See script/local.sh
• by mpirun: Run script/mpi_root.sh at the root machine. An example configuration is in config/mpi.conf.
• by yarn: In progress.