• On Amazon EC2's FREE MACHINE (512M memory, 1 core). We can sample 3.6M varialbes/seconds.
• On a 2-node Amazon EC2 machine, sampling 7 billion random variables, each of which has 10 features, takes 3 minutes. This means we can run inference for all living human beings on this planet with $15 (100 samples!)
• On Macbook, DimmWitted runs 10x faster than DeepDive's default sampler.

See: the DimmWitted sampler page in DeepDive's documentation.

The binary format for DimmWitted's input is specified in DeepDive's factor graph schema reference.

First, install dependencies

make dep

Then

make

This will use whatever in you $(CXX) variable to compile. We assume that you have > g++4.7.2 or clang++-4.2. To specify a compiler to use, type in something like

CXX=/dfs/rulk/0/czhang/software/gcc/bin/g++ make

On MacOS, CXX=/opt/local/bin/clang++ make

To test, run

make test

On MacOS, you can use MacPorts to install clang.

port select --list clang
sudo port install clang-3.7
sudo port select --set clang mp-clang-3.7 

You can then compile using

CXX=/opt/local/bin/clang++ make

Build

docker build -t sampler .

Pull from Docker Hub

docker pull closedloop/sampler

Simple Run

docker run -it -v $GRAPH_FOLDER:/data/graph -v $OUTPUT_FOLDER:/data/out closedloop/sampler

where

• $GRAPH_FOLDER is the location of the graph.weights, graph.variables, graph.factors, graph.edges graph.meta files
• $OUTPUT_FOLDER is the folder location for the generated inference_result.out.text and inference_result.out.weights.text files

The basic usage example above calls the following function:

./dw gibbs -w /data/graph/graph.weights -v /data/graph/graph.variables -f /data/graph/graph.factors -e /data/graph/graph.edges -m /data/graph/graph.meta -o /data/out -i 500 -s 1 -l 1000

Run with custom parameters

docker run -it -v $GRAPH_FOLDER:/data/graph -v $OUTPUT_FOLDER:/data/out closedloop/sampler -i 1000 -s 1 -l 1000 --alpha 0.01

C. Zhang and C. Ré. DimmWitted: A study of main-memory statistical analytics. PVLDB, 2014.