NOTE: This project was moved under the [NANOGrav][https://github.com/nanograv] github organization in November 2023

MPI enabled Parallel Tempering MCMC code written in Python.

See the examples for some simple use cases.

For MPI support you will need A functional MPI 1.x/2.x/3.x implementation like:

• MPICH

  # mac
  brew install mpich
  
  # debian
  sudo apt install mpich

• Open MPI

  # mac
  brew install open-mpi
  
  # debian
  sudo apt install libopenmpi-dev

To run with MPI support you can run your script containing a sampler with:

mpirun -np <number of temperature chains> script.py

This will kick off np chains running at different temperatures. The temperature ladder and sampling schemes can be set in the PTMCMCSampler.sample() method.

For development clone this repo and run:

make init
source venv/bin/activate

pip install ptmcmcsampler

for MPI support use

pip install ptmcmcsampler[mpi]

conda install -c conda-forge ptmcmcsampler

for MPI support use

conda install -c conda-forge ptmcmcsampler mpi4py

Production:

For production use, the latest release of PTMCMCSampler is installed directly from PyPi.

# Build the image, tagging it as `ptmcmc:latest`.  
# 
# This example includes both optional MPI support and the optional `acor` library 
# (you can omit either / both).
docker build --pull --build-arg "MPI=1" --build-arg "ACOR=1" -t ptmcmc --no-cache .

# Run the image, mounting the `data/` subdirectory on your computer 
# to `/code/data/` inside the Docker container. Note that MPI won't work 
# if we run as the root user (the default).
docker run -it --rm --user mcmc_user -v $(pwd)/data:/code/data ptmcmc

# When finished with the container, exit back to the host OS
CTRL^D

Development:

For PTMCMCSampler development, dependencies are installed from requirements.txt and requirements_dev.txt. No PTMCMCSampler code is omitted from the built image, whose purpose is for testing new code. You can also add --build-arg "ACOR=1" to the build command to include the optional acor dependency (MPI is always included via requirements.txt).

# Build the image
docker build --pull -t ptmcmc --build-arg "TARGET_ENV=dev" --no-cache .

# Run the image, mounting the working directory on the host OS to /code/ inside the container.
# MPI won't work if we run as the root user (the default).
docker run -it --rm --user mcmc_user -v $(pwd)/:/code ptmcmc

# Exit back to host OS
CTRL-D

If you make use of this code, please cite:

@misc{justin_ellis_2017_1037579,
  author       = {Justin Ellis and
                  Rutger van Haasteren},
  title        = {jellis18/PTMCMCSampler: Official Release},
  month        = oct,
  year         = 2017,
  doi          = {10.5281/zenodo.1037579},
  url          = {https://doi.org/10.5281/zenodo.1037579}
}

In order for the sampler to run correctly using acor with Python 3 kernels the GitHub version of acor needs to be installed. (Currently the PyPI version is behind the GitHub version.) It can be easily installed with:

pip install git+https://github.com/dfm/acor.git@master

Note that acor is not required to run the sampler, it simply calculates the effective chain length for output in the chain file.