PTMCMCSampler
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:
-
# mac brew install mpich # debian sudo apt install mpich
-
# 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.
Installation
Development
For development clone this repo and run:
make init
source venv/bin/activate
Via pip
pip install ptmcmcsampler
for MPI support use
pip install ptmcmcsampler[mpi]
Via conda
conda install -c conda-forge ptmcmcsampler
for MPI support use
conda install -c conda-forge ptmcmcsampler mpi4py
Via Docker
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
Attribution
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}
}
Correlation Length
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.