MPI parallel map and cluster scheduling
The pyina package provides several basic tools to make MPI-based parallel computing more accessable to the end user. The goal of pyina is to allow the user to extend their own code to MPI-based parallel computing with minimal refactoring.
The central element of pyina is the parallel map algorithm. pyina currently provides two strategies for executing the parallel-map, where a strategy is the algorithm for distributing the work list of jobs across the availble nodes. These strategies can be used “in-the-raw” (i.e. directly) to provide the map algorithm to a user’s own mpi-aware code. Further, in pyina.mpi pyina provides pipe and map implementations (known as “easy map”) that hide the MPI internals from the user. With the “easy map”, the user can launch their code in parallel batch mode – using standard Python and without ever having to write a line of MPI code.
There are several ways that a user would typically launch their code in parallel – directly with mpirun or mpiexec, or through the use of a scheduler such as torque or slurm. pyina encapsulates several of these “launchers”, and provides a common interface to the different methods of launching a MPI job.
pyina is part of pathos, a Python framework for heterogeneous computing. pyina is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of issues is located at https://github.com/uqfoundation/pyina/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query.
pyina provides a highly configurable parallel map interface to running MPI jobs, with:
a map interface that extends the Python map standard
the ability to submit batch jobs to a selection of schedulers
the ability to customize node and process launch configurations
the ability to launch parallel MPI jobs with standard Python
ease in selecting different strategies for processing a work list
The latest released version of pyina is available at:
pyina is distributed under a 3-clause BSD license.
You can get the latest development version with all the shiny new features at:
If you have a new contribution, please submit a pull request.
pyina can be installed with pip:
$ pip install pyina
A version of MPI must also be installed. Launchers in pyina that submit to a scheduler will throw errors if the underlying scheduler is not available, however a scheduler is not required for pyina to execute.
python (or pypy), >=3.7
Probably the best way to get started is to look at the documentation at http://pyina.rtfd.io. Also see https://github.com/uqfoundation/pyina/tree/master/examples and pyina.tests for a set of scripts that demonstrate the configuration and launching of mpi-based parallel jobs using the “easy map” interface. You can run the tests with python -m pyina.tests. A script is included for querying, setting up, and tearing down an MPI environment, see python -m pyina for more information. The source code is generally well documented, so further questions may be resolved by inspecting the code itself. Please feel free to submit a ticket on github, or ask a question on stackoverflow (@Mike McKerns). If you would like to share how you use pyina in your work, please send an email (to mmckerns at uqfoundation dot org).
Important classes and functions are found here:
pyina.mpi [the map API definition]
pyina.schedulers [all available schedulers]
pyina.launchers [all available launchers]
Mapping strategies are found here:
pyina.mpi_scatter [the scatter-gather strategy]
pyina.mpi_pool [the worker pool strategy]
pyina also provides a convience script that helps navigate the MPI environment. This script can be run from anywhere with:
If may also be convienent to set a shell alias for the launch of ‘raw’ mpi-python jobs. Set something like the following (for bash):
$ alias mpython1='mpiexec -np 1 `which python`' $ alias mpython2='mpiexec -np 2 `which python`' $ ...
If you use pyina to do research that leads to publication, we ask that you acknowledge use of pyina by citing the following in your publication:
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis, "Building a framework for predictive science", Proceedings of the 10th Python in Science Conference, 2011; http://arxiv.org/pdf/1202.1056 Michael McKerns and Michael Aivazis, "pathos: a framework for heterogeneous computing", 2010- ; https://uqfoundation.github.io/project/pathos
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.