pyina package documentation¶
pyina: MPI parallel map and cluster scheduling¶
pyina package provides several basic tools to make MPI-based
parallel computing more accessable to the end user. The goal of
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.
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
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
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 install pyina
A version of MPI must also be installed. Launchers in
submit to a scheduler will throw errors if the underlying scheduler is
not available, however a scheduler is not required for
pyina to execute.
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
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
Please see https://uqfoundation.github.io/project/pathos or http://arxiv.org/pdf/1202.1056 for further information.
- balance_workload(nproc, popsize, *index, **kwds)¶
divide popsize elements on ‘nproc’ chunks
nproc: int number of nodes popsize: int number of jobs index: int rank of node(s) to calculate for (using slice notation) skip: int rank of node upon which to not calculate (i.e. the master)
returns (begin, end) index vectors
- ceil(x, /)¶
Return the ceiling of x as an Integral.
This is the smallest integer >= x.
- ensure_mpi(size=1, doc=None)¶
ensure that mpi-enabled python is being called with the appropriate size
size: minimum required size of the MPI world [default = 1]
doc: error string to throw if size restriction is violated
- get_workload(index, nproc, popsize, skip=None)¶
returns the workload that this processor is responsible for
index: int rank of node to calculate for nproc: int number of nodes popsize: int number of jobs skip: int rank of node upon which to not calculate (i.e. the master)
returns (begin, end) index
generate an isoformat timestring for the given time in seconds
calculate number of seconds from a given isoformat timestring
- lookup(inputs, *index)¶
get tuple of inputs corresponding to the given index
- mpiprint(string='', end='\n', rank=0, comm=None)¶
print the given string to the given rank
- wait_for(path, sleep=1, tries=150, ignore_errors=False)¶
block execution by waiting for a file to appear at the given path
if the file is not found after the given number of tries, an error will be thrown unless
subproc = Popen(...)and
subproc.wait()is usually a better approach. However, when a handle to the subprocess is unavailable, waiting for a file to appear at a given path is a decent last resort.
- which_mpirun(mpich=None, fullpath=False)¶
try to autodetect an available mpi launcher
if mpich=True only look for mpich, if False only look for openmpi
- which_python(lazy=False, fullpath=True)¶
get an invocation for this python on the execution path
- which_strategy(scatter=True, lazy=False, fullpath=True)¶
try to autodetect an available strategy (scatter or pool)