Distributed and Parallel Computing Framework with / for Python
dispy
.. note:: Full documentation for dispy is now available at `dispy.org
<https://dispy.org>`_.
dispy <https://dispy.org>_ is a comprehensive, yet easy
to use framework for creating and using compute clusters to execute computations
in parallel across multiple processors in a single machine (SMP), among many
machines in a cluster, grid or cloud. dispy is well suited for data parallel
(SIMD) paradigm where a computation is evaluated with different (large) datasets
independently with no communication among computation tasks (except for
computation tasks sending intermediate results to the client).
dispy works with Python versions 2.7+ and 3.1+ on Linux, Mac OS X and Windows; it may
work on other platforms (e.g., FreeBSD and other BSD variants) too.
dispy is implemented with pycos <https://pycos.org>,
an independent framework for asynchronous, concurrent, distributed, network
programming with tasks (without threads). pycos uses non-blocking sockets with
I/O notification mechanisms epoll, kqueue and poll, and Windows I/O Completion
Ports (IOCP) for high performance and scalability, so dispy works efficiently
with a single node or large cluster(s) of nodes. pycos itself has support for
distributed/parallel computing, including transferring computations, files
etc., and message passing (for communicating with client and other computation
tasks). While dispy can be used to schedule jobs of a computation to get the
results, pycos can be used to create distributed communicating processes <https://pycos.org/dispycos.html>, for broad range of use cases.
Computations (Python functions or standalone programs) and their
dependencies (files, Python functions, classes, modules) are
distributed automatically.
Computation nodes can be anywhere on the network (local or
remote). For security, either simple hash based authentication or
SSL encryption can be used.
After each execution is finished, the results of execution, output,
errors and exception trace are made available for further
processing.
Nodes may become available dynamically: dispy will schedule jobs
whenever a node is available and computations can use that node.
If callback function is provided, dispy executes that function
when a job is finished; this can be used for processing job
results as they become available.
Client-side and server-side fault recovery are supported:
If user program (client) terminates unexpectedly (e.g., due to
uncaught exception), the nodes continue to execute scheduled
jobs. If client-side fault recover option is used when creating a
cluster, the results of the scheduled (but unfinished at the time of
crash) jobs for that cluster can be retrieved later.
If a computation is marked reentrant when a cluster is created and a
node (server) executing jobs for that computation fails, dispy
automatically resubmits those jobs to other available nodes.
dispy can be used in a single process to use all the nodes
exclusively (with JobCluster - simpler to use) or in multiple
processes simultaneously sharing the nodes (with
SharedJobCluster and dispyscheduler program).
Cluster can be monitored and managed <https:/dispy.org/httpd.html>_ with web browser.
dispy requires pycos_ for concurrent, asynchronous network programming with tasks. pycos is
automatically installed if dispy is installed with pip. Under Windows efficient polling notifier
I/O Completion Ports (IOCP) is supported only if pywin32 <https://github.com/mhammond/pywin32>_
is installed; otherwise, inefficient select notifier is used.
To install dispy, run::
python -m pip install dispy
Short summary of changes for each release can be found at News <https://pycos.com/forum/viewforum.php?f=11>. Detailed logs / changes are at
github commits <https://github.com/pgiri/dispy/commits/master>.
dispy.org_.Examples <https://dispy.org/examples.html>_.Github (Code Respository) <https://github.com/pgiri/dispy>_.