Metadata-Version: 1.1
Name: dispy
Version: 4.6.12
Summary: Distributed and Parallel Computing with/for Python.
Home-page: http://dispy.sourceforge.net
Author: Giridhar Pemmasani
Author-email: pgiri@yahoo.com
License: MIT
Description: dispy
        ######
        
        `dispy <http://dispy.sourceforge.net>`_ 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). If
        communication/cooperation among tasks is needed, `asyncoro
        <http://asyncoro.sourceforge.net>`_ framework could be used.
        
        dispy works with Python versions 2.7+ and 3.1+. It has been tested
        with Linux, OS X and Windows; it may work on other platforms too.
        
        Features
        --------
        * dispy is implemented with asyncoro_, an independent framework for
          asynchronous, concurrent, distributed, network programming with
          coroutines (without threads). asyncoro 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. asyncoro 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, asyncoro can be used to create `distributed
          communicating processes
          <http://asyncoro.sourceforge.net/discoro.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
          <http://dispy.sourceforge.net/httpd.html>`_ with web browser.
        
        Dependencies
        ------------
        
        dispy requires asyncoro_ for concurrent, asynchronous network
        programming with coroutines. asyncoro is automatically installed if
        dispy is installed with pip. Under Windows efficient polling notifier
        I/O Completion Ports (IOCP) is supported only if `pywin32
        <http://sourceforge.net/projects/pywin32/files/pywin32/>`_ is
        installed; otherwise, inefficient *select* notifier is used.
        
        Installation
        ------------
        To install dispy for Python 2.7+, run::
        
           pip install dispy
        
        or to install dispy for Python 3.1+, run::
        
           pip3 install dispy
        
        Authors
        -------
        * Giridhar Pemmasani
        
        Links
        -----
        * `Project page <http://dispy.sourceforge.net>`_.
        * `Examples <http://dispy.sourceforge.net/examples.html>`_.
        * `Changes <https://sourceforge.net/p/dispy/news/>`_.
        * `Source <https://github.com/pgiri/dispy>`_.
        
Keywords: distributed computing,parallel processing,mapreduce,hadoop,job scheduler
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.1
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
