dispy 4.3

Distributed and Parallel Computing Framework with/for Python.

dispy is a rather 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 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), although it doesn’t include job scheduling.

• 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 with web browser.

Dependencies

dispy requires asyncoro for concurrent, asynchronous network programming with coroutines. asyncoro can be installed for Python 2.7+ with:

pip install asyncoro

or for Python 3.1+ with:

pip3 install asyncoro

Under Windows efficient polling notifier I/O Completion Ports (IOCP) is supported only if 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.

• Examples.

• Changes.