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 (Python function or standalone program) is evaluated with different (large) datasets independently with no communication among computation tasks (except for computation tasks sending Provisional/Intermediate Results or Transferring Files to the client). If communication/cooperation among tasks is needed, asyncoro framework could be used.
Some of the features of dispy:
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 to nodes automatically. Computations, if they are Python functions, can also transfer files on the nodes to the client.
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.
Amazon cloud computing platform EC2 can be used as compute nodes, either exclusively or in addition to any local compute nodes; see Cloud Computing (with Amazon EC2) for details.
dispy works with Python versions 2.7+ and 3.1+ and tested on Linux, OS X and Windows; it may work on other platforms too. dispy works with PyPy as well.
dispy is availble through Python Package Index (PyPI) so it can be easily installed for Python 2.7+ with:
pip install dispy
and/or for Python 3.1+ with:
pip3 install dispy
dispy can also be downloaded from Sourceforge Files. Files in ‘py2’ directory in the downloaded package are to be used with Python 2.7+ and files in ‘py3’ directory are to be used with Python 3.1+.
Below is a quick guide on how to use dispy. More details are available in dispy.
dispy framework consists of 4 components:
As a tutorial, consider the following program, in which function compute is distributed to nodes on a local network for parallel execution. First, run dispynode program (‘dispynode.py’) on each of the nodes on the network. Now run the program below, which creates a cluster with function compute; this cluster is then used to create jobs to execute compute with a random number 10 times.:
# 'compute' is distributed to each node running 'dispynode' def compute(n): import time, socket time.sleep(n) host = socket.gethostname() return (host, n) if __name__ == '__main__': import dispy, random cluster = dispy.JobCluster(compute) jobs =  for i in range(10): # schedule execution of 'compute' on a node (running 'dispynode') # with a parameter (random number in this case) job = cluster.submit(random.randint(5,20)) job.id = i # optionally associate an ID to job (if needed later) jobs.append(job) # cluster.wait() # wait for all scheduled jobs to finish for job in jobs: host, n = job() # waits for job to finish and returns results print('%s executed job %s at %s with %s' % (host, job.id, job.start_time, n)) # other fields of 'job' that may be useful: # print(job.stdout, job.stderr, job.exception, job.ip_addr, job.start_time, job.end_time) cluster.stats()
dispy’s scheduler runs the jobs on the processors in the nodes running dispynode. The nodes execute each job with the job’s arguments in isolation - computations shouldn’t depend on global state, such as modules imported outside of computations, global variables etc. (except if ‘setup’ parameter is used, as explained in dispy and Examples). In this case, compute needs modules time and socket, so it must import them. The program then gets results of execution for each job with job().
dispy can also be used as a command line tool; in this case the computations should only be programs and dependencies should only be files.:
dispy.py -f /some/file1 -f file2 -a "arg11 arg12" -a "arg21 arg22" -a "arg3" /some/program
will distribute ‘/some/program’ with dependencies ‘/some/file1’ and ‘file2’ and then execute ‘/some/program’ in parallel with arg11 and arg12 (two arguments to the program), arg21 and arg22 (two arguments), and arg3 (one argument).