Skip to main content

Soma-workflow is a unified and simple interface to parallel computing resources. It aims at making easier the use of parallel resources by non expert users and software.

Project description

Main Features

Unified interface to multiple computing resources:
Submission of jobs or workflows with an unique interface to various parallel resources: multiple core machines or clusters which can be managed by various systems (such as Grid Engine, Condor, Torque/PBS, LSF..)
Workflow management:
Soma-workflow provides the possibility to submit a set of tasks (called jobs) with execution dependencies without dealing with individual task submission.
Python API and Graphical User Interface:
The Python API was designed to be easily used by non expert user, but also complete to meet external software needs: submission, control and monitoring of jobs and workflows. The GUI provides an easy and quick way of monitoring workflows on various computing resources. The workflows can also be submitted and controlled using the GUI.
Quick start on multiple core machines:
Soma-workflow is directly operational on any multiple core machine.
Transparent remote access to computing resources:
When the computing resource is remote, Soma-workflow can be used as a client-server application. The communication with a remote computing resource is done transparently for the user through a ssh port forwarding tunnel. The client/server architecture enables the user to close the client application at any time. The workflows and jobs execution are not stopped. The user can open a client at any time to check the status of his work.
File transfer and file path mapping tools:
If the user’s machine and the remote computing resource do not have a shared file system, Soma-workflow provides tools to handle file transfers and/or path name matchings.


Visit Soma-workflow main page!

An extensive documentation is available, with ready to use examples.


Qt version 4.6.2 or more, PyQt version 4.7.2 or more are required if you want to use the graphical interface.

To provide you quickly with a functional application, your own multiple core machine can be used directly and without any configuration to distribute computation, no matter the installation mode chosen.

We recommend to install Soma-workflow in a local directory (no special rights required & easy clean up at any time removing the local directory)

  1. Create a local directory such as ~/.local/lib/python2.6/site-packages and create the bin directory: ~/.local/bin

  2. Setup the environment variables with the commands:

    $ export PYTHONPATH=$HOME/.local/lib/python2.6/site-packages:$PYTHONPATH
    $ export PATH=$HOME/.local/bin:$PATH
You can copy these lines in your ~/.bashrc for an automatic setup of the variables at login.
  1. Install Soma-workflow using or easy_install.


Download the latest tarball and expand it.

Install Soma-workflow in the ~/.local directory:

$ python install --user

If you chose a different name for you local directory (ex: ~/mylocal) use instead the following command:

$ python install --prefix ~/mylocal

Installation on the system with administrator rights:

$ python install

With easy_install

This command will just install Soma-workflow:

$ easy_install --prefix ~/.local "soma-workflow"

To enable plotting in the GUI, this command will install matplotlib as well:

$ easy_install --prefix ~/.local "soma-workflow[plotting]"

To install the client interface to a remote computing resource, this command will install Soma-workflow, Pyro and Paramiko

$ easy_install --prefix ~/.local "soma-workflow[client]"

To install the client application with plotting enabled, this command will install Soma-workflow, Pyro, Paramiko and matplotlib:

$ easy_install --prefix ~/.local "soma-workflow[client,plotting]"

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for soma-workflow, version 2.1.0
Filename, size File type Python version Upload date Hashes
Filename, size soma-workflow-2.1.0.tar.gz (2.2 MB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page