PyWPS has extensions to enhance its usability in special uses cases, for example to run Web Processing Services at High Performance Compute (HPC) centers. These extensions are disabled by default. They need a modified configuration and have additional software packages. The extensions are:

  • Using batch job schedulers (distributed resource management) at HPC compute centers.
  • Using container solutions like Docker in a cloud computing infrastructure.

Job Scheduler Extension

By default PyWPS executes all processes on the same machine as the PyWPS service is running on. Using the PyWPS scheduler extension it becomes possible to delegate the execution of asynchronous processes to a scheduler system like Slurm, Grid Engine and TORQUE. By enabling this extension one can handle the processing workload using an existing scheduler system commonly found at High Performance Compute (HPC) centers.


The PyWPS process implementations are not changed by using the scheduler extension.

To activate this extension you need to edit the pywps.cfg configuration file and make the following changes:

mode = scheduler

The scheduler extension uses the DRMAA library to talk to the different scheduler systems. Install the additional Python dependencies using pip:

$ pip install -r requirements-processing.txt  # drmaa

If you are using the conda package manager you can install the dependencies with:

$ conda install drmaa dill

The package dill is an enhanced version of the Python pickle module for serializing and de-serializing Python objects.


In addition you need to install and configure the drmaa modules for your scheduler system on the machine PyWPS is running on. Follow the instructions given in the DRMAA documentation and by your scheduler system installation guide.


See an example on how to use this extension with a Slurm batch system in a docker demo.


COWS WPS has a scheduler extension for Sun Grid Engine (SGE).

Interactions of PyWPS with a scheduler system

The PyWPS scheduler extension uses the Python dill library to dump and load the processing job to/from filesystem. The batch script executed on the scheduler system calls the PyWPS joblauncher script with the dumped job status and executes the job (no WPS service running on scheduler). The job status is updated on the filesystem. Both the PyWPS service and the joblauncher script use the same PyWPS configuration. The scheduler assumes that the PyWPS server has a shared filesystem with the scheduler system so that XML status documents and WPS outputs can be found at the same file location. See the interaction diagram how the communication between PyWPS and the scheduler works.


Interaction diagram for PyWPS scheduler extension.

The following image shows an example of using the scheduler extension with Slurm.


Example of PyWPS scheduler extension usage with Slurm.

Docker Container Extension


This extension is on our wish list. In can be used to encapsulate and control the execution of a process. It enhances also the use case of Web Processing Services in a cloud computing infrastructure.