This is a simple REST service that can run (some) CWL jobs on some remote compute resource. It uses a REST API as its interface and PyXenon to run jobs remotely.
The implementation is fairly complete, and the main things needed are some real-world testing, bug fixing, and polish.
Cerise is a generic service for running CWL workflows on compute resources (i.e. clusters, supercomputers, and simply remote machines). It tries to offer a consistent environment for workflows, so that a workflow sent to resource A will work unchanged on resource B as well.
To achieve this, and to offer a bit of safety and perhaps security, Cerise does not allow running arbitrary CWL command line tools. Instead, it expects the user to submit a workflow document that refers to predefined steps built into the service.
Defining these steps, and adding them to the service, is called specialising the service. A specialisation of Cerise is always specific to a project (which determines which steps are available and what inputs and outputs they have), and to a compute resource (which determines how they are implemented). Thus, two workflows sent to two different specialisations to the same project, but to different compute resources, should give the same result (assuming the calculation is deterministic!).
Documentation on how to specialise Cerise may be found in docs/specialising.rst. Other documentation there covers configuring the Cerise service itself (i.e. port numbers, logging configuration, etc.). There is also a requirements document there, a detailed description of the design, and source code documentation. You can also read the documentation online at http://cerise.readthedocs.io/en/develop/
Cerise can be run directly on a host, or in a Docker container. A local installation is created as follows:
clone the repository
git clone firstname.lastname@example.org:MD-Studio/cerise.git
change into the top-level directory
pip3 install .
Steps and supporting files may then be placed in the api/ directory to specialise the service. For a detailed explanation, see http://cerise.readthedocs.io/en/develop/specialising.html.
To build the Docker image, use
docker build -t cerise .
and then start a container using
docker run --name=cerise -p 29593:29593 cerise
Note that the docker image gets its
the source tree.
However, this will run a plain, unspecialised Cerise, which is not very useful, as it runs jobs locally inside the container, and it doesn't contain any steps to execute. To use Cerise in Docker, you should make a new, specialised Docker image based on the standard Cerise image, and start that instead. Please refer to http://cerise.readthedocs.io/en/develop/specialising.html for further instructions.
- Python 3.5 or up
On the compute resource:
- Python 2.7 with CWLTool, or
- Python 3 with CWLTool or CWLTiny, the built-in CWL runner, or
- Some other CWL runner and whichever dependencies it needs
examples directory, you will find some example Python scripts that
create and execute jobs on the running service.
Cerise follows the Google Python style guide, with Sphinxdoc docstrings for module public functions. If you want to contribute to the project please fork it, create a branch including your addition, and create a pull request.
The tests use relative imports and can be run directly after making
changes to the code. To run all tests use
pytest in the main directory.
This will also run the integration tests, which take several minutes to complete
as a bunch of Docker containers is built, started, and stopped.
Before creating a pull request please ensure the following:
- You have written unit tests to test your additions
- All unit tests pass
- The examples still work and produce the same (or better) results
- The code is compatible with Python 3.5
- An entry about the change or addition is created in CHANGELOG.md
- You've added yourself as contributing author
Contributing authors so far:
- Lourens Veen