Interface with HPC job schedulers (SLURM, LSF, PBS, etc)

This section describes how to send any discipline, or sub process such as an MDA to a HPC using the job scheduler interfaces.

The method to be used is wrap_discipline_in_job_scheduler() to wrap any discipline.

This feature is extensible through plugins using SchedulersFactory.

gemseo.wrap_discipline_in_job_scheduler(discipline, scheduler_name, workdir_path, **options)[source]

Wrap the discipline within another one to delegate its execution to a job scheduler.

The discipline is serialized to the disk, its input too, then a job file is created from a template to execute it with the provided options. The submission command is launched, it will setup the environment, deserialize the discipline and its inputs, execute it and serialize the outputs. Finally, the deserialized outputs are returned by the wrapper.

All process classes MDOScenario, or MDA, inherit from MDODiscipline so can be sent to HPCs in this way.

The job scheduler template script can be provided directly or the predefined templates file names in gemseo.wrappers.job_schedulers.template can be used. SLURM and LSF templates are provided, but one can use other job schedulers or to customize the scheduler commands according to the user needs and infrastructure requirements.

The command to submit the job can also be overloaded.

  • discipline (MDODiscipline) – The discipline to wrap in the job scheduler.

  • scheduler_name (str) – The name of the job scheduler (for instance LSF, SLURM, PBS).

  • workdir_path (Path) – The path to the workdir

  • **options (dict[str, Any]) – The submission options.


OSError – if the job template does not exist.

Return type:



This method serializes the passed discipline so it has to be serializable. All disciplines provided in GEMSEO are serializable but it is possible that custom ones are not and this will make the submission proess fail.


This example execute a DOE of 100 points on an MDA, each MDA is executed on 24 CPUS using the SLURM wrapper, on a HPC, and at most 10 points run in parallel, everytime a point of the DOE is computed, another one is submitted to the queue.

>>> from gemseo.wrappers.job_schedulers.schedulers_factory import (
...     SchedulersFactory,
... )
>>> from gemseo import create_discipline, create_scenario, create_mda
>>> from gemseo.problems.sellar.sellar_design_space import SellarDesignSpace
>>> disciplines = create_discipline(["Sellar1", "Sellar2", "SellarSystem"])
>>> mda = create_mda(disciplines)
>>> wrapped_mda= wrap_discipline_in_job_scheduler(mda, scheduler_name="SLURM",
>>>                                               workdir_path="workdir",
>>>                                               cpus_per_task=24)
>>> scn=create_scenario(mda, "DisciplinaryOpt", "obj", SellarDesignSpace(),
>>>                     scenario_type="DOE")
>>> scn.execute(algo="lhs", n_samples=100, algo_options={"n_processes": 10})

In this variant, each discipline is wrapped independently in the job scheduler, which allows to parallelize more the process because each discipline will run on indpendent nodes, whithout being parallelized using MPI. The drawback is that each discipline execution will be queued on the HPC. A HDF5 cache is attached to the MDA, so all executions will be recorded. Each wrapped discipline can also be cached using a HDF cache.

>>> from gemseo.core.discipline import MDODiscipline
>>> disciplines = create_discipline(["Sellar1", "Sellar2", "SellarSystem"])
>>> wrapped_discs=[wrap_discipline_in_job_scheduler(disc,
>>>                                                 workdir_path="workdir",
>>>                                                 cpus_per_task=24,
>>>                                                 scheduler_name="SLURM"),
>>>                for disc in disciplines]
>>> scn=create_scenario(wrapped_discs, "MDF", "obj", SellarDesignSpace(),
>>>                     scenario_type="DOE")
>>> scn.formulation.mda.set_cache_policy(MDODiscipline.HDF5_CACHE,
>>>                                      cache_hdf_file="mda_cache.h5")
>>> scn.execute(algo="lhs", n_samples=100, algo_options={"n_processes": 10})