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Configuring compute backends¶

The REANA platform is able to dispatch user jobs to several supported compute backends including HTCondor, Kubernetes and Slurm. The following documentation explains how to configure them from the administrator point of view.

Note

This page is addressed to computer engineers deploying the REANA platform on their clusters. If you are a researcher looking for information on how to configure your workflows so that your jobs would be sent to a particular supported compute backend, please see the corresponding user-oriented compute backend documentation pages instead.

HTCondor¶

The HTCondor compute backend is useful for high-throughput user computing needs. Currently REANA supports the HTCondor batch system at CERN (htcondorcern).

If you wish to offer the HTCondor compute backend to users in your installation, please include the htcondorcern value in the Helm list compute_backends and customise the components.reana_job_controller.image value to use an image compiled with the HTCondor CERN backend support, such as reanahub/reana-job-controller-htcondorcern-slurmcern:0.9.0.

Currently it is not possible to further customise the HTCondor integration through additional Helm values or environment variables. Please get in touch should you need to customise the HTCondor integration further in your REANA deployment.

Here is a customisation snippet example:

compute_backends:
  - kubernetes
  - htcondorcern
components:
  reana_job_controller:
    image: docker.io/reanahub/reana-job-controller-htcondorcern-slurmcern:0.9.0

Kubernetes¶

Kubernetes is the default compute backend used by REANA for user jobs. It does not require any particular configuration on the cluster administration side.

If you would like to further customise the Kubernetes compute backend, you can set various Helm values during deployment that will affect the scheduling of user job pods:

If you are using node_label_runtimejobs Helm value, the jobs will be scheduled only to the cluster nodes that are labelled with the corresponding label value, such as reana.io/system=runtimejobs. The dynamic labelling of nodes allows to increase/decrease the cluster job capacity based on increasing/decreasing user workload demands. The node labelling system is described in a dedicated document Deploying at scale.
The default memory limit for user job containers is governed by the Helm value kubernetes_jobs_memory_limit. If a user job exceeds this limit, the user job will be terminated and an error reported back to the user. By default the limit is around 4 GiB. Please set this variable based on the cluster node flavours you have in your system and the typical user workflow memory needs.
The maximum memory limit a user can request is governed by the Helm value kubernetes_jobs_max_user_memory_limit. For example, you can allow users to request up to 10 GiB of memory even though the default is 4 GiB. If a user would request more than this value, the job would not be accepted.
The default runtime limit for user job containers is governed by the Helm value kubernetes_jobs_timeout_limit. If a user job exceeds this limit, the user job will be terminated and an error reported back to the user. By default the runtime limit is about 7 days. Please set this variable based on the typical user workflow runtime needs in your installation.
The maximum runtime limit a user can request is governed by the Helm value kubernetes_jobs_max_user_timeout_limit. For example, you can allow users to request a runtime of up to 14 days even though the default is 7 days. If a user would request more than this value, the job would not be accepted.

Here is a customisation snippet example:

compute_backends:
  - kubernetes
kubernetes_jobs_memory_limit: 2Gi
kubernetes_jobs_max_user_memory_limit: 10Gi

Slurm¶

The Slurm compute backend is useful for high-performance user computing needs. Currently REANA supports the Slurm batch system at CERN (slurmcern).

If you wish to include the Slurm compute backend in your installation, please include the slurmcern value in the Helm list compute_backends and customise the components.reana_job_controller.image value to use an image compiled with the Slurm CERN backend support, such as reanahub/reana-job-controller-htcondorcern-slurmcern:0.9.0.

Currently it is not possible to further customise the Slurm integration through additional Helm values or environment variables. Please get in touch should you need to customise the Slurm integration further in your REANA deployment.

Here is a customisation snippet example:

compute_backends:
  - kubernetes
  - slurmcern
components:
  reana_job_controller:
    image: docker.io/reanahub/reana-job-controller-htcondorcern-slurmcern:0.9.0