Configuration Examples

We include configuration files for known supercomputers. Hopefully these help both other users that use those machines and new users who want to see examples for similar clusters.

Additional examples from other cluster welcome here.

Cheyenne

NCAR’s Cheyenne Supercomputer uses both PBS (for Cheyenne itself) and Slurm (for the attached DAV clusters Geyser/Caldera).

distributed:
  scheduler:
    bandwidth: 1000000000     # GB MB/s estimated worker-worker bandwidth
  worker:
    memory:
      target: 0.90  # Avoid spilling to disk
      spill: False  # Avoid spilling to disk
      pause: 0.80  # fraction at which we pause worker threads
      terminate: 0.95  # fraction at which we terminate the worker
  comm:
    compression: null

jobqueue:
  pbs:
    name: dask-worker
    cores: 36                   # Total number of cores per job
    memory: '109 GB'            # Total amount of memory per job
    processes: 9                # Number of Python processes per job
    interface: ib0              # Network interface to use like eth0 or ib0

    queue: regular
    walltime: '00:30:00'
    resource-spec: select=1:ncpus=36:mem=109GB

  slurm:
    name: dask-worker

    # Dask worker options
    cores: 1                    # Total number of cores per job
    memory: '25 GB'             # Total amount of memory per job
    processes: 1                # Number of Python processes per job

    interface: ib0

    project: PXYZ123
    walltime: '00:30:00'
    job-extra: {-C geyser}

NERSC Cori

NERSC Cori Supercomputer

It should be noted that the the following config file assumes you are running the scheduler on a worker node. Currently the login node appears unable to talk to the worker nodes bidirectionally. As such you need to request an interactive node with the following:

$ salloc -N 1 -C haswell --qos=interactive -t 04:00:00

Then you will run dask jobqueue directly on that interactive node. Note the distributed section that is set up to avoid having dask write to disk. This was due to some weird behavior with the local filesystem.

Alternatively you may use the experimental NERSC jupyterhub which will launch a notebook server on a reserved large memory node of Cori. In this case no special interactive session is needed and dask jobqueue will perform as expected.

distributed:
  worker:
    memory:
      target: False  # Avoid spilling to disk
      spill: False  # Avoid spilling to disk
      pause: 0.80  # fraction at which we pause worker threads
      terminate: 0.95  # fraction at which we terminate the worker

jobqueue:
    slurm:
        cores: 64
        memory: 128GB
        processes: 4
        queue: debug
        walltime: '00:10:00'
        job-extra: ['-C haswell', '-L project, SCRATCH, cscratch1']

ARM Stratus

Department of Energy Atmospheric Radiation Measurement (DOE-ARM) Stratus Supercomputer.

jobqueue:
  pbs:
    name: dask-worker
    cores: 36
    memory: 270GB
    processes: 6
    interface: ib0
    local-directory: $localscratch
    queue: high_mem # Can also select batch or gpu_ssd
    project: arm
    walltime: 00:30:00 #Adjust this to job size
    job-extra: ['-W group_list=cades-arm']