distributed_deep_learning/jobqueue.yaml at main · lukeconibear/distributed_deep_learning · GitHub

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jobqueue:

  sge:
    name: dask-worker

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

    interface: ib0                       # Network interface to use like eth0 or ib0
    death-timeout: 60                    # Number of seconds to wait if a worker can not find a scheduler
    local-directory: null                # Location of fast local storage like /scratch or $TMPDIR

    # SGE resource manager options
    shebang: "#!/usr/bin/env bash"
    queue: null
    project: null
    walltime: '01:00:00'
    extra: []
    env-extra: []
    job-extra: []
    log-directory: null

    resource-spec: null

distributed:
  worker:
    memory:
      target: false # dont spill to disk
      spill: false # dont spill to disk
      pause: 0.80 # pause memory execution at 80% use
      terminate: 0.95 # restart the worker at 95% use