Easily deploy Dask on job queuing systems like PBS, Slurm, MOAB, SGE, LSF, and HTCondor.

The Dask-jobqueue project makes it easy to deploy Dask on common job queuing systems typically found in high performance supercomputers, academic research institutions, and other clusters. It provides a convenient interface that is accessible from interactive systems like Jupyter notebooks, or batch jobs.


from dask_jobqueue import PBSCluster
cluster = PBSCluster()
cluster.scale(jobs=10)    # Deploy ten single-node jobs

from dask.distributed import Client
client = Client(cluster)  # Connect this local process to remote workers

# wait for jobs to arrive, depending on the queue, this may take some time

import dask.array as da
x = ...                   # Dask commands now use these distributed resources


Dask jobqueue can also adapt the cluster size dynamically based on current load. This helps to scale up the cluster when necessary but scale it down and save resources when not actively computing.

cluster.adapt(minimum_jobs=10, maximum_jobs=100)  # auto-scale between 10 and 100 jobs
cluster.adapt(maximum_memory="10 TB")  # or use core/memory limits