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Dask on DRMAA

Project description

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Deploy a Dask.distributed cluster on top of a cluster running a DRMAA-compliant job scheduler.


Launch from Python

from dask_drmaa import DRMAACluster
cluster = DRMAACluster()

from dask.distributed import Client
client = Client(cluster)

>>> future = client.submit(lambda x: x + 1, 10)
>>> future.result()

Or launch from the command line:

$ dask-drmaa 10  # starts local scheduler and ten remote workers


Currently this is only available through GitHub and source installation:

pip install git+ --upgrade


git clone
cd dask-drmaa
python install

You must have the DRMAA system library installed and be able to submit jobs from your local machine.


This repository contains a Docker-compose testing harness for a Son of Grid Engine cluster with a master and two slaves. You can initialize this system as follows

docker-compose build

And run tests with py.test in the master docker container

docker exec -it sge_master /bin/bash -c "cd /dask-drmaa; python develop"
docker exec -it sge_master py.test dask-drmaa/dask_drmaa --verbose

Adaptive Load

Dask-drmaa can adapt to scheduler load, deploying more workers on the grid when it has more work, and cleaning up these workers when they are no longer necessary. This can simplify setup (you can just leave a cluster running) and it can reduce load on the cluster, making IT happy.

To enable this, call the Adaptive class on a DRMAACluster. You can submit computations to the cluster without ever explicitly creating workers.

from dask_drmaa import DRMAACluster, Adaptive
from dask.distributed import Client

cluster = DRMAACluster()
adapt = Adaptive(cluster)
client = Client(cluster)

futures =, seq)  # workers will be created as necessary


The DRMAA interface is the lowest common denominator among many different job schedulers like SGE, SLURM, LSF, Torque, and others. However, sometimes users need to specify parameters particular to their cluster, such as resource queues, wall times, memory constraints, etc..

DRMAA allows users to pass native specifications either when constructing the cluster or when starting new workers:

cluster = DRMAACluster(template={'nativeSpecification': '-l h_rt=01:00:00'})
# or
cluster.start_workers(10, nativeSpecification='-l h_rt=01:00:00')

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