Setup Network

A dask.distributed network consists of one Scheduler node and several Worker nodes. One can set these up in a variety of ways

Using the Command Line

We launch the dask-scheduler executable in one process and the dask-worker executable in several processes, possibly on different machines.

Launch dask-scheduler on one node:

$ dask-scheduler
Start scheduler at

Then launch dask-worker on the rest of the nodes, providing the address to the node that hosts dask-scheduler:

$ dask-worker
Start worker at:  
Registered with center at:

$ dask-worker
Start worker at:  
Registered with center at:

$ dask-worker
Start worker at:  
Registered with center at:

There are various mechanisms to deploy these executables on a cluster, ranging from manualy SSH-ing into all of the nodes to more automated systems like SGE/SLURM/Torque or Yarn/Mesos. Additionally, cluster SSH tools exist to send the same commands to many machines. One example is tmux-cssh.


  • The scheduler and worker both need to accept TCP connections. By default the scheduler uses port 8786 and the worker binds to a random open port. If you are behind a firewall then you may have to open particular ports or tell Dask to use particular ports with the --port and -worker-port keywords. Other ports like 8787, 8788, and 8789 are also useful to keep open for the diagnostic web interfaces.
  • More information about relevant ports is available by looking at the help pages with dask-scheduler --help and dask-worker --help

Using SSH

The convenience script dask-ssh opens several SSH connections to your target computers and initializes the network accordingly. You can give it a list of hostnames or IP addresses:

$ dask-ssh

Or you can use normal UNIX grouping:

$ dask-ssh 192.168.0.{1,2,3,4}

Or you can specify a hostfile that includes a list of hosts:

$ cat hostfile.txt

$ dask-ssh --hostfile hostfile.txt

The dask-ssh utility depends on the paramiko:

pip install paramiko

Using a Shared Network File System and a Job Scheduler

Some clusters benefit from a shared network file system (NFS) and can use this to communicate the scheduler location to the workers:

dask-scheduler --scheduler-file /path/to/scheduler.json

dask-worker --scheduler-file /path/to/scheduler.json
dask-worker --scheduler-file /path/to/scheduler.json
>>> client = Client(scheduler_file='/path/to/scheduler.json')

This can be particularly useful when deploying dask-scheduler and dask-worker processes using a job scheduler like SGE/SLURM/Torque/etc.. Here is an example using SGE’s qsub command:

# Start a dask-scheduler somewhere and write connection information to file
qsub -b y /path/to/dask-scheduler --scheduler-file /path/to/scheduler.json

# Start 100 dask-worker processes in an array job pointing to the same file
qsub -b y -t 1-100 /path/to/dask-worker --scheduler-file /path/to/scheduler.json

Note, the --scheduler-file option is only valuable if your scheduler and workers share a standard POSIX file system.

Using MPI

You can launch a Dask network using mpirun or mpiexec and the dask-mpi command line executable.

mpirun --np 4 dask-mpi --scheduler-file /path/to/scheduler.json
from dask.distributed import Client
client = Client(scheduler_file='/path/to/scheduler.json')

This depends on the mpi4py library. It only uses MPI to start the Dask cluster, and not for inter-node communication. You may want to specify a high-bandwidth network interface like infiniband using the --interface keyword

mpirun --np 4 dask-mpi --nthreads 1 \
                       --interface ib0 \
                       --scheduler-file /path/to/scheduler.json

Using the Python API

Alternatively you can start up the distributed.scheduler.Scheduler and distributed.worker.Worker objects within a Python session manually.

Start the Scheduler, provide the listening port (defaults to 8786) and Tornado IOLoop (defaults to IOLoop.current())

from distributed import Scheduler
from tornado.ioloop import IOLoop
from threading import Thread

loop = IOLoop.current()
t = Thread(target=loop.start, daemon=True)

s = Scheduler(loop=loop)
s.start('tcp://:8786')   # Listen on TCP port 8786

On other nodes start worker processes that point to the URL of the scheduler.

from distributed import Worker
from tornado.ioloop import IOLoop
from threading import Thread

loop = IOLoop.current()
t = Thread(target=loop.start, daemon=True)

w = Worker('tcp://', loop=loop)
w.start()  # choose randomly assigned port

Alternatively, replace Worker with Nanny if you want your workers to be managed in a separate process by a local nanny process. This allows workers to restart themselves in case of failure, provides some additional monitoring, and is useful when coordinating many workers that should live in different processes to avoid the GIL.

Using LocalCluster

You can do the work above easily using LocalCluster.

from distributed import LocalCluster
c = LocalCluster(processes=False)

A scheduler will be available under c.scheduler and a list of workers under c.workers. There is an IOLoop running in a background thread.

Using Amazon EC2

See the EC2 quickstart for information on the dask-ec2 easy setup script to launch a canned cluster on EC2.

Cluster Resource Managers

Dask.distributed has been deployed on dozens of different cluster resource managers. This section contains links to some external projects, scripts, and instructions that may serve as useful starting points.

Software Environment

The workers and clients should all share the same software environment. That means that they should all have access to the same libraries and that those libraries should be the same version. Dask generally assumes that it can call a function on any worker with the same outcome (unless explicitly told otherwise.)

This is typically enforced through external means, such as by having a network file system (NFS) mount for libraries, by starting the dask-worker processes in equivalent Docker containers, using Conda environments, or through any of the other means typically employed by cluster administrators.



  • Running a dask-scheduler on Windows architectures is supported for only a limited number of workers (roughly 100). This is a detail of the underlying tcp server implementation and is discussed here.
  • Running dask-worker processes on Windows is well supported, performant, and without limit.

If you wish to run in a primarily Windows environment, it is recommneded to run a dask-scheduler on a linux or MacOSX environment, with dask-worker workers on the Windows boxes. This works because the scheduler environment is de-coupled from that of the workers.

Customizing initialization

Both dask-scheduler and dask-worker support a --preload option that allows custom initialization of each scheduler/worker respectively. A module or python file passed as a --preload value is guaranteed to be imported before establishing any connection. A dask_setup(service) function is called if found, with a Scheduler or Worker instance as the argument. As the service stops, dask_teardown(service) is called if present.

As an example, consider the following file that creates a scheduler plugin and registers it with the scheduler

from distributed.diagnostics.plugin import SchedulerPlugin

class MyPlugin(SchedulerPlugin):
    def add_worker(self, scheduler=None, worker=None, **kwargs):
        print("Added a new worker at", worker)

def dask_setup(scheduler):
    plugin = MyPlugin()

We can then run this preload script by referring to its filename (or module name if it is on the path) when we start the scheduler:

dask-scheduler --preload