It is easy to set up Dask on informally managed networks of machines using SSH. This can be done manually using SSH and the Dask command line interface, or automatically using either the SSHCluster Python command or the dask-ssh command line tool. This document describes both of these options.

Python Interface

Command Line

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:

python -m pip install paramiko


The command line documentation here may differ depending on your installed version. We recommend referring to the output of dask-ssh --help.


Launch a distributed cluster over SSH. A ‘dask-scheduler’ process will run on the first host specified in [HOSTNAMES] or in the hostfile (unless –scheduler is specified explicitly). One or more ‘dask-worker’ processes will be run each host in [HOSTNAMES] or in the hostfile. Use command line flags to adjust how many dask-worker process are run on each host (–nprocs) and how many cpus are used by each dask-worker process (–nthreads).

dask-ssh [OPTIONS] [HOSTNAMES]...


--scheduler <scheduler>

Specify scheduler node. Defaults to first address.

--scheduler-port <scheduler_port>

Specify scheduler port number.

--nthreads <nthreads>

Number of threads per worker process. Defaults to number of cores divided by the number of processes per host.

--nprocs <nprocs>

Number of worker processes per host.

--hostfile <hostfile>

Textfile with hostnames/IP addresses

--ssh-username <ssh_username>

Username to use when establishing SSH connections.

--ssh-port <ssh_port>

Port to use for SSH connections.

--ssh-private-key <ssh_private_key>

Private key file to use for SSH connections.


Do not pass the hostname to the worker.

--log-directory <log_directory>

Directory to use on all cluster nodes for the output of dask-scheduler and dask-worker commands.

--local-directory <local_directory>

Directory to use on all cluster nodes to place workers files.

--remote-python <remote_python>

Path to Python on remote nodes.

--memory-limit <memory_limit>

Bytes of memory that the worker can use. This can be an integer (bytes), float (fraction of total system memory), string (like 5GB or 5000M), ‘auto’, or zero for no memory management

--worker-port <worker_port>

Serving computation port, defaults to random

--nanny-port <nanny_port>

Serving nanny port, defaults to random

--remote-dask-worker <remote_dask_worker>

Worker to run.


Show the version and exit.



Optional argument(s)