This is the most fundamental way to deploy Dask on multiple machines. In production environments, this process is often automated by some other resource manager. Hence, it is rare that people need to follow these instructions explicitly. Instead, these instructions are useful for IT professionals who may want to set up automated services to deploy Dask within their institution.
dask.distributed network consists of one
dask-scheduler process and
dask-worker processes that connect to that scheduler. These are
normal Python processes that can be executed from the command line. We launch
dask-scheduler executable in one process and the
executable in several processes, possibly on different machines.
To accomplish this, launch
dask-scheduler on one node:
$ dask-scheduler Scheduler at: tcp://18.104.22.168:8786
dask-worker on the rest of the nodes, providing the address to
the node that hosts
$ dask-worker tcp://22.214.171.124:8786 Start worker at: tcp://192.0.0.1:12345 Registered to: tcp://126.96.36.199:8786 $ dask-worker tcp://188.8.131.52:8786 Start worker at: tcp://192.0.0.2:40483 Registered to: tcp://184.108.40.206:8786 $ dask-worker tcp://220.127.116.11:8786 Start worker at: tcp://192.0.0.3:27372 Registered to: tcp://18.104.22.168:8786
The workers connect to the scheduler, which then sets up a long-running network connection back to the worker. The workers will learn the location of other workers from the scheduler.
The scheduler and workers both need to accept TCP connections on an open port.
By default, the scheduler binds to 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 listen on particular ports with the
dask-scheduler --port 8000 dask-worker --bokeh-port 8000 --nanny-port 8001
Dask workers are run within a nanny process that monitors the worker process and restarts it if necessary.
Diagnostic Web Servers¶
Additionally, Dask schedulers and workers host interactive diagnostic web
servers using Bokeh. These are optional, but
generally useful to users. The diagnostic server on the scheduler is
particularly valuable, and is served on port
8787 by default (configurable
For more information about relevant ports, please take a look at the help
dask-scheduler --help and
There are various mechanisms to deploy these executables on a cluster, ranging from manually SSH-ing into all of the machines to more automated systems like SGE/SLURM/Torque or Yarn/Mesos. Additionally, cluster SSH tools exist to send the same commands to many machines. We recommend searching online for “cluster ssh” or “cssh”.
These may be out-dated. We recommend referring to the
--help text of your
$ dask-scheduler --help Usage: dask-scheduler [OPTIONS] Options: --host TEXT URI, IP or hostname of this server --port INTEGER Serving port --interface TEXT Preferred network interface like 'eth0' or 'ib0' --tls-ca-file PATH CA cert(s) file for TLS (in PEM format) --tls-cert PATH certificate file for TLS (in PEM format) --tls-key PATH private key file for TLS (in PEM format) --bokeh-port INTEGER Bokeh port for visual diagnostics --bokeh / --no-bokeh Launch Bokeh Web UI [default: True] --show / --no-show Show web UI --bokeh-whitelist TEXT IP addresses to whitelist for bokeh --bokeh-prefix TEXT Prefix for the bokeh app --use-xheaders BOOLEAN User xheaders in bokeh app for ssl termination in header [default: False] --pid-file TEXT File to write the process PID --scheduler-file TEXT File to write connection information. This may be a good way to share connection information if your cluster is on a shared network file system --local-directory TEXT Directory to place scheduler files --preload TEXT Module that should be loaded by each worker process like "foo.bar" or "/path/to/foo.py" --help Show this message and exit
$ dask-worker --help Usage: dask-worker [OPTIONS] [SCHEDULER] Options: --tls-ca-file PATH CA cert(s) file for TLS (in PEM format) --tls-cert PATH certificate file for TLS (in PEM format) --tls-key PATH private key file for TLS (in PEM format) --worker-port INTEGER Serving computation port, defaults to random --nanny-port INTEGER Serving nanny port, defaults to random --bokeh-port INTEGER Bokeh port, defaults to 8789 --bokeh / --no-bokeh Launch Bokeh Web UI [default: True] --listen-address TEXT The address to which the worker binds. Example: tcp://0.0.0.0:9000 --contact-address TEXT The address the worker advertises to the scheduler for communication with it and other workers. Example: tcp://127.0.0.1:9000 --host TEXT Serving host. Should be an ip address that is visible to the scheduler and other workers. See --listen-address and --contact-address if you need different listen and contact addresses. See --interface --interface TEXT Network interface like 'eth0' or 'ib0' --nthreads INTEGER Number of threads per process --nprocs INTEGER Number of worker processes. Defaults to one --name TEXT A unique name for this worker like 'worker-1' --memory-limit TEXT 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 --reconnect / --no-reconnect Reconnect to scheduler if disconnected --nanny / --no-nanny Start workers in nanny process for management --pid-file TEXT File to write the process PID --local-directory TEXT Directory to place worker files --resources TEXT Resources for task constraints like "GPU=2 MEM=10e9" --scheduler-file TEXT Filename to JSON encoded scheduler information. Use with dask-scheduler --scheduler-file --death-timeout FLOAT Seconds to wait for a scheduler before closing --bokeh-prefix TEXT Prefix for the bokeh app --preload TEXT Module that should be loaded by each worker process like "foo.bar" or "/path/to/foo.py" --help Show this message and exit