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 192.168.0.1:8786
Then launch dask-worker on the rest of the nodes, providing the address to the
node that hosts dask-scheduler:
$ dask-worker 192.168.0.1:8786
Start worker at: 192.168.0.2:12345
Registered with center at: 192.168.0.1:8786
$ dask-worker 192.168.0.1:8786
Start worker at: 192.168.0.3:12346
Registered with center at: 192.168.0.1:8786
$ dask-worker 192.168.0.1:8786
Start worker at: 192.168.0.4:12347
Registered with center at: 192.168.0.1:8786
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.
Note
- 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
--portand-worker-portkeywords. 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 --helpanddask-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 192.168.0.1 192.168.0.2 192.168.0.3 192.168.0.4
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
192.168.0.1
192.168.0.2
192.168.0.3
192.168.0.4
$ dask-ssh --hostfile hostfile.txt
The dask-ssh utility depends on the paramiko:
pip install paramiko
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)
t.start()
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)
t.start()
w = Worker('tcp://127.0.0.1:8786', 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.
Kubernetes¶
DRMAA (SGE, SLURM, Torque, etc..)¶
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.
Windows¶
Note
- Running a
dask-scheduleron 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-workerprocesses 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
# scheduler-setup.py
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()
scheduler.add_plugin(plugin)
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 scheduler-setup.py