Local Cluster

For convenience you can start a local cluster from your Python session.

>>> from distributed import Client, LocalCluster
>>> cluster = LocalCluster()
LocalCluster("", workers=8, ncores=8)
>>> client = Client(cluster)
<Client: scheduler= processes=8 cores=8>

You can dynamically scale this cluster up and down:

>>> worker = cluster.add_worker()
>>> cluster.remove_worker(worker)

Alternatively, a LocalCluster is made for you automatically if you create an Client with no arguments:

>>> from distributed import Client
>>> client = Client()
>>> client
<Client: scheduler= processes=8 cores=8>


class distributed.deploy.local.LocalCluster(n_workers=None, threads_per_worker=None, processes=True, loop=None, start=None, ip=None, scheduler_port=0, silence_logs=30, diagnostics_port=8787, services=None, worker_services=None, service_kwargs=None, asynchronous=False, security=None, **worker_kwargs)[source]

Create local Scheduler and Workers

This creates a “cluster” of a scheduler and workers running on the local machine.

n_workers: int

Number of workers to start

processes: bool

Whether to use processes (True) or threads (False). Defaults to True

threads_per_worker: int

Number of threads per each worker

scheduler_port: int

Port of the scheduler. 8786 by default, use 0 to choose a random port

silence_logs: logging level

Level of logs to print out to stdout. logging.WARN by default. Use a falsey value like False or None for no change.

ip: string

IP address on which the scheduler will listen, defaults to only localhost

diagnostics_port: int

Port on which the Web Interface will be provided. 8787 by default, use 0 to choose a random port, None to disable it, or an (ip:port) tuple to listen on a different IP address than the scheduler.

asynchronous: bool (False by default)

Set to True if using this cluster within async/await functions or within Tornado gen.coroutines. This should remain False for normal use.

kwargs: dict

Extra worker arguments, will be passed to the Worker constructor.

service_kwargs: Dict[str, Dict]

Extra keywords to hand to the running services

security : Security


>>> c = LocalCluster()  # Create a local cluster with as many workers as cores  
>>> c  
LocalCluster("", workers=8, ncores=8)
>>> c = Client(c)  # connect to local cluster  

Add a new worker to the cluster

>>> w = c.start_worker(ncores=2)  

Shut down the extra worker

>>> c.stop_worker(w)  

Pass extra keyword arguments to Bokeh

>>> LocalCluster(service_kwargs={'bokeh': {'prefix': '/foo'}})  

Close the cluster


Remove workers from the cluster

Given a list of worker addresses this function should remove those workers from the cluster. This may require tracking which jobs are associated to which worker address.

This can be implemented either as a function or as a Tornado coroutine.

scale_up(n, **kwargs)[source]

Bring the total count of workers up to n

This function/coroutine should bring the total number of workers up to the number n.

This can be implemented either as a function or as a Tornado coroutine.


Add a new worker to the running cluster

port: int (optional)

Port on which to serve the worker, defaults to 0 or random

ncores: int (optional)

Number of threads to use. Defaults to number of logical cores

The created Worker or Nanny object. Can be discarded.


>>> c = LocalCluster()  
>>> c.start_worker(ncores=2)  

Stop a running worker


>>> c = LocalCluster()  
>>> w = c.start_worker(ncores=2)  
>>> c.stop_worker(w)