Anaconda Enterprise: Documentation

# Local Cluster

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

    >>> from distributed import Client, LocalCluster
    >>> cluster = LocalCluster()
    LocalCluster("127.0.0.1:8786", workers=8, ncores=8)
    >>> client = Client(cluster)
    <Client: scheduler=127.0.0.1:8786 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=127.0.0.1:8786 processes=8 cores=8>

## API

class `distributed.deploy.local.``LocalCluster`(n_workers=None, threads_per_worker=None, processes=True, loop=None, start=True, ip=None, scheduler_port=0, silence_logs=50, diagnostics_port=8787, services={}, worker_services={}, nanny=None, **worker_kwargs)

  Create local Scheduler and Workers

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

  Parameters:

  - 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.CRITICAL` 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
  - kwargs (dict) – Extra worker arguments, will be passed to the Worker constructor.

  Examples

      >>> c = LocalCluster()  # Create a local cluster with as many workers as cores
      >>> c
      LocalCluster("127.0.0.1:8786", workers=8, ncores=8)

      >>> c = Client(c)  # connect to local cluster

  Add a new worker to the cluster >>> w = c.start_worker(ncores=2) # doctest: +SKIP

  Shut down the extra worker >>> c.remove_worker(w) # doctest: +SKIP

  `close`()

    Close the cluster

  `scale_down`(*args, **kwargs)

    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`(*args, **kwargs)

    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.

  `start_worker`(ncores=0, **kwargs)

    Add a new worker to the running cluster

    Parameters:

    - 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

    Examples

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

    Returns: |
    Return type: | The created Worker or Nanny object. Can be discarded.

  `stop_worker`(w)

    Stop a running worker

    Examples

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