Anaconda Enterprise: Documentation

# Quickstart

## Install

    $ pip install dask distributed --upgrade

See installation document for more information.

## Setup Dask.distributed the Easy Way

If you create an client without providing an address it will start up a local scheduler and worker for you.

    >>> from dask.distributed import Client
    >>> client = Client()  # set up local cluster on your laptop
    >>> client
    <Client: scheduler="127.0.0.1:8786" processes=8 cores=8>

## Setup Dask.distributed the Hard Way

This allows dask.distributed to use multiple machines as workers.

Set up scheduler and worker processes on your local computer:

    $ dask-scheduler
    Scheduler started at 127.0.0.1:8786

    $ dask-worker 127.0.0.1:8786
    $ dask-worker 127.0.0.1:8786
    $ dask-worker 127.0.0.1:8786

Note

At least one `dask-worker` must be running after launching a scheduler.

Launch an Client and point it to the IP/port of the scheduler.

    >>> from dask.distributed import Client
    >>> client = Client('127.0.0.1:8786')

See setup for advanced use.

### Map and Submit Functions

Use the `map` and `submit` methods to launch computations on the cluster. The `map/submit` functions send the function and arguments to the remote workers for processing. They return `Future` objects that refer to remote data on the cluster. The `Future` returns immediately while the computations run remotely in the background.

    >>> def square(x):
            return x ** 2

    >>> def neg(x):
            return -x

    >>> A = client.map(square, range(10))
    >>> B = client.map(neg, A)
    >>> total = client.submit(sum, B)
    >>> total.result()
    -285

### Gather

The `map/submit` functions return `Future` objects, lightweight tokens that refer to results on the cluster. By default the results of computations stay on the cluster.

    >>> total  # Function hasn't yet completed
    <Future: status: waiting, key: sum-58999c52e0fa35c7d7346c098f5085c7>

    >>> total  # Function completed, result ready on remote worker
    <Future: status: finished, key: sum-58999c52e0fa35c7d7346c098f5085c7>

Gather results to your local machine either with the `Future.result` method for a single future, or with the `Client.gather` method for many futures at once.

    >>> total.result()   # result for single future
    -285
    >>> client.gather(A) # gather for many futures
    [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

### Restart

When things go wrong, or when you want to reset the cluster state, call the `restart` method.

    >>> client.restart()

See client for advanced use.