Client — Anaconda Enterprise 4.4 documentation

Anaconda Enterprise: Documentation

# Client

The Client is the primary entry point for users of `dask.distributed`.

After we setup a cluster, we initialize a `Client` by pointing it to the address of a `Scheduler`:

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

There are a few different ways to interact with the cluster through the client:

1. The Client satisfies most of the standard concurrent.futures - PEP-3148 interface with `.submit`, `.map` functions and `Future` objects, allowing the immediate and direct submission of tasks.
2. The Client registers itself as the default Dask scheduler, and so runs all dask collections like dask.array, dask.bag, dask.dataframe and dask.delayed
3. The Client has additional methods for manipulating data remotely. See the full API for a thorough list.

## Concurrent.futures

We can submit individual function calls with the `client.submit` method or many function calls with the `client.map` method

    >>> def inc(x):
            return x + 1

    >>> x = client.submit(inc, 10)
    >>> x
    <Future - key: inc-e4853cffcc2f51909cdb69d16dacd1a5>

    >>> L = client.map(inc, range(1000))
    >>> L
    [<Future - key: inc-e4853cffcc2f51909cdb69d16dacd1a5>,
     <Future - key: inc-...>,
     <Future - key: inc-...>,
     <Future - key: inc-...>, ...]

These results live on distributed workers.

We can submit tasks on futures. The function will go to the machine where the futures are stored and run on the result once it has completed.

    >>> y = client.submit(inc, x)      # Submit on x, a Future
    >>> total = client.submit(sum, L)  # Map on L, a list of Futures

We gather back the results using either the `Future.result` method for single futures or `client.gather` method for many futures at once.

    >>> x.result()
    11

    >>> client.gather(L)
    [1, 2, 3, 4, 5, ...]

But, as always, we want to minimize communicating results back to the local process. It’s often best to leave data on the cluster and operate on it remotely with functions like `submit`, `map`, `get` and `compute`. See efficiency for more information on efficient use of distributed.

## Dask

The parent library Dask contains objects like dask.array, dask.dataframe, dask.bag, and dask.delayed, which automatically produce parallel algorithms on larger datasets. All dask collections work smoothly with the distributed scheduler.

When we create a `Client` object it registers itself as the default Dask scheduler. All `.compute()` methods will automatically start using the distributed system.

    client = Client('scheduler:8786')

    my_dataframe.sum().compute()  # Now uses the distributed system by default

We can stop this behavior by using the `set_as_default=False` keyword argument when starting the Client.

Dask’s normal `.compute()` methods are synchronous, meaning that they block the interpreter until they complete. Dask.distributed allows the new ability of asynchronous computing, we can trigger computations to occur in the background and persist in memory while we continue doing other work. This is typically handled with the `Client.persist` and `Client.compute` methods which are used for larger and smaller result sets respectively.

    >>> df = client.persist(df)  # trigger all computations, keep df in memory
    >>> type(df)
    dask.DataFrame

For more information see the page on Managing Computation.

## Pure Functions by Default

By default we assume that all functions are pure. If this is not the case we should use the `pure=False` keyword argument.

The client associates a key to all computations. This key is accessible on the Future object.

    >>> from operator import add
    >>> x = client.submit(add, 1, 2)
    >>> x.key
    'add-ebf39f96ad7174656f97097d658f3fa2'

This key should be the same across all computations with the same inputs and across all machines. If we run the computation above on any computer with the same environment then we should get the exact same key.

The scheduler avoids redundant computations. If the result is already in memory from a previous call then that old result will be used rather than recomputing it. Calls to submit or map are idempotent in the common case.

While convenient, this feature may be undesired for impure functions, like `random`. In these cases two calls to the same function with the same inputs should produce different results. We accomplish this with the `pure=False` keyword argument. In this case keys are randomly generated (by `uuid4`.)

    >>> import numpy as np
    >>> client.submit(np.random.random, 1000, pure=False).key
    'random_sample-fc814a39-ee00-42f3-8b6f-cac65bcb5556'
    >>> client.submit(np.random.random, 1000, pure=False).key
    'random_sample-a24e7220-a113-47f2-a030-72209439f093'

## Tornado Coroutines

If we are operating in an asynchronous environment then the blocking functions listed above become asynchronous equivalents. You must start your client with the `asynchronous=True` keyword and `yield` or `await` blocking functions.

    @gen.coroutine
    def f():
        client = yield Client(asynchronous=True)
        future = client.submit(func, *args)
        result = yield future
        return result

If you want to reuse the same client in asynchronous and synchronous environments you can apply the `asynchronous=True` keyword at each method call.

    client = Client()  # normal blocking client

    @gen.coroutine
    def f():
        futures = client.map(func, L)
        results = yield client.gather(futures, asynchronous=True)
        return results

See the Asynchronous documentation for more information.

## Additional Links

For more information on how to use dask.distributed you may want to look at the following pages:

- Managing Memory
- Managing Computation
- Data Locality
- API