Asynchronous Operation ====================== Dask can run fully asynchronously and so interoperate with other highly concurrent applications. Internally Dask is built on top of Tornado coroutines but also has a compatibility layer for asyncio (see below). Basic Operation --------------- When starting a client provide the ``asynchronous=True`` keyword to tell Dask that you intend to use this client within an asynchronous context, such as a function defined with ``async/await`` syntax. .. code-block:: python async def f(): client = await Client(asynchronous=True) Operations that used to block now provide Tornado coroutines on which you can ``await``. Fast functions that only submit work remain fast and don't need to be awaited. This includes all functions that submit work to the cluster, like ``submit``, ``map``, ``compute``, and ``persist``. .. code-block:: python future = client.submit(lambda x: x + 1, 10) You can await futures directly .. code-block:: python result = await future >>> print(result) 11 Or you can use the normal client methods. Any operation that waited until it received information from the scheduler should now be ``await``'ed. .. code-block:: python result = await client.gather(future) If you want to use an asynchronous function with a synchronous ``Client`` (one made without the ``asynchronous=True`` keyword) then you can apply the ``asynchronous=True`` keyword at each method call and use the ``Client.sync`` function to run the asynchronous function: .. code-block:: python from dask.distributed import Client client = Client() # normal blocking client async def f(): future = client.submit(lambda x: x + 1, 10) result = await client.gather(future, asynchronous=True) return result client.sync(f) .. note: Blocking operations like the .compute() method aren’t ok to use in asynchronous mode. Instead you’ll have to use the Client.compute method .. code-block:: python async with Client(asynchronous=True) as client: arr = da.random.random((1000, 1000), chunks=(1000, 100)) await client.compute(arr.mean()) Example ------- This self-contained example starts an asynchronous client, submits a trivial job, waits on the result, and then shuts down the client. You can see implementations for Asyncio and Tornado. Python 3 with Tornado or Asyncio ++++++++++++++++++++++++++++++++ .. code-block:: python from dask.distributed import Client async def f(): client = await Client(asynchronous=True) future = client.submit(lambda x: x + 1, 10) result = await future await client.close() return result # Either use Tornado from tornado.ioloop import IOLoop IOLoop().run_sync(f) # Or use asyncio import asyncio asyncio.get_event_loop().run_until_complete(f()) Use Cases --------- Historically this has been used in a few kinds of applications: 1. To integrate Dask into other asynchronous services (such as web backends), supplying a computational engine similar to Celery, but while still maintaining a high degree of concurrency and not blocking needlessly. 2. For computations that change or update state very rapidly, such as is common in some advanced machine learning workloads. 3. To develop the internals of Dask's distributed infrastructure, which is written entirely in this style. 4. For complex control and data structures in advanced applications.