docs/source/deploying-python-advanced.rst
.. currentmodule:: distributed
In some rare cases, experts may want to create Scheduler, Worker, and
Nanny objects explicitly in Python. This is often necessary when making
tools to automatically deploy Dask in custom settings.
It is more common to create a :doc:Local cluster with Client() on a single machine <deploying-python> or use the :doc:Command Line Interface (CLI) <deploying-cli>.
New readers are recommended to start there.
If you do want to start Scheduler and Worker objects yourself you should be a
little familiar with async/await style Python syntax. These objects
are awaitable and are commonly used within async with context managers.
Here are a few examples to show a few ways to start and finish things.
.. autosummary:: Scheduler Worker Client
We first start with a comprehensive example of setting up a Scheduler, two Workers, and one Client in the same event loop, running a simple computation, and then cleaning everything up.
.. code-block:: python
import asyncio from dask.distributed import Scheduler, Worker, Client
async def f(): async with Scheduler() as s: async with Worker(s.address) as w1, Worker(s.address) as w2: async with Client(s.address, asynchronous=True) as client: future = client.submit(lambda x: x + 1, 10) result = await future print(result)
asyncio.get_event_loop().run_until_complete(f())
Now we look at simpler examples that build up to this case.
.. autosummary:: Scheduler
We create scheduler by creating a Scheduler() object, and then await
that object to wait for it to start up. We can then wait on the .finished
method to wait until it closes. In the meantime the scheduler will be active
managing the cluster..
.. code-block:: python
import asyncio from dask.distributed import Scheduler, Worker
async def f(): s = Scheduler() # scheduler created, but not yet running s = await s # the scheduler is running await s.finished() # wait until the scheduler closes
asyncio.get_event_loop().run_until_complete(f())
This program will run forever, or until some external process connects to the
scheduler and tells it to stop. If you want to close things yourself you can
close any Scheduler, Worker, Nanny, or Client class by awaiting
the .close method:
.. code-block:: python
await s.close()
.. autosummary:: Worker
The worker follows the same API. The only difference is that the worker needs to know the address of the scheduler.
.. code-block:: python
import asyncio from dask.distributed import Scheduler, Worker
async def f(scheduler_address): w = await Worker(scheduler_address) await w.finished()
asyncio.get_event_loop().run_until_complete(f("tcp://127.0.0.1:8786"))
.. autosummary:: Scheduler Worker
We can run as many of these objects as we like in the same event loop.
.. code-block:: python
import asyncio from dask.distributed import Scheduler, Worker
async def f(): s = await Scheduler() w = await Worker(s.address) await w.finished() await s.finished()
asyncio.get_event_loop().run_until_complete(f())
We can also use async with context managers to make sure that we clean up
properly. Here is the same example as from above:
.. code-block:: python
import asyncio from dask.distributed import Scheduler, Worker
async def f(): async with Scheduler() as s: async with Worker(s.address) as w: await w.finished() await s.finished()
asyncio.get_event_loop().run_until_complete(f())
Alternatively, in the example below we also include a Client, run a small
computation, and then allow things to clean up after that computation..
.. code-block:: python
import asyncio from dask.distributed import Scheduler, Worker, Client
async def f(): async with Scheduler() as s: async with Worker(s.address) as w1, Worker(s.address) as w2: async with Client(s.address, asynchronous=True) as client: future = client.submit(lambda x: x + 1, 10) result = await future print(result)
asyncio.get_event_loop().run_until_complete(f())
This is equivalent to creating and awaiting each server, and then calling
.close on each as we leave the context.
In this example we don't wait on s.finished(), so this will terminate
relatively quickly. You could have called await s.finished() though if you
wanted this to run forever.
.. _nanny:
.. autosummary:: Nanny
Alternatively, we can replace Worker with Nanny if we want your workers
to be managed in a separate process. The Nanny constructor follows the
same API. This allows workers to restart themselves in case of failure. Also,
it provides some additional monitoring, and is useful when coordinating many
workers that should live in different processes in order to avoid the GIL_.
.. code-block:: python
w = await Nanny(s.address)
.. _GIL: https://docs.python.org/3/glossary.html#term-gil
These classes have a variety of keyword arguments that you can use to control their behavior. See the API documentation below for more information.
Scheduler
.. autoclass:: Scheduler
:members:
Worker
~~~~~~
.. autoclass:: Worker
:members:
Nanny
~~~~~
.. autoclass:: Nanny
:members: