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.. _api:
API
===
.. currentmodule:: distributed
**Client**
The client connects to and submits computation to a Dask cluster (such as a :class:`distributed.LocalCluster`)
.. autosummary::
Client
.. autoautosummary:: distributed.Client
:methods:
.. currentmodule:: distributed
.. autosummary::
worker_client
get_worker
get_client
secede
rejoin
print
warn
Reschedule
.. currentmodule:: distributed.recreate_tasks
.. autosummary::
ReplayTaskClient.recreate_task_locally
ReplayTaskClient.recreate_error_locally
.. currentmodule:: distributed
**Future**
.. autosummary::
Future
.. autoautosummary:: distributed.Future
:methods:
**Synchronization**
.. currentmodule:: distributed
.. autosummary::
Event
Lock
MultiLock
Semaphore
Queue
Variable
**Other**
.. autosummary::
as_completed
distributed.diagnostics.progressbar.progress
wait
fire_and_forget
futures_of
get_task_stream
get_task_metadata
performance_report
**Utilities**
.. autosummary::
distributed.utils.Log
distributed.utils.Logs
Asynchronous methods
--------------------
Most methods and functions can be used equally well within a blocking or
asynchronous environment using Tornado coroutines. If used within a Tornado
IOLoop then you should yield or await otherwise blocking operations
appropriately.
You must tell the client that you intend to use it within an asynchronous
environment by passing the ``asynchronous=True`` keyword
.. code-block:: python
# blocking
client = Client()
future = client.submit(func, *args) # immediate, no blocking/async difference
result = client.gather(future) # blocking
# asynchronous Python 2/3
client = yield Client(asynchronous=True)
future = client.submit(func, *args) # immediate, no blocking/async difference
result = yield client.gather(future) # non-blocking/asynchronous
# asynchronous Python 3
client = await Client(asynchronous=True)
future = client.submit(func, *args) # immediate, no blocking/async difference
result = await client.gather(future) # non-blocking/asynchronous
The asynchronous variants must be run within a Tornado coroutine. See the
:doc:`Asynchronous <asynchronous>` documentation for more information.
Client
------
.. currentmodule:: distributed
.. autoclass:: Client
:members:
.. autoclass:: distributed.recreate_tasks.ReplayTaskClient
:members:
Future
------
.. autoclass:: Future
:members:
Synchronization
---------------
.. autoclass:: Event
:members:
.. autoclass:: Lock
:members:
.. autoclass:: MultiLock
:members:
.. autoclass:: Semaphore
:members:
.. autoclass:: Queue
:members:
.. autoclass:: Variable
:members:
Cluster
-------
Classes relevant for cluster creation and management. Other libraries
(like `dask-jobqueue`_, `dask-gateway`_, `dask-kubernetes`_, `dask-yarn`_ etc.)
provide additional cluster objects.
.. _dask-jobqueue: https://jobqueue.dask.org/
.. _dask-gateway: https://gateway.dask.org/
.. _dask-kubernetes: https://kubernetes.dask.org/
.. _dask-yarn: https://yarn.dask.org/en/latest/
.. autosummary::
LocalCluster
SpecCluster
.. autoclass:: LocalCluster
:members:
.. autoclass:: SpecCluster
:members:
Other
-----
.. autoclass:: as_completed
:members:
.. autofunction:: distributed.diagnostics.progressbar.progress
.. autofunction:: wait
.. autofunction:: fire_and_forget
.. autofunction:: futures_of
.. currentmodule:: distributed
.. autofunction:: distributed.worker_client
.. autofunction:: distributed.get_worker
.. autofunction:: distributed.get_client
.. autofunction:: distributed.secede
.. autofunction:: distributed.rejoin
.. autofunction:: distributed.print
.. autofunction:: distributed.warn
.. autoclass:: distributed.Reschedule
.. autoclass:: get_task_stream
.. autoclass:: get_task_metadata
.. autoclass:: performance_report
Utilities
---------
.. autoclass:: distributed.utils.Log
.. autoclass:: distributed.utils.Logs
Adaptive
--------
.. currentmodule:: distributed.deploy
.. autoclass:: Adaptive
:members:
|
