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Plugins

Dask's plugin system enables you to run custom Python code for certain events. You can use plugins that are specific to schedulers, workers, or nannies. A worker plugin, for example, allows you to run custom Python code on all your workers at certain event in the worker's lifecycle (e.g. when the worker process is started). In each section below, you'll see how to create your own plugin or use a Dask-provided built-in plugin.

Scheduler Plugins

.. autoclass:: distributed.diagnostics.plugin.SchedulerPlugin
   :members:

RabbitMQ Example

RabbitMQ is a distributed messaging queue that we can use to post updates about task transitions. By posting transitions to RabbitMQ, we allow other machines to do the processing of transitions and keep scheduler processing to a minimum. See the RabbitMQ tutorial for more information on RabbitMQ and how to consume the messages.

import json
from distributed.diagnostics.plugin import SchedulerPlugin
import pika

class RabbitMQPlugin(SchedulerPlugin):
    def __init__(self):
        # Update host to be your RabbitMQ host
        self.connection = pika.BlockingConnection(
            pika.ConnectionParameters(host='localhost'))
        self.channel = self.connection.channel()
        self.channel.queue_declare(queue='dask_task_status', durable=True)

    def transition(self, key, start, finish, *args, **kwargs):
        message = dict(
            key=key,
            start=start,
            finish=finish,
        )
        self.channel.basic_publish(
            exchange='',
            routing_key='dask_task_status',
            body=json.dumps(message),
            properties=pika.BasicProperties(
                delivery_mode=2,  # make message persistent
            ))

@click.command()
def dask_setup(scheduler):
    plugin = RabbitMQPlugin()
    scheduler.add_plugin(plugin)

Run with: dask scheduler --preload <filename.py>

Accessing Full Task State

If you would like to access the full :class:`distributed.scheduler.TaskState` stored in the scheduler you can do this by passing and storing a reference to the scheduler as so:

from distributed.diagnostics.plugin import SchedulerPlugin

class MyPlugin(SchedulerPlugin):
    def __init__(self, scheduler):
         self.scheduler = scheduler

    def transition(self, key, start, finish, *args, **kwargs):
         # Get full TaskState
         ts = self.scheduler.tasks[key]

@click.command()
def dask_setup(scheduler):
    plugin = MyPlugin(scheduler)
    scheduler.add_plugin(plugin)

Built-In Scheduler Plugins

.. autoclass:: distributed.diagnostics.plugin.PipInstall

.. autoclass:: distributed.diagnostics.plugin.CondaInstall

.. autoclass:: distributed.diagnostics.plugin.InstallPlugin

.. autoclass:: distributed.diagnostics.plugin.SchedulerUploadFile

Worker Plugins

:class:`distributed.diagnostics.plugin.WorkerPlugin` provides a base class for creating your own worker plugins. In addition, Dask provides some :ref:`built-in plugins <plugins.builtin>`.

Watch the video below for an example using a WorkerPlugin to add a :py:class:`concurrent.futures.ProcessPoolExecutor`:

.. autoclass:: distributed.diagnostics.plugin.WorkerPlugin
   :members:

Built-In Worker Plugins

.. autoclass:: distributed.diagnostics.plugin.UploadFile

.. autoclass:: distributed.diagnostics.plugin.ForwardLoggingPlugin

.. autoclass:: distributed.diagnostics.plugin.ForwardOutput

Nanny Plugins

.. autoclass:: distributed.diagnostics.plugin.NannyPlugin
   :members:

Built-In Nanny Plugins

.. autoclass:: distributed.diagnostics.plugin.Environ

.. autoclass:: distributed.diagnostics.plugin.UploadDirectory