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Customize Initialization

Customize Initialization

Often we want to run custom code when we start up or tear down a scheduler or worker. We might do this manually with functions like Client.run or Client.run_on_scheduler, but this is error prone and difficult to automate.

To resolve this, Dask includes a few mechanisms to run arbitrary code around the lifecycle of a Scheduler, Worker, Nanny, or Client.

Preload Scripts

Both dask-scheduler and dask-worker support a --preload option that allows custom initialization of each scheduler/worker respectively. A module or Python file passed as a --preload value is guaranteed to be imported before establishing any connection. A dask_setup(service) function is called if found, with a Scheduler, Worker, Nanny, or Client instance as the argument. As the service stops, dask_teardown(service) is called if present.

To support additional configuration, a single --preload module may register additional command-line arguments by exposing dask_setup as a Click command. This command will be used to parse additional arguments provided to dask-worker or dask-scheduler and will be called before service initialization.

Example

As an example, consider the following file that creates a scheduler plugin and registers it with the scheduler

# scheduler-setup.py
import click

from distributed.diagnostics.plugin import SchedulerPlugin

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

    def add_worker(self, scheduler=None, worker=None, **kwargs):
        print("Added a new worker at:", worker)
        if self.print_count and scheduler is not None:
            print("Total workers:", len(scheduler.workers))

@click.command()
@click.option("--print-count/--no-print-count", default=False)
def dask_setup(scheduler, print_count):
    plugin = MyPlugin(print_count)
    scheduler.add_plugin(plugin)

We can then run this preload script by referring to its filename (or module name if it is on the path) when we start the scheduler:

dask-scheduler --preload scheduler-setup.py --print-count

Types

Preloads can be specified as any of the following forms:

  • A path to a script, like /path/to/myfile.py

  • A module name that is on the path, like my_module.initialize

  • The text of a Python script, like import os; os.environ["A"] = "value"

Configuration

Preloads can also be registered with configuration at the following values:

distributed:
  scheduler:
    preload:
    - "import os; os.environ['A'] = 'b'"  # use Python text
    - /path/to/myfile.py                  # or a filename
    - my_module                           # or a module name
    preload-argv:
    - []                                  # Pass optional keywords
    - ["--option", "value"]
    - []
  worker:
    preload: []
    preload-argv: []
  nanny:
    preload: []
    preload-argv: []
  client:
    preload: []
    preload-argv: []

Note

Because the dask-worker command needs to accept keywords for both the Worker and the Nanny (if a nanny is used) it has both a --preload and --preload-nanny keyword. All extra keywords (like --print-count above) will be sent to the workers rather than the nanny. There is no way to specify extra keywords to the nanny preload scripts on the command line. We recommend the use of the more flexible configuration if this is necessary.

Worker Lifecycle Plugins

You can also create a class with setup, teardown, and transition methods, and register that class with the scheduler to give to every worker using the Client.register_worker_plugin method.

Client.register_worker_plugin(plugin[, ...])

Registers a lifecycle worker plugin for all current and future workers.
Client.register_worker_plugin(plugin: distributed.diagnostics.plugin.NannyPlugin | distributed.diagnostics.plugin.WorkerPlugin, name: str | None = None, nanny: bool | None = None)[source]

Registers a lifecycle worker plugin for all current and future workers.

Deprecated since version 2023.9.2: Use Client.register_plugin() instead.

This registers a new object to handle setup, task state transitions and teardown for workers in this cluster. The plugin will instantiate itself on all currently connected workers. It will also be run on any worker that connects in the future.

The plugin may include methods setup, teardown, transition, and release_key. See the dask.distributed.WorkerPlugin class or the examples below for the interface and docstrings. It must be serializable with the pickle or cloudpickle modules.

If the plugin has a name attribute, or if the name= keyword is used then that will control idempotency. If a plugin with that name has already been registered, then it will be removed and replaced by the new one.

For alternatives to plugins, you may also wish to look into preload scripts.

Parameters
pluginWorkerPlugin or NannyPlugin

WorkerPlugin or NannyPlugin instance to register.

namestr, optional

A name for the plugin. Registering a plugin with the same name will have no effect. If plugin has no name attribute a random name is used.

nannybool, optional

Whether to register the plugin with workers or nannies.

See also

distributed.WorkerPlugin
unregister_worker_plugin

Examples

>>> class MyPlugin(WorkerPlugin):
...     def __init__(self, *args, **kwargs):
...         pass  # the constructor is up to you
...     def setup(self, worker: dask.distributed.Worker):
...         pass
...     def teardown(self, worker: dask.distributed.Worker):
...         pass
...     def transition(self, key: str, start: str, finish: str,
...                    **kwargs):
...         pass
...     def release_key(self, key: str, state: str, cause: str | None, reason: None, report: bool):
...         pass

>>> plugin = MyPlugin(1, 2, 3)
>>> client.register_plugin(plugin)

You can get access to the plugin with the get_worker function

>>> client.register_plugin(other_plugin, name='my-plugin')
>>> def f():
...    worker = get_worker()
...    plugin = worker.plugins['my-plugin']
...    return plugin.my_state

>>> future = client.run(f)

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