import logging
import os
import socket
import subprocess
import sys
logger = logging.getLogger(__name__)
[docs]class SchedulerPlugin:
"""Interface to extend the Scheduler
The scheduler operates by triggering and responding to events like
``task_finished``, ``update_graph``, ``task_erred``, etc..
A plugin enables custom code to run at each of those same events. The
scheduler will run the analogous methods on this class when each event is
triggered. This runs user code within the scheduler thread that can
perform arbitrary operations in synchrony with the scheduler itself.
Plugins are often used for diagnostics and measurement, but have full
access to the scheduler and could in principle affect core scheduling.
To implement a plugin implement some of the methods of this class and add
the plugin to the scheduler with ``Scheduler.add_plugin(myplugin)``.
Examples
--------
>>> class Counter(SchedulerPlugin):
... def __init__(self):
... self.counter = 0
...
... def transition(self, key, start, finish, *args, **kwargs):
... if start == 'processing' and finish == 'memory':
... self.counter += 1
...
... def restart(self, scheduler):
... self.counter = 0
>>> plugin = Counter()
>>> scheduler.add_plugin(plugin) # doctest: +SKIP
"""
[docs] async def start(self, scheduler):
"""Run when the scheduler starts up
This runs at the end of the Scheduler startup process
"""
pass
[docs] async def close(self):
"""Run when the scheduler closes down
This runs at the beginning of the Scheduler shutdown process, but after
workers have been asked to shut down gracefully
"""
pass
[docs] def update_graph(self, scheduler, dsk=None, keys=None, restrictions=None, **kwargs):
""" Run when a new graph / tasks enter the scheduler """
[docs] def restart(self, scheduler, **kwargs):
""" Run when the scheduler restarts itself """
[docs] def transition(self, key, start, finish, *args, **kwargs):
"""Run whenever a task changes state
Parameters
----------
key : string
start : string
Start state of the transition.
One of released, waiting, processing, memory, error.
finish : string
Final state of the transition.
*args, **kwargs : More options passed when transitioning
This may include worker ID, compute time, etc.
"""
[docs] def add_worker(self, scheduler=None, worker=None, **kwargs):
""" Run when a new worker enters the cluster """
[docs] def remove_worker(self, scheduler=None, worker=None, **kwargs):
""" Run when a worker leaves the cluster """
[docs] def add_client(self, scheduler=None, client=None, **kwargs):
""" Run when a new client connects """
[docs] def remove_client(self, scheduler=None, client=None, **kwargs):
""" Run when a client disconnects """
[docs]class WorkerPlugin:
"""Interface to extend the Worker
A worker plugin enables custom code to run at different stages of the Workers'
lifecycle: at setup, during task state transitions, when a task or dependency
is released, and at teardown.
A plugin enables custom code to run at each of step of a Workers's life. Whenever such
an event happens, the corresponding method on this class will be called. Note that the
user code always runs within the Worker's main thread.
To implement a plugin implement some of the methods of this class and register
the plugin to your client in order to have it attached to every existing and
future workers with ``Client.register_worker_plugin``.
Examples
--------
>>> class ErrorLogger(WorkerPlugin):
... def __init__(self, logger):
... self.logger = logger
...
... def setup(self, worker):
... self.worker = worker
...
... def transition(self, key, start, finish, *args, **kwargs):
... if finish == 'error':
... exc = self.worker.exceptions[key]
... self.logger.error("Task '%s' has failed with exception: %s" % (key, str(exc)))
>>> plugin = ErrorLogger()
>>> client.register_worker_plugin(plugin) # doctest: +SKIP
"""
[docs] def setup(self, worker):
"""
Run when the plugin is attached to a worker. This happens when the plugin is registered
and attached to existing workers, or when a worker is created after the plugin has been
registered.
"""
[docs] def teardown(self, worker):
""" Run when the worker to which the plugin is attached to is closed """
[docs] def transition(self, key, start, finish, **kwargs):
"""
Throughout the lifecycle of a task (see :doc:`Worker <worker>`), Workers are
instructed by the scheduler to compute certain tasks, resulting in transitions
in the state of each task. The Worker owning the task is then notified of this
state transition.
Whenever a task changes its state, this method will be called.
Parameters
----------
key : string
start : string
Start state of the transition.
One of waiting, ready, executing, long-running, memory, error.
finish : string
Final state of the transition.
kwargs : More options passed when transitioning
"""
[docs] def release_key(self, key, state, cause, reason, report):
"""
Called when the worker releases a task.
Parameters
----------
key : string
state : string
State of the released task.
One of waiting, ready, executing, long-running, memory, error.
cause : string or None
Additional information on what triggered the release of the task.
reason : None
Not used.
report : bool
Whether the worker should report the released task to the scheduler.
"""
[docs] def release_dep(self, dep, state, report):
"""
Called when the worker releases a dependency.
Parameters
----------
dep : string
state : string
State of the released dependency.
One of waiting, flight, memory.
report : bool
Whether the worker should report the released dependency to the scheduler.
"""
[docs]class PipInstall(WorkerPlugin):
"""A Worker Plugin to pip install a set of packages
This accepts a set of packages to install on all workers.
You can also optionally ask for the worker to restart itself after
performing this installation.
.. note::
This will increase the time it takes to start up
each worker. If possible, we recommend including the
libraries in the worker environment or image. This is
primarily intended for experimentation and debugging.
Additional issues may arise if multiple workers share the same
file system. Each worker might try to install the packages
simultaneously.
Parameters
----------
packages : List[str]
A list of strings to place after "pip install" command
pip_options : List[str]
Additional options to pass to pip.
restart : bool, default False
Whether or not to restart the worker after pip installing
Only functions if the worker has an attached nanny process
Examples
--------
>>> from dask.distributed import PipInstall
>>> plugin = PipInstall(packages=["scikit-learn"], pip_options=["--upgrade"])
>>> client.register_worker_plugin(plugin)
"""
name = "pip"
def __init__(self, packages, pip_options=None, restart=False):
self.packages = packages
self.restart = restart
if pip_options is None:
pip_options = []
self.pip_options = pip_options
async def setup(self, worker):
from ..lock import Lock
async with Lock(socket.gethostname()): # don't clobber one installation
logger.info("Pip installing the following packages: %s", self.packages)
proc = subprocess.Popen(
[sys.executable, "-m", "pip", "install"]
+ self.pip_options
+ self.packages,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
)
stdout, stderr = proc.communicate()
returncode = proc.wait()
if returncode:
logger.error("Pip install failed with '%s'", stderr.decode().strip())
return
if self.restart and worker.nanny:
lines = stdout.strip().split(b"\n")
if not all(
line.startswith(b"Requirement already satisfied") for line in lines
):
worker.loop.add_callback(
worker.close_gracefully, restart=True
) # restart
# Adapted from https://github.com/dask/distributed/issues/3560#issuecomment-596138522
[docs]class UploadFile(WorkerPlugin):
"""A WorkerPlugin to upload a local file to workers.
Parameters
----------
filepath: str
A path to the file (.py, egg, or zip) to upload
Examples
--------
>>> from distributed.diagnostics.plugin import UploadFile
>>> client.register_worker_plugin(UploadFile("/path/to/file.py")) # doctest: +SKIP
"""
name = "upload_file"
def __init__(self, filepath):
"""
Initialize the plugin by reading in the data from the given file.
"""
self.filename = os.path.basename(filepath)
with open(filepath, "rb") as f:
self.data = f.read()
async def setup(self, worker):
response = await worker.upload_file(
comm=None, filename=self.filename, data=self.data, load=True
)
assert len(self.data) == response["nbytes"]