Source code for distributed.deploy.local

from __future__ import print_function, division, absolute_import

from functools import partial
import logging
import math
from threading import Thread
from time import sleep

from tornado.ioloop import IOLoop
from tornado.iostream import StreamClosedError
from tornado import gen

from ..http.scheduler import HTTPScheduler
from ..utils import sync, ignoring, All
from ..client import Client
from ..nanny import Nanny
from ..scheduler import Scheduler
from ..worker import Worker, _ncores

logger = logging.getLogger(__file__)


[docs]class LocalCluster(object): """ Create local Scheduler and Workers This creates a "cluster" of a scheduler and workers running on the local machine. Parameters ---------- n_workers: int Number of workers to start threads_per_worker: int Number of threads per each worker nanny: boolean If true start the workers in separate processes managed by a nanny. If False keep the workers in the main calling process scheduler_port: int Port of the scheduler. 8786 by default, use 0 to choose a random port kwargs: dict Extra worker arguments, will be passed to the Worker constructor. Examples -------- >>> c = LocalCluster() # Create a local cluster with as many workers as cores # doctest: +SKIP >>> c # doctest: +SKIP LocalCluster("127.0.0.1:8786", workers=8, ncores=8) >>> c = Client(c) # connect to local cluster # doctest: +SKIP Add a new worker to the cluster >>> w = c.start_worker(ncores=2) # doctest: +SKIP Shut down the extra worker >>> c.remove_worker(w) # doctest: +SKIP Start a diagnostic web server and open a new browser tab >>> c.start_diagnostics_server(show=True) # doctest: +SKIP """ def __init__(self, n_workers=None, threads_per_worker=None, nanny=True, loop=None, start=True, scheduler_port=8786, silence_logs=logging.CRITICAL, diagnostics_port=8787, services={'http': HTTPScheduler}, **kwargs): self.status = None self.nanny = nanny if silence_logs: for l in ['distributed.scheduler', 'distributed.worker', 'distributed.core', 'distributed.nanny']: logging.getLogger(l).setLevel(silence_logs) if n_workers is None and threads_per_worker is None: if nanny: n_workers = _ncores threads_per_worker = 1 else: n_workers = 1 threads_per_worker = _ncores if n_workers is None and threads_per_worker is not None: n_workers = max(1, _ncores // threads_per_worker) if n_workers and threads_per_worker is None: # Overcommit threads per worker, rather than undercommit threads_per_worker = max(1, int(math.ceil(_ncores / n_workers))) self.loop = loop or IOLoop() if not self.loop._running: self._thread = Thread(target=self.loop.start) self._thread.daemon = True self._thread.start() while not self.loop._running: sleep(0.001) self.scheduler = Scheduler(loop=self.loop, ip='127.0.0.1', services=services) self.scheduler.start(scheduler_port) self.workers = [] if start: _start_worker = self.start_worker else: _start_worker = partial(self.loop.add_callback, self._start_worker) for i in range(n_workers): _start_worker(ncores=threads_per_worker, nanny=nanny, **kwargs) self.status = 'running' self.diagnostics = None if diagnostics_port is not None: self.start_diagnostics_server(diagnostics_port, silence=silence_logs) def __str__(self): return 'LocalCluster("%s", workers=%d, ncores=%d)' % ( self.scheduler_address, len(self.workers), sum(w.ncores for w in self.workers)) __repr__ = __str__ @gen.coroutine def _start_worker(self, port=0, nanny=None, **kwargs): if nanny is None: nanny = self.nanny if nanny: W = Nanny kwargs['quiet'] = True else: W = Worker w = W(self.scheduler.ip, self.scheduler.port, loop=self.loop, **kwargs) yield w._start(port) self.workers.append(w) while w.worker_address not in self.scheduler.worker_info: yield gen.sleep(0.01) raise gen.Return(w)
[docs] def start_worker(self, port=0, ncores=0, **kwargs): """ Add a new worker to the running cluster Parameters ---------- port: int (optional) Port on which to serve the worker, defaults to 0 or random ncores: int (optional) Number of threads to use. Defaults to number of logical cores nanny: boolean If true start worker in separate process managed by a nanny Examples -------- >>> c = LocalCluster() # doctest: +SKIP >>> c.start_worker(ncores=2) # doctest: +SKIP Returns ------- The created Worker or Nanny object. Can be discarded. """ return sync(self.loop, self._start_worker, port, ncores=ncores, **kwargs)
@gen.coroutine def _stop_worker(self, w): yield w._close() self.workers.remove(w)
[docs] def stop_worker(self, w): """ Stop a running worker Examples -------- >>> c = LocalCluster() # doctest: +SKIP >>> w = c.start_worker(ncores=2) # doctest: +SKIP >>> c.stop_worker(w) # doctest: +SKIP """ sync(self.loop, self._stop_worker, w)
[docs] def start_diagnostics_server(self, port=8787, show=False, silence=logging.CRITICAL): """ Start Diagnostics Web Server This starts a web application to show diagnostics of what is happening on the cluster. This application runs in a separate process and is generally available at the following location: http://localhost:8787/status/ """ try: from distributed.bokeh.application import BokehWebInterface except ImportError: logger.info("To start diagnostics web server please install Bokeh") return assert self.diagnostics is None if 'http' not in self.scheduler.services: self.scheduler.services['http'] = HTTPScheduler(self.scheduler, io_loop=self.scheduler.loop) self.scheduler.services['http'].listen(0) self.diagnostics = BokehWebInterface( tcp_port=self.scheduler.port, http_port=self.scheduler.services['http'].port, bokeh_port=port, show=show, log_level=logging.getLevelName(silence).lower())
@gen.coroutine def _close(self): with ignoring(gen.TimeoutError, StreamClosedError, OSError): yield All([w._close() for w in self.workers]) with ignoring(gen.TimeoutError, StreamClosedError, OSError): yield self.scheduler.close(fast=True) del self.workers[:] if self.diagnostics: self.diagnostics.close()
[docs] def close(self): """ Close the cluster """ if self.status == 'running': self.status = 'closed' if self.loop._running: sync(self.loop, self._close) if hasattr(self, '_thread'): sync(self.loop, self.loop.stop) self._thread.join(timeout=1) self.loop.close() del self._thread
@gen.coroutine
[docs] def scale_up(self, n, **kwargs): """ Bring the total count of workers up to ``n`` This function/coroutine should bring the total number of workers up to the number ``n``. This can be implemented either as a function or as a Tornado coroutine. """ yield [self._start_worker(**kwargs) for i in range(n - len(self.workers))]
@gen.coroutine
[docs] def scale_down(self, workers): """ Remove ``workers`` from the cluster Given a list of worker addresses this function should remove those workers from the cluster. This may require tracking which jobs are associated to which worker address. This can be implemented either as a function or as a Tornado coroutine. """ workers = set(workers) yield [self._stop_worker(w) for w in self.workers if w.worker_address in workers] while workers & set(self.workers): yield gen.sleep(0.01)
def __del__(self): self.close() def __enter__(self): return self def __exit__(self, *args): self.close() @property def scheduler_address(self): return self.scheduler.address