Source code for distributed.deploy.adaptive

from __future__ import print_function, division, absolute_import

from collections import deque
import logging
import math

import toolz
from tornado import gen

from ..metrics import time
from ..utils import log_errors, PeriodicCallback, parse_timedelta

logger = logging.getLogger(__name__)


[docs]class Adaptive(object): ''' Adaptively allocate workers based on scheduler load. A superclass. Contains logic to dynamically resize a Dask cluster based on current use. This class needs to be paired with a system that can create and destroy Dask workers using a cluster resource manager. Typically it is built into already existing solutions, rather than used directly by users. It is most commonly used from the ``.adapt(...)`` method of various Dask cluster classes. Parameters ---------- scheduler: distributed.Scheduler cluster: object Must have scale_up and scale_down methods/coroutines startup_cost : timedelta or str, default "1s" Estimate of the number of seconds for nnFactor representing how costly it is to start an additional worker. Affects quickly to adapt to high tasks per worker loads interval : timedelta or str, default "1000 ms" Milliseconds between checks wait_count: int, default 3 Number of consecutive times that a worker should be suggested for removal before we remove it. scale_factor : int, default 2 Factor to scale by when it's determined additional workers are needed target_duration: timedelta or str, default "5s" Amount of time we want a computation to take. This affects how aggressively we scale up. worker_key: Callable[WorkerState] Function to group workers together when scaling down See Scheduler.workers_to_close for more information minimum: int Minimum number of workers to keep around maximum: int Maximum number of workers to keep around **kwargs: Extra parameters to pass to Scheduler.workers_to_close Examples -------- This is commonly used from existing Dask classes, like KubeCluster >>> from dask_kubernetes import KubeCluster >>> cluster = KubeCluster() >>> cluster.adapt(minimum=10, maximum=100) Alternatively you can use it from your own Cluster class by subclassing from Dask's Cluster superclass >>> from distributed.deploy import Cluster >>> class MyCluster(Cluster): ... def scale_up(self, n): ... """ Bring worker count up to n """ ... def scale_down(self, workers): ... """ Remove worker addresses from cluster """ >>> cluster = MyCluster() >>> cluster.adapt(minimum=10, maximum=100) Notes ----- Subclasses can override :meth:`Adaptive.should_scale_up` and :meth:`Adaptive.workers_to_close` to control when the cluster should be resized. The default implementation checks if there are too many tasks per worker or too little memory available (see :meth:`Adaptive.needs_cpu` and :meth:`Adaptive.needs_memory`). :meth:`Adaptive.get_scale_up_kwargs` method controls the arguments passed to the cluster's ``scale_up`` method. ''' def __init__(self, scheduler, cluster=None, interval='1s', startup_cost='1s', scale_factor=2, minimum=0, maximum=None, wait_count=3, target_duration='5s', worker_key=lambda x: x, **kwargs): interval = parse_timedelta(interval, default='ms') self.worker_key = worker_key self.scheduler = scheduler self.cluster = cluster self.startup_cost = parse_timedelta(startup_cost, default='s') self.scale_factor = scale_factor if self.cluster: self._adapt_callback = PeriodicCallback(self._adapt, interval * 1000, io_loop=scheduler.loop) self.scheduler.loop.add_callback(self._adapt_callback.start) self._adapting = False self._workers_to_close_kwargs = kwargs self.minimum = minimum self.maximum = maximum self.log = deque(maxlen=1000) self.close_counts = {} self.wait_count = wait_count self.target_duration = parse_timedelta(target_duration) self.scheduler.handlers['adaptive_recommendations'] = self.recommendations def stop(self): if self.cluster: self._adapt_callback.stop() self._adapt_callback = None del self._adapt_callback
[docs] def needs_cpu(self): """ Check if the cluster is CPU constrained (too many tasks per core) Notes ----- Returns ``True`` if the occupancy per core is some factor larger than ``startup_cost``. """ total_occupancy = self.scheduler.total_occupancy total_cores = sum([ws.ncores for ws in self.scheduler.workers.values()]) if total_occupancy / (total_cores + 1e-9) > self.startup_cost * 2: logger.info("CPU limit exceeded [%d occupancy / %d cores]", total_occupancy, total_cores) return True else: return False
[docs] def needs_memory(self): """ Check if the cluster is RAM constrained Notes ----- Returns ``True`` if the required bytes in distributed memory is some factor larger than the actual distributed memory available. """ limit_bytes = {addr: ws.memory_limit for addr, ws in self.scheduler.workers.items()} worker_bytes = [ws.nbytes for ws in self.scheduler.workers.values()] limit = sum(limit_bytes.values()) total = sum(worker_bytes) if total > 0.6 * limit: logger.info("Ram limit exceeded [%d/%d]", limit, total) return True else: return False
[docs] def should_scale_up(self): """ Determine whether additional workers should be added to the cluster Returns ------- scale_up : bool Notes ---- Additional workers are added whenever 1. There are unrunnable tasks and no workers 2. The cluster is CPU constrained 3. The cluster is RAM constrained 4. There are fewer workers than our minimum See Also -------- needs_cpu needs_memory """ with log_errors(): if len(self.scheduler.workers) < self.minimum: return True if self.maximum is not None and len(self.scheduler.workers) >= self.maximum: return False if self.scheduler.unrunnable and not self.scheduler.workers: return True needs_cpu = self.needs_cpu() needs_memory = self.needs_memory() if needs_cpu or needs_memory: return True return False
[docs] def workers_to_close(self, **kwargs): """ Determine which, if any, workers should potentially be removed from the cluster. Notes ----- ``Adaptive.workers_to_close`` dispatches to Scheduler.workers_to_close(), but may be overridden in subclasses. Returns ------- List of worker addresses to close, if any See Also -------- Scheduler.workers_to_close """ if len(self.scheduler.workers) <= self.minimum: return [] kw = dict(self._workers_to_close_kwargs) kw.update(kwargs) if self.maximum is not None and len(self.scheduler.workers) > self.maximum: kw['n'] = len(self.scheduler.workers) - self.maximum L = self.scheduler.workers_to_close(**kw) if len(self.scheduler.workers) - len(L) < self.minimum: L = L[:len(self.scheduler.workers) - self.minimum] return L
@gen.coroutine def _retire_workers(self, workers=None): if workers is None: workers = self.workers_to_close(key=self.worker_key, minimum=self.minimum) if not workers: raise gen.Return(workers) with log_errors(): yield self.scheduler.retire_workers(workers=workers, remove=True, close_workers=True) logger.info("Retiring workers %s", workers) f = self.cluster.scale_down(workers) if gen.is_future(f): yield f raise gen.Return(workers)
[docs] def get_scale_up_kwargs(self): """ Get the arguments to be passed to ``self.cluster.scale_up``. Notes ----- By default the desired number of total workers is returned (``n``). Subclasses should ensure that the return dictionary includes a key- value pair for ``n``, either by implementing it or by calling the parent's ``get_scale_up_kwargs``. See Also -------- LocalCluster.scale_up """ target = math.ceil(self.scheduler.total_occupancy / self.target_duration) instances = max(1, len(self.scheduler.workers) * self.scale_factor, target, self.minimum) if self.maximum: instances = min(self.maximum, instances) instances = int(instances) logger.info("Scaling up to %d workers", instances) return {'n': instances}
def recommendations(self, comm=None): should_scale_up = self.should_scale_up() workers = set(self.workers_to_close(key=self.worker_key, minimum=self.minimum)) if should_scale_up and workers: logger.info("Attempting to scale up and scale down simultaneously.") self.close_counts.clear() return {'status': 'error', 'msg': 'Trying to scale up and down simultaneously'} elif should_scale_up: self.close_counts.clear() return toolz.merge({'status': 'up'}, self.get_scale_up_kwargs()) elif workers: d = {} to_close = [] for w, c in self.close_counts.items(): if w in workers: if c >= self.wait_count: to_close.append(w) else: d[w] = c for w in workers: d[w] = d.get(w, 0) + 1 self.close_counts = d if to_close: return {'status': 'down', 'workers': to_close} else: self.close_counts.clear() return None @gen.coroutine def _adapt(self): if self._adapting: # Semaphore to avoid overlapping adapt calls return self._adapting = True try: recommendations = self.recommendations() if not recommendations: return status = recommendations.pop('status') if status == 'up': f = self.cluster.scale_up(**recommendations) self.log.append((time(), 'up', recommendations)) if gen.is_future(f): yield f elif status == 'down': self.log.append((time(), 'down', recommendations['workers'])) workers = yield self._retire_workers(workers=recommendations['workers']) finally: self._adapting = False def adapt(self): self.scheduler.loop.add_callback(self._adapt)