Source code for distributed.worker

import asyncio
import bisect
from collections import defaultdict, deque, namedtuple
from collections.abc import MutableMapping
from datetime import timedelta
import heapq
import logging
import os
from pickle import PicklingError
import random
import threading
import sys
import uuid
import warnings
import weakref

import dask
from dask.core import istask
from dask.compatibility import apply
from dask.utils import format_bytes, funcname

try:
    from cytoolz import pluck, partial, merge, first, keymap
except ImportError:
    from toolz import pluck, partial, merge, first, keymap
from tornado import gen
from tornado.ioloop import IOLoop

from . import profile, comm, system
from .batched import BatchedSend
from .comm import get_address_host, connect
from .comm.addressing import address_from_user_args
from .core import error_message, CommClosedError, send_recv, pingpong, coerce_to_address
from .diskutils import WorkSpace
from .metrics import time
from .node import ServerNode
from .preloading import preload_modules
from .proctitle import setproctitle
from .protocol import pickle, to_serialize, deserialize_bytes, serialize_bytelist
from .pubsub import PubSubWorkerExtension
from .security import Security
from .sizeof import safe_sizeof as sizeof
from .threadpoolexecutor import ThreadPoolExecutor, secede as tpe_secede
from .utils import (
    get_ip,
    typename,
    has_arg,
    _maybe_complex,
    log_errors,
    ignoring,
    import_file,
    silence_logging,
    thread_state,
    json_load_robust,
    key_split,
    offload,
    PeriodicCallback,
    parse_bytes,
    parse_timedelta,
    iscoroutinefunction,
    warn_on_duration,
)
from .utils_comm import pack_data, gather_from_workers
from .utils_perf import ThrottledGC, enable_gc_diagnosis, disable_gc_diagnosis

logger = logging.getLogger(__name__)

LOG_PDB = dask.config.get("distributed.admin.pdb-on-err")

no_value = "--no-value-sentinel--"

IN_PLAY = ("waiting", "ready", "executing", "long-running")
PENDING = ("waiting", "ready", "constrained")
PROCESSING = ("waiting", "ready", "constrained", "executing", "long-running")
READY = ("ready", "constrained")


DEFAULT_EXTENSIONS = [PubSubWorkerExtension]

DEFAULT_METRICS = {}

DEFAULT_STARTUP_INFORMATION = {}

SerializedTask = namedtuple("SerializedTask", ["function", "args", "kwargs", "task"])


[docs]class Worker(ServerNode): """ Worker node in a Dask distributed cluster Workers perform two functions: 1. **Serve data** from a local dictionary 2. **Perform computation** on that data and on data from peers Workers keep the scheduler informed of their data and use that scheduler to gather data from other workers when necessary to perform a computation. You can start a worker with the ``dask-worker`` command line application:: $ dask-worker scheduler-ip:port Use the ``--help`` flag to see more options:: $ dask-worker --help The rest of this docstring is about the internal state the the worker uses to manage and track internal computations. **State** **Informational State** These attributes don't change significantly during execution. * **nthreads:** ``int``: Number of nthreads used by this worker process * **executor:** ``concurrent.futures.ThreadPoolExecutor``: Executor used to perform computation * **local_directory:** ``path``: Path on local machine to store temporary files * **scheduler:** ``rpc``: Location of scheduler. See ``.ip/.port`` attributes. * **name:** ``string``: Alias * **services:** ``{str: Server}``: Auxiliary web servers running on this worker * **service_ports:** ``{str: port}``: * **total_out_connections**: ``int`` The maximum number of concurrent outgoing requests for data * **total_in_connections**: ``int`` The maximum number of concurrent incoming requests for data * **total_comm_nbytes**: ``int`` * **batched_stream**: ``BatchedSend`` A batched stream along which we communicate to the scheduler * **log**: ``[(message)]`` A structured and queryable log. See ``Worker.story`` **Volatile State** This attributes track the progress of tasks that this worker is trying to complete. In the descriptions below a ``key`` is the name of a task that we want to compute and ``dep`` is the name of a piece of dependent data that we want to collect from others. * **data:** ``{key: object}``: Prefer using the **host** attribute instead of this, unless memory_limit and at least one of memory_target_fraction or memory_spill_fraction values are defined, in that case, this attribute is a zict.Buffer, from which information on LRU cache can be queried. * **data.memory:** ``{key: object}``: Dictionary mapping keys to actual values stored in memory. Only available if condition for **data** being a zict.Buffer is met. * **data.disk:** ``{key: object}``: Dictionary mapping keys to actual values stored on disk. Only available if condition for **data** being a zict.Buffer is met. * **task_state**: ``{key: string}``: The state of all tasks that the scheduler has asked us to compute. Valid states include waiting, constrained, executing, memory, erred * **tasks**: ``{key: dict}`` The function, args, kwargs of a task. We run this when appropriate * **dependencies**: ``{key: {deps}}`` The data needed by this key to run * **dependents**: ``{dep: {keys}}`` The keys that use this dependency * **data_needed**: deque(keys) The keys whose data we still lack, arranged in a deque * **waiting_for_data**: ``{kep: {deps}}`` A dynamic verion of dependencies. All dependencies that we still don't have for a particular key. * **ready**: [keys] Keys that are ready to run. Stored in a LIFO stack * **constrained**: [keys] Keys for which we have the data to run, but are waiting on abstract resources like GPUs. Stored in a FIFO deque * **executing**: {keys} Keys that are currently executing * **executed_count**: int A number of tasks that this worker has run in its lifetime * **long_running**: {keys} A set of keys of tasks that are running and have started their own long-running clients. * **dep_state**: ``{dep: string}``: The state of all dependencies required by our tasks Valid states include waiting, flight, and memory * **who_has**: ``{dep: {worker}}`` Workers that we believe have this data * **has_what**: ``{worker: {deps}}`` The data that we care about that we think a worker has * **pending_data_per_worker**: ``{worker: [dep]}`` The data on each worker that we still want, prioritized as a deque * **in_flight_tasks**: ``{task: worker}`` All dependencies that are coming to us in current peer-to-peer connections and the workers from which they are coming. * **in_flight_workers**: ``{worker: {task}}`` The workers from which we are currently gathering data and the dependencies we expect from those connections * **comm_bytes**: ``int`` The total number of bytes in flight * **suspicious_deps**: ``{dep: int}`` The number of times a dependency has not been where we expected it * **nbytes**: ``{key: int}`` The size of a particular piece of data * **types**: ``{key: type}`` The type of a particular piece of data * **threads**: ``{key: int}`` The ID of the thread on which the task ran * **active_threads**: ``{int: key}`` The keys currently running on active threads * **exceptions**: ``{key: exception}`` The exception caused by running a task if it erred * **tracebacks**: ``{key: traceback}`` The exception caused by running a task if it erred * **startstops**: ``{key: [(str, float, float)]}`` Log of transfer, load, and compute times for a task * **priorities**: ``{key: tuple}`` The priority of a key given by the scheduler. Determines run order. * **durations**: ``{key: float}`` Expected duration of a task * **resource_restrictions**: ``{key: {str: number}}`` Abstract resources required to run a task Parameters ---------- scheduler_ip: str scheduler_port: int ip: str, optional data: MutableMapping, type, None The object to use for storage, builds a disk-backed LRU dict by default nthreads: int, optional loop: tornado.ioloop.IOLoop local_directory: str, optional Directory where we place local resources name: str, optional memory_limit: int, float, string Number of bytes of memory that this worker should use. Set to zero for no limit. Set to 'auto' to calculate as system.MEMORY_LIMIT * min(1, nthreads / total_cores) Use strings or numbers like 5GB or 5e9 memory_target_fraction: float Fraction of memory to try to stay beneath memory_spill_fraction: float Fraction of memory at which we start spilling to disk memory_pause_fraction: float Fraction of memory at which we stop running new tasks executor: concurrent.futures.Executor resources: dict Resources that this worker has like ``{'GPU': 2}`` nanny: str Address on which to contact nanny, if it exists lifetime: str Amount of time like "1 hour" after which we gracefully shut down the worker. This defaults to None, meaning no explicit shutdown time. lifetime_stagger: str Amount of time like "5 minutes" to stagger the lifetime value The actual lifetime will be selected uniformly at random between lifetime +/- lifetime_stagger lifetime_restart: bool Whether or not to restart a worker after it has reached its lifetime Default False Examples -------- Use the command line to start a worker:: $ dask-scheduler Start scheduler at 127.0.0.1:8786 $ dask-worker 127.0.0.1:8786 Start worker at: 127.0.0.1:1234 Registered with scheduler at: 127.0.0.1:8786 See Also -------- distributed.scheduler.Scheduler distributed.nanny.Nanny """ _instances = weakref.WeakSet() def __init__( self, scheduler_ip=None, scheduler_port=None, scheduler_file=None, ncores=None, nthreads=None, loop=None, local_dir=None, local_directory=None, services=None, service_ports=None, service_kwargs=None, name=None, reconnect=True, memory_limit="auto", executor=None, resources=None, silence_logs=None, death_timeout=None, preload=None, preload_argv=None, security=None, contact_address=None, memory_monitor_interval="200ms", extensions=None, metrics=DEFAULT_METRICS, startup_information=DEFAULT_STARTUP_INFORMATION, data=None, interface=None, host=None, port=None, protocol=None, dashboard_address=None, nanny=None, plugins=(), low_level_profiler=dask.config.get("distributed.worker.profile.low-level"), validate=False, profile_cycle_interval=None, lifetime=None, lifetime_stagger=None, lifetime_restart=None, **kwargs ): self.tasks = dict() self.task_state = dict() self.dep_state = dict() self.dependencies = dict() self.dependents = dict() self.waiting_for_data = dict() self.who_has = dict() self.has_what = defaultdict(set) self.pending_data_per_worker = defaultdict(deque) self.nanny = nanny self._lock = threading.Lock() self.data_needed = deque() # TODO: replace with heap? self.in_flight_tasks = dict() self.in_flight_workers = dict() self.total_out_connections = dask.config.get( "distributed.worker.connections.outgoing" ) self.total_in_connections = dask.config.get( "distributed.worker.connections.incoming" ) self.total_comm_nbytes = 10e6 self.comm_nbytes = 0 self.suspicious_deps = defaultdict(lambda: 0) self._missing_dep_flight = set() self.nbytes = dict() self.types = dict() self.threads = dict() self.exceptions = dict() self.tracebacks = dict() self.active_threads_lock = threading.Lock() self.active_threads = dict() self.profile_keys = defaultdict(profile.create) self.profile_keys_history = deque(maxlen=3600) self.profile_recent = profile.create() self.profile_history = deque(maxlen=3600) self.priorities = dict() self.generation = 0 self.durations = dict() self.startstops = defaultdict(list) self.resource_restrictions = dict() self.ready = list() self.constrained = deque() self.executing = set() self.executed_count = 0 self.long_running = set() self.batched_stream = None self.recent_messages_log = deque( maxlen=dask.config.get("distributed.comm.recent-messages-log-length") ) self.target_message_size = 50e6 # 50 MB self.log = deque(maxlen=100000) self.validate = validate self._transitions = { ("waiting", "ready"): self.transition_waiting_ready, ("waiting", "memory"): self.transition_waiting_done, ("waiting", "error"): self.transition_waiting_done, ("ready", "executing"): self.transition_ready_executing, ("ready", "memory"): self.transition_ready_memory, ("constrained", "executing"): self.transition_constrained_executing, ("executing", "memory"): self.transition_executing_done, ("executing", "error"): self.transition_executing_done, ("executing", "rescheduled"): self.transition_executing_done, ("executing", "long-running"): self.transition_executing_long_running, ("long-running", "error"): self.transition_executing_done, ("long-running", "memory"): self.transition_executing_done, ("long-running", "rescheduled"): self.transition_executing_done, } self._dep_transitions = { ("waiting", "flight"): self.transition_dep_waiting_flight, ("waiting", "memory"): self.transition_dep_waiting_memory, ("flight", "waiting"): self.transition_dep_flight_waiting, ("flight", "memory"): self.transition_dep_flight_memory, } self.incoming_transfer_log = deque(maxlen=100000) self.incoming_count = 0 self.outgoing_transfer_log = deque(maxlen=100000) self.outgoing_count = 0 self.outgoing_current_count = 0 self.repetitively_busy = 0 self.bandwidth = parse_bytes(dask.config.get("distributed.scheduler.bandwidth")) self.bandwidth_workers = defaultdict( lambda: (0, 0) ) # bw/count recent transfers self.bandwidth_types = defaultdict(lambda: (0, 0)) # bw/count recent transfers self.latency = 0.001 self._client = None if profile_cycle_interval is None: profile_cycle_interval = dask.config.get("distributed.worker.profile.cycle") profile_cycle_interval = parse_timedelta(profile_cycle_interval, default="ms") self._setup_logging(logger) if scheduler_file: cfg = json_load_robust(scheduler_file) scheduler_addr = cfg["address"] elif scheduler_ip is None and dask.config.get("scheduler-address", None): scheduler_addr = dask.config.get("scheduler-address") elif scheduler_port is None: scheduler_addr = coerce_to_address(scheduler_ip) else: scheduler_addr = coerce_to_address((scheduler_ip, scheduler_port)) self.contact_address = contact_address # Target interface on which we contact the scheduler by default # TODO: it is unfortunate that we special-case inproc here if not host and not interface and not scheduler_addr.startswith("inproc://"): host = get_ip(get_address_host(scheduler_addr)) self._start_address = address_from_user_args( host=host, port=port, interface=interface, protocol=protocol, security=security, ) if ncores is not None: warnings.warn("the ncores= parameter has moved to nthreads=") nthreads = ncores self.nthreads = nthreads or system.CPU_COUNT self.total_resources = resources or {} self.available_resources = (resources or {}).copy() self.death_timeout = parse_timedelta(death_timeout) self.preload = preload if self.preload is None: self.preload = dask.config.get("distributed.worker.preload") self.preload_argv = preload_argv if self.preload_argv is None: self.preload_argv = dask.config.get("distributed.worker.preload-argv") self.memory_monitor_interval = parse_timedelta( memory_monitor_interval, default="ms" ) self.extensions = dict() if silence_logs: silence_logging(level=silence_logs) if local_dir is not None: warnings.warn("The local_dir keyword has moved to local_directory") local_directory = local_dir if local_directory is None: local_directory = dask.config.get("temporary-directory") or os.getcwd() if not os.path.exists(local_directory): os.mkdir(local_directory) local_directory = os.path.join(local_directory, "dask-worker-space") with warn_on_duration( "1s", "Creating scratch directories is taking a surprisingly long time. " "This is often due to running workers on a network file system. " "Consider specifying a local-directory to point workers to write " "scratch data to a local disk.", ): self._workspace = WorkSpace(os.path.abspath(local_directory)) self._workdir = self._workspace.new_work_dir(prefix="worker-") self.local_directory = self._workdir.dir_path self.security = security or Security() assert isinstance(self.security, Security) self.connection_args = self.security.get_connection_args("worker") self.listen_args = self.security.get_listen_args("worker") self.memory_limit = parse_memory_limit(memory_limit, self.nthreads) self.paused = False if "memory_target_fraction" in kwargs: self.memory_target_fraction = kwargs.pop("memory_target_fraction") else: self.memory_target_fraction = dask.config.get( "distributed.worker.memory.target" ) if "memory_spill_fraction" in kwargs: self.memory_spill_fraction = kwargs.pop("memory_spill_fraction") else: self.memory_spill_fraction = dask.config.get( "distributed.worker.memory.spill" ) if "memory_pause_fraction" in kwargs: self.memory_pause_fraction = kwargs.pop("memory_pause_fraction") else: self.memory_pause_fraction = dask.config.get( "distributed.worker.memory.pause" ) if isinstance(data, MutableMapping): self.data = data elif callable(data): self.data = data() elif isinstance(data, tuple): self.data = data[0](**data[1]) elif self.memory_limit and ( self.memory_target_fraction or self.memory_spill_fraction ): try: from zict import Buffer, File, Func except ImportError: raise ImportError("Please `pip install zict` for spill-to-disk workers") path = os.path.join(self.local_directory, "storage") storage = Func( partial(serialize_bytelist, on_error="raise"), deserialize_bytes, File(path), ) target = int(float(self.memory_limit) * self.memory_target_fraction) self.data = Buffer({}, storage, target, weight) self.data.memory = self.data.fast self.data.disk = self.data.slow else: self.data = dict() self.actors = {} self.loop = loop or IOLoop.current() self.status = None self.reconnect = reconnect self.executor = executor or ThreadPoolExecutor( self.nthreads, thread_name_prefix="Dask-Worker-Threads'" ) self.actor_executor = ThreadPoolExecutor( 1, thread_name_prefix="Dask-Actor-Threads" ) self.name = name self.scheduler_delay = 0 self.stream_comms = dict() self.heartbeat_active = False self._ipython_kernel = None if self.local_directory not in sys.path: sys.path.insert(0, self.local_directory) self.services = {} self.service_specs = services or {} if dashboard_address is not None: try: from distributed.dashboard import BokehWorker except ImportError: logger.debug("To start diagnostics web server please install Bokeh") else: self.service_specs[("dashboard", dashboard_address)] = ( BokehWorker, (service_kwargs or {}).get("dashboard", {}), ) self.metrics = dict(metrics) if metrics else {} self.startup_information = ( dict(startup_information) if startup_information else {} ) self.low_level_profiler = low_level_profiler handlers = { "gather": self.gather, "run": self.run, "run_coroutine": self.run_coroutine, "get_data": self.get_data, "update_data": self.update_data, "delete_data": self.delete_data, "terminate": self.close, "ping": pingpong, "upload_file": self.upload_file, "start_ipython": self.start_ipython, "call_stack": self.get_call_stack, "profile": self.get_profile, "profile_metadata": self.get_profile_metadata, "get_logs": self.get_logs, "keys": self.keys, "versions": self.versions, "actor_execute": self.actor_execute, "actor_attribute": self.actor_attribute, "plugin-add": self.plugin_add, } stream_handlers = { "close": self.close, "compute-task": self.add_task, "release-task": partial(self.release_key, report=False), "delete-data": self.delete_data, "steal-request": self.steal_request, } super(Worker, self).__init__( handlers=handlers, stream_handlers=stream_handlers, io_loop=self.loop, connection_args=self.connection_args, **kwargs ) self.scheduler = self.rpc(scheduler_addr) self.execution_state = { "scheduler": self.scheduler.address, "ioloop": self.loop, "worker": self, } pc = PeriodicCallback(self.heartbeat, 1000, io_loop=self.io_loop) self.periodic_callbacks["heartbeat"] = pc self._address = contact_address if self.memory_limit: self._memory_monitoring = False pc = PeriodicCallback( self.memory_monitor, self.memory_monitor_interval * 1000, io_loop=self.io_loop, ) self.periodic_callbacks["memory"] = pc if extensions is None: extensions = DEFAULT_EXTENSIONS for ext in extensions: ext(self) self._throttled_gc = ThrottledGC(logger=logger) setproctitle("dask-worker [not started]") pc = PeriodicCallback( self.trigger_profile, parse_timedelta( dask.config.get("distributed.worker.profile.interval"), default="ms" ) * 1000, io_loop=self.io_loop, ) self.periodic_callbacks["profile"] = pc pc = PeriodicCallback( self.cycle_profile, profile_cycle_interval * 1000, io_loop=self.io_loop ) self.periodic_callbacks["profile-cycle"] = pc self.plugins = {} self._pending_plugins = plugins self.lifetime = lifetime or dask.config.get( "distributed.worker.lifetime.duration" ) lifetime_stagger = lifetime_stagger or dask.config.get( "distributed.worker.lifetime.stagger" ) self.lifetime_restart = lifetime_restart or dask.config.get( "distributed.worker.lifetime.restart" ) if isinstance(self.lifetime, str): self.lifetime = parse_timedelta(self.lifetime) if isinstance(lifetime_stagger, str): lifetime_stagger = parse_timedelta(lifetime_stagger) if self.lifetime: self.lifetime += (random.random() * 2 - 1) * lifetime_stagger self.io_loop.call_later(self.lifetime, self.close_gracefully) Worker._instances.add(self) ################## # Administrative # ################## def __repr__(self): return ( "<%s: %s, %s, stored: %d, running: %d/%d, ready: %d, comm: %d, waiting: %d>" % ( self.__class__.__name__, self.address, self.status, len(self.data), len(self.executing), self.nthreads, len(self.ready), len(self.in_flight_tasks), len(self.waiting_for_data), ) ) @property def worker_address(self): """ For API compatibility with Nanny """ return self.address @property def local_dir(self): """ For API compatibility with Nanny """ warnings.warn( "The local_dir attribute has moved to local_directory", stacklevel=2 ) return self.local_directory async def get_metrics(self): core = dict( executing=len(self.executing), in_memory=len(self.data), ready=len(self.ready), in_flight=len(self.in_flight_tasks), bandwidth={ "total": self.bandwidth, "workers": dict(self.bandwidth_workers), "types": keymap(typename, self.bandwidth_types), }, ) custom = {} for k, metric in self.metrics.items(): try: result = metric(self) if hasattr(result, "__await__"): result = await result custom[k] = result except Exception: # TODO: log error once pass return merge(custom, self.monitor.recent(), core) async def get_startup_information(self): result = {} for k, f in self.startup_information.items(): try: v = f(self) if hasattr(v, "__await__"): v = await v result[k] = v except Exception: # TODO: log error once pass return result def identity(self, comm=None): return { "type": type(self).__name__, "id": self.id, "scheduler": self.scheduler.address, "nthreads": self.nthreads, "ncores": self.nthreads, # backwards compatibility "memory_limit": self.memory_limit, } ##################### # External Services # ##################### async def _register_with_scheduler(self): self.periodic_callbacks["heartbeat"].stop() start = time() if self.contact_address is None: self.contact_address = self.address logger.info("-" * 49) while True: try: _start = time() types = {k: typename(v) for k, v in self.data.items()} comm = await connect( self.scheduler.address, connection_args=self.connection_args ) comm.name = "Worker->Scheduler" comm._server = weakref.ref(self) await comm.write( dict( op="register-worker", reply=False, address=self.contact_address, keys=list(self.data), nthreads=self.nthreads, name=self.name, nbytes=self.nbytes, types=types, now=time(), resources=self.total_resources, memory_limit=self.memory_limit, local_directory=self.local_directory, services=self.service_ports, nanny=self.nanny, pid=os.getpid(), metrics=await self.get_metrics(), extra=await self.get_startup_information(), ), serializers=["msgpack"], ) future = comm.read(deserializers=["msgpack"]) response = await future _end = time() middle = (_start + _end) / 2 self.latency = (_end - start) * 0.05 + self.latency * 0.95 self.scheduler_delay = response["time"] - middle self.status = "running" break except EnvironmentError: logger.info("Waiting to connect to: %26s", self.scheduler.address) await gen.sleep(0.1) except gen.TimeoutError: logger.info("Timed out when connecting to scheduler") if response["status"] != "OK": raise ValueError("Unexpected response from register: %r" % (response,)) else: await asyncio.gather( *[ self.plugin_add(plugin=plugin) for plugin in response["worker-plugins"] ] ) logger.info(" Registered to: %26s", self.scheduler.address) logger.info("-" * 49) self.batched_stream = BatchedSend(interval="2ms", loop=self.loop) self.batched_stream.start(comm) pc = PeriodicCallback( lambda: self.batched_stream.send({"op": "keep-alive"}), 60000, io_loop=self.io_loop, ) self.periodic_callbacks["keep-alive"] = pc pc.start() self.periodic_callbacks["heartbeat"].start() self.loop.add_callback(self.handle_scheduler, comm) async def heartbeat(self): if not self.heartbeat_active: self.heartbeat_active = True logger.debug("Heartbeat: %s" % self.address) try: start = time() response = await self.scheduler.heartbeat_worker( address=self.contact_address, now=time(), metrics=await self.get_metrics(), ) end = time() middle = (start + end) / 2 if response["status"] == "missing": await self._register_with_scheduler() return self.scheduler_delay = response["time"] - middle self.periodic_callbacks["heartbeat"].callback_time = ( response["heartbeat-interval"] * 1000 ) self.bandwidth_workers.clear() self.bandwidth_types.clear() except CommClosedError: logger.warning("Heartbeat to scheduler failed") finally: self.heartbeat_active = False else: logger.debug("Heartbeat skipped: channel busy") async def handle_scheduler(self, comm): try: await self.handle_stream( comm, every_cycle=[self.ensure_communicating, self.ensure_computing] ) except Exception as e: logger.exception(e) raise finally: if self.reconnect and self.status == "running": logger.info("Connection to scheduler broken. Reconnecting...") self.loop.add_callback(self.heartbeat) else: await self.close(report=False) def start_ipython(self, comm): """Start an IPython kernel Returns Jupyter connection info dictionary. """ from ._ipython_utils import start_ipython if self._ipython_kernel is None: self._ipython_kernel = start_ipython( ip=self.ip, ns={"worker": self}, log=logger ) return self._ipython_kernel.get_connection_info() async def upload_file(self, comm, filename=None, data=None, load=True): out_filename = os.path.join(self.local_directory, filename) def func(data): if isinstance(data, str): data = data.encode() with open(out_filename, "wb") as f: f.write(data) f.flush() return data if len(data) < 10000: data = func(data) else: data = await offload(func, data) if load: try: import_file(out_filename) except Exception as e: logger.exception(e) return {"status": "error", "exception": to_serialize(e)} return {"status": "OK", "nbytes": len(data)} def keys(self, comm=None): return list(self.data) async def gather(self, comm=None, who_has=None): who_has = { k: [coerce_to_address(addr) for addr in v] for k, v in who_has.items() if k not in self.data } result, missing_keys, missing_workers = await gather_from_workers( who_has, rpc=self.rpc, who=self.address ) if missing_keys: logger.warning( "Could not find data: %s on workers: %s (who_has: %s)", missing_keys, missing_workers, who_has, ) return {"status": "missing-data", "keys": missing_keys} else: self.update_data(data=result, report=False) return {"status": "OK"} ############# # Lifecycle # ############# async def start(self): if self.status and self.status.startswith("clos"): return assert self.status is None, self.status enable_gc_diagnosis() thread_state.on_event_loop_thread = True self.listen(self._start_address, listen_args=self.listen_args) self.ip = get_address_host(self.address) if self.name is None: self.name = self.address preload_modules( self.preload, parameter=self, file_dir=self.local_directory, argv=self.preload_argv, ) # Services listen on all addresses # Note Nanny is not a "real" service, just some metadata # passed in service_ports... self.start_services(self.ip) try: listening_address = "%s%s:%d" % (self.listener.prefix, self.ip, self.port) except Exception: listening_address = "%s%s" % (self.listener.prefix, self.ip) logger.info(" Start worker at: %26s", self.address) logger.info(" Listening to: %26s", listening_address) for k, v in self.service_ports.items(): logger.info(" %16s at: %26s" % (k, self.ip + ":" + str(v))) logger.info("Waiting to connect to: %26s", self.scheduler.address) logger.info("-" * 49) logger.info(" Threads: %26d", self.nthreads) if self.memory_limit: logger.info(" Memory: %26s", format_bytes(self.memory_limit)) logger.info(" Local Directory: %26s", self.local_directory) setproctitle("dask-worker [%s]" % self.address) await asyncio.gather( *[self.plugin_add(plugin=plugin) for plugin in self._pending_plugins] ) self._pending_plugins = () await self._register_with_scheduler() self.start_periodic_callbacks() return self def _close(self, *args, **kwargs): warnings.warn("Worker._close has moved to Worker.close", stacklevel=2) return self.close(*args, **kwargs) async def close( self, report=True, timeout=10, nanny=True, executor_wait=True, safe=False ): with log_errors(): if self.status in ("closed", "closing"): await self.finished() return self.reconnect = False disable_gc_diagnosis() try: logger.info("Stopping worker at %s", self.address) except ValueError: # address not available if already closed logger.info("Stopping worker") if self.status not in ("running", "closing-gracefully"): logger.info("Closed worker has not yet started: %s", self.status) self.status = "closing" if nanny and self.nanny: with self.rpc(self.nanny) as r: await r.close_gracefully() setproctitle("dask-worker [closing]") teardowns = [ plugin.teardown(self) for plugin in self.plugins.values() if hasattr(plugin, "teardown") ] await asyncio.gather(*[td for td in teardowns if hasattr(td, "__await__")]) for pc in self.periodic_callbacks.values(): pc.stop() with ignoring(EnvironmentError, gen.TimeoutError): if report: await gen.with_timeout( timedelta(seconds=timeout), self.scheduler.unregister( address=self.contact_address, safe=safe ), ) self.scheduler.close_rpc() self._workdir.release() for k, v in self.services.items(): v.stop() if self.batched_stream and not self.batched_stream.comm.closed(): self.batched_stream.send({"op": "close-stream"}) if self.batched_stream: self.batched_stream.close() self.actor_executor._work_queue.queue.clear() if isinstance(self.executor, ThreadPoolExecutor): self.executor._work_queue.queue.clear() self.executor.shutdown(wait=executor_wait, timeout=timeout) else: self.executor.shutdown(wait=False) self.actor_executor.shutdown(wait=executor_wait, timeout=timeout) self.stop() self.rpc.close() self.status = "closed" await ServerNode.close(self) setproctitle("dask-worker [closed]") return "OK" async def close_gracefully(self): """ Gracefully shut down a worker This first informs the scheduler that we're shutting down, and asks it to move our data elsewhere. Afterwards, we close as normal """ if self.status.startswith("closing"): await self.finished() if self.status == "closed": return logger.info("Closing worker gracefully: %s", self.address) self.status = "closing-gracefully" await self.scheduler.retire_workers(workers=[self.address], remove=False) await self.close(safe=True, nanny=not self.lifetime_restart) async def terminate(self, comm, report=True, **kwargs): await self.close(report=report, **kwargs) return "OK" async def wait_until_closed(self): warnings.warn("wait_until_closed has moved to finished()") await self.finished() assert self.status == "closed" ################ # Worker Peers # ################ def send_to_worker(self, address, msg): if address not in self.stream_comms: bcomm = BatchedSend(interval="1ms", loop=self.loop) self.stream_comms[address] = bcomm async def batched_send_connect(): comm = await connect( address, connection_args=self.connection_args # TODO, serialization ) comm.name = "Worker->Worker" await comm.write({"op": "connection_stream"}) bcomm.start(comm) self.loop.add_callback(batched_send_connect) self.stream_comms[address].send(msg) async def get_data( self, comm, keys=None, who=None, serializers=None, max_connections=None ): start = time() if max_connections is None: max_connections = self.total_in_connections # Allow same-host connections more liberally if ( max_connections and comm and get_address_host(comm.peer_address) == get_address_host(self.address) ): max_connections = max_connections * 2 if ( max_connections is not False and self.outgoing_current_count > max_connections ): return {"status": "busy"} self.outgoing_current_count += 1 data = {k: self.data[k] for k in keys if k in self.data} if len(data) < len(keys): for k in set(keys) - set(data): if k in self.actors: from .actor import Actor data[k] = Actor(type(self.actors[k]), self.address, k) msg = {"status": "OK", "data": {k: to_serialize(v) for k, v in data.items()}} nbytes = {k: self.nbytes.get(k) for k in data} stop = time() if self.digests is not None: self.digests["get-data-load-duration"].add(stop - start) start = time() try: compressed = await comm.write(msg, serializers=serializers) response = await comm.read(deserializers=serializers) assert response == "OK", response except EnvironmentError: logger.exception( "failed during get data with %s -> %s", self.address, who, exc_info=True ) comm.abort() raise finally: self.outgoing_current_count -= 1 stop = time() if self.digests is not None: self.digests["get-data-send-duration"].add(stop - start) total_bytes = sum(filter(None, nbytes.values())) self.outgoing_count += 1 duration = (stop - start) or 0.5 # windows self.outgoing_transfer_log.append( { "start": start + self.scheduler_delay, "stop": stop + self.scheduler_delay, "middle": (start + stop) / 2, "duration": duration, "who": who, "keys": nbytes, "total": total_bytes, "compressed": compressed, "bandwidth": total_bytes / duration, } ) return "dont-reply" ################### # Local Execution # ################### def update_data(self, comm=None, data=None, report=True, serializers=None): for key, value in data.items(): if key in self.task_state: self.transition(key, "memory", value=value) else: self.put_key_in_memory(key, value) self.task_state[key] = "memory" self.tasks[key] = None self.priorities[key] = None self.durations[key] = None self.dependencies[key] = set() if key in self.dep_state: self.transition_dep(key, "memory", value=value) self.log.append((key, "receive-from-scatter")) if report: self.batched_stream.send({"op": "add-keys", "keys": list(data)}) info = {"nbytes": {k: sizeof(v) for k, v in data.items()}, "status": "OK"} return info async def delete_data(self, comm=None, keys=None, report=True): if keys: for key in list(keys): self.log.append((key, "delete")) if key in self.task_state: self.release_key(key) if key in self.dep_state: self.release_dep(key) logger.debug("Deleted %d keys", len(keys)) if report: logger.debug("Reporting loss of keys to scheduler") # TODO: this route seems to not exist? await self.scheduler.remove_keys( address=self.contact_address, keys=list(keys) ) return "OK" async def set_resources(self, **resources): for r, quantity in resources.items(): if r in self.total_resources: self.available_resources[r] += quantity - self.total_resources[r] else: self.available_resources[r] = quantity self.total_resources[r] = quantity await self.scheduler.set_resources( resources=self.total_resources, worker=self.contact_address ) ################### # Task Management # ################### def add_task( self, key, function=None, args=None, kwargs=None, task=no_value, who_has=None, nbytes=None, priority=None, duration=None, resource_restrictions=None, actor=False, **kwargs2 ): try: if key in self.tasks: state = self.task_state[key] if state == "memory": assert key in self.data or key in self.actors logger.debug( "Asked to compute pre-existing result: %s: %s", key, state ) self.send_task_state_to_scheduler(key) return if state in IN_PLAY: return if state == "erred": del self.exceptions[key] del self.tracebacks[key] if priority is not None: priority = tuple(priority) + (self.generation,) self.generation -= 1 if self.dep_state.get(key) == "memory": self.task_state[key] = "memory" self.send_task_state_to_scheduler(key) self.tasks[key] = None self.log.append((key, "new-task-already-in-memory")) self.priorities[key] = priority self.durations[key] = duration return self.log.append((key, "new")) self.tasks[key] = SerializedTask(function, args, kwargs, task) if actor: self.actors[key] = None self.priorities[key] = priority self.durations[key] = duration if resource_restrictions: self.resource_restrictions[key] = resource_restrictions self.task_state[key] = "waiting" if nbytes is not None: self.nbytes.update(nbytes) who_has = who_has or {} self.dependencies[key] = set(who_has) self.waiting_for_data[key] = set() for dep in who_has: if dep not in self.dependents: self.dependents[dep] = set() self.dependents[dep].add(key) if dep not in self.dep_state: if self.task_state.get(dep) == "memory": state = "memory" else: state = "waiting" self.dep_state[dep] = state self.log.append((dep, "new-dep", state)) if self.dep_state[dep] != "memory": self.waiting_for_data[key].add(dep) for dep, workers in who_has.items(): assert workers if dep not in self.who_has: self.who_has[dep] = set(workers) self.who_has[dep].update(workers) for worker in workers: self.has_what[worker].add(dep) if self.dep_state[dep] != "memory": self.pending_data_per_worker[worker].append(dep) if self.waiting_for_data[key]: self.data_needed.append(key) else: self.transition(key, "ready") if self.validate: if who_has: assert all(dep in self.dep_state for dep in who_has) assert all(dep in self.nbytes for dep in who_has) for dep in who_has: self.validate_dep(dep) self.validate_key(key) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_dep(self, dep, finish, **kwargs): try: start = self.dep_state[dep] except KeyError: return if start == finish: return func = self._dep_transitions[start, finish] state = func(dep, **kwargs) self.log.append(("dep", dep, start, state or finish)) if dep in self.dep_state: self.dep_state[dep] = state or finish if self.validate: self.validate_dep(dep) def transition_dep_waiting_flight(self, dep, worker=None): try: if self.validate: assert dep not in self.in_flight_tasks assert self.dependents[dep] self.in_flight_tasks[dep] = worker except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_dep_flight_waiting(self, dep, worker=None, remove=True): try: if self.validate: assert dep in self.in_flight_tasks del self.in_flight_tasks[dep] if remove: try: self.who_has[dep].remove(worker) except KeyError: pass try: self.has_what[worker].remove(dep) except KeyError: pass if not self.who_has.get(dep): if dep not in self._missing_dep_flight: self._missing_dep_flight.add(dep) self.loop.add_callback(self.handle_missing_dep, dep) for key in self.dependents.get(dep, ()): if self.task_state[key] == "waiting": if remove: # try a new worker immediately self.data_needed.appendleft(key) else: # worker was probably busy, wait a while self.data_needed.append(key) if not self.dependents[dep]: self.release_dep(dep) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_dep_flight_memory(self, dep, value=None): try: if self.validate: assert dep in self.in_flight_tasks del self.in_flight_tasks[dep] if self.dependents[dep]: self.dep_state[dep] = "memory" self.put_key_in_memory(dep, value) self.batched_stream.send({"op": "add-keys", "keys": [dep]}) else: self.release_dep(dep) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_dep_waiting_memory(self, dep, value=None): try: if self.validate: assert dep in self.data assert dep in self.nbytes assert dep in self.types assert self.task_state[dep] == "memory" except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise if value is not no_value and dep not in self.data: self.put_key_in_memory(dep, value, transition=False) def transition(self, key, finish, **kwargs): start = self.task_state[key] if start == finish: return func = self._transitions[start, finish] state = func(key, **kwargs) self.log.append((key, start, state or finish)) self.task_state[key] = state or finish if self.validate: self.validate_key(key) self._notify_transition(key, start, finish, **kwargs) def transition_waiting_ready(self, key): try: if self.validate: assert self.task_state[key] == "waiting" assert key in self.waiting_for_data assert not self.waiting_for_data[key] assert all( dep in self.data or dep in self.actors for dep in self.dependencies[key] ) assert key not in self.executing assert key not in self.ready self.waiting_for_data.pop(key, None) if key in self.resource_restrictions: self.constrained.append(key) return "constrained" else: heapq.heappush(self.ready, (self.priorities[key], key)) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_waiting_done(self, key, value=None): try: if self.validate: assert self.task_state[key] == "waiting" assert key in self.waiting_for_data assert key not in self.executing assert key not in self.ready del self.waiting_for_data[key] self.send_task_state_to_scheduler(key) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_ready_executing(self, key): try: if self.validate: assert key not in self.waiting_for_data # assert key not in self.data assert self.task_state[key] in READY assert key not in self.ready assert all( dep in self.data or dep in self.actors for dep in self.dependencies[key] ) self.executing.add(key) self.loop.add_callback(self.execute, key) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_ready_memory(self, key, value=None): self.send_task_state_to_scheduler(key) def transition_constrained_executing(self, key): self.transition_ready_executing(key) for resource, quantity in self.resource_restrictions[key].items(): self.available_resources[resource] -= quantity if self.validate: assert all(v >= 0 for v in self.available_resources.values()) def transition_executing_done(self, key, value=no_value, report=True): try: if self.validate: assert key in self.executing or key in self.long_running assert key not in self.waiting_for_data assert key not in self.ready out = None if key in self.resource_restrictions: for resource, quantity in self.resource_restrictions[key].items(): self.available_resources[resource] += quantity if self.task_state[key] == "executing": self.executing.remove(key) self.executed_count += 1 elif self.task_state[key] == "long-running": self.long_running.remove(key) if value is not no_value: try: self.task_state[key] = "memory" self.put_key_in_memory(key, value, transition=False) except Exception as e: logger.info("Failed to put key in memory", exc_info=True) msg = error_message(e) self.exceptions[key] = msg["exception"] self.tracebacks[key] = msg["traceback"] self.task_state[key] = "error" out = "error" if key in self.dep_state: self.transition_dep(key, "memory") if report and self.batched_stream and self.status == "running": self.send_task_state_to_scheduler(key) else: raise CommClosedError return out except EnvironmentError: logger.info("Comm closed") except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def transition_executing_long_running(self, key, compute_duration=None): try: if self.validate: assert key in self.executing self.executing.remove(key) self.long_running.add(key) self.batched_stream.send( {"op": "long-running", "key": key, "compute_duration": compute_duration} ) self.ensure_computing() except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def maybe_transition_long_running(self, key, compute_duration=None): if self.task_state.get(key) == "executing": self.transition(key, "long-running", compute_duration=compute_duration) def stateof(self, key): return { "executing": key in self.executing, "waiting_for_data": key in self.waiting_for_data, "heap": key in pluck(1, self.ready), "data": key in self.data, } def story(self, *keys): return [ msg for msg in self.log if any(key in msg for key in keys) or any( key in c for key in keys for c in msg if isinstance(c, (tuple, list, set)) ) ] def ensure_communicating(self): changed = True try: while ( changed and self.data_needed and len(self.in_flight_workers) < self.total_out_connections ): changed = False logger.debug( "Ensure communicating. Pending: %d. Connections: %d/%d", len(self.data_needed), len(self.in_flight_workers), self.total_out_connections, ) key = self.data_needed[0] if key not in self.tasks: self.data_needed.popleft() changed = True continue if self.task_state.get(key) != "waiting": self.log.append((key, "communication pass")) self.data_needed.popleft() changed = True continue deps = self.dependencies[key] if self.validate: assert all(dep in self.dep_state for dep in deps) deps = [dep for dep in deps if self.dep_state[dep] == "waiting"] missing_deps = {dep for dep in deps if not self.who_has.get(dep)} if missing_deps: logger.info("Can't find dependencies for key %s", key) missing_deps2 = { dep for dep in missing_deps if dep not in self._missing_dep_flight } for dep in missing_deps2: self._missing_dep_flight.add(dep) self.loop.add_callback(self.handle_missing_dep, *missing_deps2) deps = [dep for dep in deps if dep not in missing_deps] self.log.append(("gather-dependencies", key, deps)) in_flight = False while deps and ( len(self.in_flight_workers) < self.total_out_connections or self.comm_nbytes < self.total_comm_nbytes ): dep = deps.pop() if self.dep_state[dep] != "waiting": continue if dep not in self.who_has: continue workers = [ w for w in self.who_has[dep] if w not in self.in_flight_workers ] if not workers: in_flight = True continue host = get_address_host(self.address) local = [w for w in workers if get_address_host(w) == host] if local: worker = random.choice(local) else: worker = random.choice(list(workers)) to_gather, total_nbytes = self.select_keys_for_gather(worker, dep) self.comm_nbytes += total_nbytes self.in_flight_workers[worker] = to_gather for d in to_gather: self.transition_dep(d, "flight", worker=worker) self.loop.add_callback( self.gather_dep, worker, dep, to_gather, total_nbytes, cause=key ) changed = True if not deps and not in_flight: self.data_needed.popleft() except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def send_task_state_to_scheduler(self, key): if key in self.data or self.actors.get(key): try: value = self.data[key] except KeyError: value = self.actors[key] nbytes = self.nbytes[key] or sizeof(value) typ = self.types.get(key) or type(value) del value try: typ_serialized = dumps_function(typ) except PicklingError: # Some types fail pickling (example: _thread.lock objects), # send their name as a best effort. typ_serialized = pickle.dumps(typ.__name__) d = { "op": "task-finished", "status": "OK", "key": key, "nbytes": nbytes, "thread": self.threads.get(key), "type": typ_serialized, "typename": typename(typ), } elif key in self.exceptions: d = { "op": "task-erred", "status": "error", "key": key, "thread": self.threads.get(key), "exception": self.exceptions[key], "traceback": self.tracebacks[key], } else: logger.error( "Key not ready to send to worker, %s: %s", key, self.task_state[key] ) return if key in self.startstops: d["startstops"] = self.startstops[key] self.batched_stream.send(d) def put_key_in_memory(self, key, value, transition=True): if key in self.data: return if key in self.actors: self.actors[key] = value else: start = time() self.data[key] = value stop = time() if stop - start > 0.020: self.startstops[key].append(("disk-write", start, stop)) if key not in self.nbytes: self.nbytes[key] = sizeof(value) self.types[key] = type(value) for dep in self.dependents.get(key, ()): if dep in self.waiting_for_data: if key in self.waiting_for_data[dep]: self.waiting_for_data[dep].remove(key) if not self.waiting_for_data[dep]: self.transition(dep, "ready") if transition and key in self.task_state: self.transition(key, "memory") self.log.append((key, "put-in-memory")) def select_keys_for_gather(self, worker, dep): deps = {dep} total_bytes = self.nbytes[dep] L = self.pending_data_per_worker[worker] while L: d = L.popleft() if self.dep_state.get(d) != "waiting": continue if total_bytes + self.nbytes[d] > self.target_message_size: break deps.add(d) total_bytes += self.nbytes[d] return deps, total_bytes async def gather_dep(self, worker, dep, deps, total_nbytes, cause=None): if self.status != "running": return with log_errors(): response = {} try: if self.validate: self.validate_state() # dep states may have changed before gather_dep runs # if a dep is no longer in-flight then don't fetch it deps = tuple(dep for dep in deps if self.dep_state.get(dep) == "flight") self.log.append(("request-dep", dep, worker, deps)) logger.debug("Request %d keys", len(deps)) start = time() response = await get_data_from_worker( self.rpc, deps, worker, who=self.address ) stop = time() if response["status"] == "busy": self.log.append(("busy-gather", worker, deps)) for dep in deps: if self.dep_state.get(dep, None) == "flight": self.transition_dep(dep, "waiting") return if cause: self.startstops[cause].append( ( "transfer", start + self.scheduler_delay, stop + self.scheduler_delay, ) ) total_bytes = sum(self.nbytes.get(dep, 0) for dep in response["data"]) duration = (stop - start) or 0.010 bandwidth = total_bytes / duration self.incoming_transfer_log.append( { "start": start + self.scheduler_delay, "stop": stop + self.scheduler_delay, "middle": (start + stop) / 2.0 + self.scheduler_delay, "duration": duration, "keys": { dep: self.nbytes.get(dep, None) for dep in response["data"] }, "total": total_bytes, "bandwidth": bandwidth, "who": worker, } ) if total_bytes > 1000000: self.bandwidth = self.bandwidth * 0.95 + bandwidth * 0.05 bw, cnt = self.bandwidth_workers[worker] self.bandwidth_workers[worker] = (bw + bandwidth, cnt + 1) types = set(map(type, response["data"].values())) if len(types) == 1: [typ] = types bw, cnt = self.bandwidth_types[typ] self.bandwidth_types[typ] = (bw + bandwidth, cnt + 1) if self.digests is not None: self.digests["transfer-bandwidth"].add(total_bytes / duration) self.digests["transfer-duration"].add(duration) self.counters["transfer-count"].add(len(response["data"])) self.incoming_count += 1 self.log.append(("receive-dep", worker, list(response["data"]))) except EnvironmentError as e: logger.exception("Worker stream died during communication: %s", worker) self.log.append(("receive-dep-failed", worker)) for d in self.has_what.pop(worker): self.who_has[d].remove(worker) if not self.who_has[d]: del self.who_has[d] except Exception as e: logger.exception(e) if self.batched_stream and LOG_PDB: import pdb pdb.set_trace() raise finally: self.comm_nbytes -= total_nbytes busy = response.get("status", "") == "busy" data = response.get("data", {}) for d in self.in_flight_workers.pop(worker): if not busy and d in data: self.transition_dep(d, "memory", value=data[d]) elif self.dep_state.get(d) != "memory": self.transition_dep( d, "waiting", worker=worker, remove=not busy ) if not busy and d not in data and d in self.dependents: self.log.append(("missing-dep", d)) self.batched_stream.send( {"op": "missing-data", "errant_worker": worker, "key": d} ) if self.validate: self.validate_state() self.ensure_computing() if not busy: self.repetitively_busy = 0 self.ensure_communicating() else: # Exponential backoff to avoid hammering scheduler/worker self.repetitively_busy += 1 await gen.sleep(0.100 * 1.5 ** self.repetitively_busy) # See if anyone new has the data await self.query_who_has(dep) self.ensure_communicating() def bad_dep(self, dep): exc = ValueError("Could not find dependent %s. Check worker logs" % str(dep)) for key in self.dependents[dep]: msg = error_message(exc) self.exceptions[key] = msg["exception"] self.tracebacks[key] = msg["traceback"] self.transition(key, "error") self.release_dep(dep) async def handle_missing_dep(self, *deps, **kwargs): original_deps = list(deps) self.log.append(("handle-missing", deps)) try: deps = {dep for dep in deps if dep in self.dependents} if not deps: return for dep in list(deps): suspicious = self.suspicious_deps[dep] if suspicious > 5: deps.remove(dep) self.bad_dep(dep) if not deps: return for dep in deps: logger.info( "Dependent not found: %s %s . Asking scheduler", dep, self.suspicious_deps[dep], ) who_has = await self.scheduler.who_has(keys=list(deps)) who_has = {k: v for k, v in who_has.items() if v} self.update_who_has(who_has) for dep in deps: self.suspicious_deps[dep] += 1 if not who_has.get(dep): self.log.append((dep, "no workers found", self.dependents.get(dep))) self.release_dep(dep) else: self.log.append((dep, "new workers found")) for key in self.dependents.get(dep, ()): if key in self.waiting_for_data: self.data_needed.append(key) except Exception: logger.error("Handle missing dep failed, retrying", exc_info=True) retries = kwargs.get("retries", 5) self.log.append(("handle-missing-failed", retries, deps)) if retries > 0: await self.handle_missing_dep(self, *deps, retries=retries - 1) else: raise finally: try: for dep in original_deps: self._missing_dep_flight.remove(dep) except KeyError: pass self.ensure_communicating() async def query_who_has(self, *deps): with log_errors(): response = await self.scheduler.who_has(keys=deps) self.update_who_has(response) return response def update_who_has(self, who_has): try: for dep, workers in who_has.items(): if not workers: continue if dep in self.who_has: self.who_has[dep].update(workers) else: self.who_has[dep] = set(workers) for worker in workers: self.has_what[worker].add(dep) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def steal_request(self, key): state = self.task_state.get(key, None) response = {"op": "steal-response", "key": key, "state": state} self.batched_stream.send(response) if state in ("ready", "waiting"): self.release_key(key) def release_key(self, key, cause=None, reason=None, report=True): try: if key not in self.task_state: return state = self.task_state.pop(key) if cause: self.log.append((key, "release-key", {"cause": cause})) else: self.log.append((key, "release-key")) del self.tasks[key] if key in self.data and key not in self.dep_state: try: del self.data[key] except FileNotFoundError: logger.error("Tried to delete %s but no file found", exc_info=True) del self.nbytes[key] del self.types[key] if key in self.actors and key not in self.dep_state: del self.actors[key] del self.nbytes[key] del self.types[key] if key in self.waiting_for_data: del self.waiting_for_data[key] for dep in self.dependencies.pop(key, ()): if dep in self.dependents: self.dependents[dep].discard(key) if not self.dependents[dep] and self.dep_state[dep] in ( "waiting", "flight", ): self.release_dep(dep) if key in self.threads: del self.threads[key] del self.priorities[key] del self.durations[key] if key in self.exceptions: del self.exceptions[key] if key in self.tracebacks: del self.tracebacks[key] if key in self.startstops: del self.startstops[key] if key in self.executing: self.executing.remove(key) if key in self.resource_restrictions: if state == "executing": for resource, quantity in self.resource_restrictions[key].items(): self.available_resources[resource] += quantity del self.resource_restrictions[key] if report and state in PROCESSING: # not finished self.batched_stream.send({"op": "release", "key": key, "cause": cause}) except CommClosedError: pass except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def release_dep(self, dep, report=False): try: if dep not in self.dep_state: return self.log.append((dep, "release-dep")) state = self.dep_state.pop(dep) if dep in self.suspicious_deps: del self.suspicious_deps[dep] if dep in self.who_has: for worker in self.who_has.pop(dep): self.has_what[worker].remove(dep) if dep not in self.task_state: if dep in self.data: del self.data[dep] del self.types[dep] if dep in self.actors: del self.actors[dep] del self.types[dep] del self.nbytes[dep] if dep in self.in_flight_tasks: worker = self.in_flight_tasks.pop(dep) self.in_flight_workers[worker].remove(dep) for key in self.dependents.pop(dep, ()): if self.task_state[key] != "memory": self.release_key(key, cause=dep) if report and state == "memory": self.batched_stream.send({"op": "release-worker-data", "keys": [dep]}) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def rescind_key(self, key): try: if self.task_state.get(key) not in PENDING: return del self.task_state[key] del self.tasks[key] if key in self.waiting_for_data: del self.waiting_for_data[key] for dep in self.dependencies.pop(key, ()): self.dependents[dep].remove(key) if not self.dependents[dep]: del self.dependents[dep] if key not in self.dependents: # if key in self.nbytes: # del self.nbytes[key] if key in self.priorities: del self.priorities[key] if key in self.durations: del self.durations[key] except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise ################ # Execute Task # ################ @gen.coroutine def executor_submit(self, key, function, args=(), kwargs=None, executor=None): """ Safely run function in thread pool executor We've run into issues running concurrent.future futures within tornado. Apparently it's advantageous to use timeouts and periodic callbacks to ensure things run smoothly. This can get tricky, so we pull it off into an separate method. """ executor = executor or self.executor job_counter[0] += 1 # logger.info("%s:%d Starts job %d, %s", self.ip, self.port, i, key) kwargs = kwargs or {} future = executor.submit(function, *args, **kwargs) pc = PeriodicCallback( lambda: logger.debug("future state: %s - %s", key, future._state), 1000 ) pc.start() try: yield future finally: pc.stop() result = future.result() # logger.info("Finish job %d, %s", i, key) raise gen.Return(result) def run(self, comm, function, args=(), wait=True, kwargs=None): kwargs = kwargs or {} return run(self, comm, function=function, args=args, kwargs=kwargs, wait=wait) def run_coroutine(self, comm, function, args=(), kwargs=None, wait=True): return run(self, comm, function=function, args=args, kwargs=kwargs, wait=wait) async def plugin_add(self, comm=None, plugin=None, name=None): with log_errors(pdb=False): if isinstance(plugin, bytes): plugin = pickle.loads(plugin) if not name: if hasattr(plugin, "name"): name = plugin.name else: name = funcname(plugin) + "-" + str(uuid.uuid4()) assert name if name in self.plugins: return {"status": "repeat"} else: self.plugins[name] = plugin logger.info("Starting Worker plugin %s" % name) if hasattr(plugin, "setup"): try: result = plugin.setup(worker=self) if hasattr(result, "__await__"): result = await result except Exception as e: msg = error_message(e) return msg return {"status": "OK"} async def actor_execute( self, comm=None, actor=None, function=None, args=(), kwargs={} ): separate_thread = kwargs.pop("separate_thread", True) key = actor actor = self.actors[key] func = getattr(actor, function) name = key_split(key) + "." + function if iscoroutinefunction(func): result = await func(*args, **kwargs) elif separate_thread: result = await self.executor_submit( name, apply_function_actor, args=( func, args, kwargs, self.execution_state, name, self.active_threads, self.active_threads_lock, ), executor=self.actor_executor, ) else: result = func(*args, **kwargs) return {"status": "OK", "result": to_serialize(result)} def actor_attribute(self, comm=None, actor=None, attribute=None): value = getattr(self.actors[actor], attribute) return {"status": "OK", "result": to_serialize(value)} def meets_resource_constraints(self, key): if key not in self.resource_restrictions: return True for resource, needed in self.resource_restrictions[key].items(): if self.available_resources[resource] < needed: return False return True def _maybe_deserialize_task(self, key): if not isinstance(self.tasks[key], SerializedTask): return self.tasks[key] try: start = time() function, args, kwargs = _deserialize(*self.tasks[key]) stop = time() if stop - start > 0.010: self.startstops[key].append(("deserialize", start, stop)) return function, args, kwargs except Exception as e: logger.warning("Could not deserialize task", exc_info=True) emsg = error_message(e) emsg["key"] = key emsg["op"] = "task-erred" self.batched_stream.send(emsg) self.log.append((key, "deserialize-error")) raise def ensure_computing(self): if self.paused: return try: while self.constrained and len(self.executing) < self.nthreads: key = self.constrained[0] if self.task_state.get(key) != "constrained": self.constrained.popleft() continue if self.meets_resource_constraints(key): self.constrained.popleft() try: # Ensure task is deserialized prior to execution self.tasks[key] = self._maybe_deserialize_task(key) except Exception: continue self.transition(key, "executing") else: break while self.ready and len(self.executing) < self.nthreads: _, key = heapq.heappop(self.ready) if self.task_state.get(key) in READY: try: # Ensure task is deserialized prior to execution self.tasks[key] = self._maybe_deserialize_task(key) except Exception: continue self.transition(key, "executing") except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise async def execute(self, key, report=False): executor_error = None if self.status in ("closing", "closed", "closing-gracefully"): return try: if key not in self.executing or key not in self.task_state: return if self.validate: assert key not in self.waiting_for_data assert self.task_state[key] == "executing" function, args, kwargs = self.tasks[key] start = time() data = {} for k in self.dependencies[key]: try: data[k] = self.data[k] except KeyError: from .actor import Actor # TODO: create local actor data[k] = Actor(type(self.actors[k]), self.address, k, self) args2 = pack_data(args, data, key_types=(bytes, str)) kwargs2 = pack_data(kwargs, data, key_types=(bytes, str)) stop = time() if stop - start > 0.005: self.startstops[key].append(("disk-read", start, stop)) if self.digests is not None: self.digests["disk-load-duration"].add(stop - start) logger.debug( "Execute key: %s worker: %s", key, self.address ) # TODO: comment out? try: result = await self.executor_submit( key, apply_function, args=( function, args2, kwargs2, self.execution_state, key, self.active_threads, self.active_threads_lock, self.scheduler_delay, ), ) except RuntimeError as e: executor_error = e raise if self.task_state.get(key) not in ("executing", "long-running"): return result["key"] = key value = result.pop("result", None) self.startstops[key].append(("compute", result["start"], result["stop"])) self.threads[key] = result["thread"] if result["op"] == "task-finished": self.nbytes[key] = result["nbytes"] self.types[key] = result["type"] self.transition(key, "memory", value=value) if self.digests is not None: self.digests["task-duration"].add(result["stop"] - result["start"]) else: if isinstance(result.pop("actual-exception"), Reschedule): self.batched_stream.send({"op": "reschedule", "key": key}) self.transition(key, "rescheduled", report=False) self.release_key(key, report=False) else: self.exceptions[key] = result["exception"] self.tracebacks[key] = result["traceback"] logger.warning( " Compute Failed\n" "Function: %s\n" "args: %s\n" "kwargs: %s\n" "Exception: %s\n", str(funcname(function))[:1000], convert_args_to_str(args2, max_len=1000), convert_kwargs_to_str(kwargs2, max_len=1000), repr(result["exception"].data), ) self.transition(key, "error") logger.debug("Send compute response to scheduler: %s, %s", key, result) if self.validate: assert key not in self.executing assert key not in self.waiting_for_data self.ensure_computing() self.ensure_communicating() except Exception as e: if executor_error is e: logger.error("Thread Pool Executor error: %s", e) else: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise finally: if key in self.executing: self.executing.remove(key) ################## # Administrative # ################## async def memory_monitor(self): """ Track this process's memory usage and act accordingly If we rise above 70% memory use, start dumping data to disk. If we rise above 80% memory use, stop execution of new tasks """ if self._memory_monitoring: return self._memory_monitoring = True total = 0 proc = self.monitor.proc memory = proc.memory_info().rss frac = memory / self.memory_limit # Pause worker threads if above 80% memory use if self.memory_pause_fraction and frac > self.memory_pause_fraction: # Try to free some memory while in paused state self._throttled_gc.collect() if not self.paused: logger.warning( "Worker is at %d%% memory usage. Pausing worker. " "Process memory: %s -- Worker memory limit: %s", int(frac * 100), format_bytes(proc.memory_info().rss), format_bytes(self.memory_limit) if self.memory_limit is not None else "None", ) self.paused = True elif self.paused: logger.warning( "Worker is at %d%% memory usage. Resuming worker. " "Process memory: %s -- Worker memory limit: %s", int(frac * 100), format_bytes(proc.memory_info().rss), format_bytes(self.memory_limit) if self.memory_limit is not None else "None", ) self.paused = False self.ensure_computing() # Dump data to disk if above 70% if self.memory_spill_fraction and frac > self.memory_spill_fraction: target = self.memory_limit * self.memory_target_fraction count = 0 need = memory - target while memory > target: if not self.data.fast: logger.warning( "Memory use is high but worker has no data " "to store to disk. Perhaps some other process " "is leaking memory? Process memory: %s -- " "Worker memory limit: %s", format_bytes(proc.memory_info().rss), format_bytes(self.memory_limit) if self.memory_limit is not None else "None", ) break k, v, weight = self.data.fast.evict() del k, v total += weight count += 1 await gen.sleep(0) memory = proc.memory_info().rss if total > need and memory > target: # Issue a GC to ensure that the evicted data is actually # freed from memory and taken into account by the monitor # before trying to evict even more data. self._throttled_gc.collect() memory = proc.memory_info().rss if count: logger.debug( "Moved %d pieces of data data and %s to disk", count, format_bytes(total), ) self._memory_monitoring = False return total def cycle_profile(self): now = time() + self.scheduler_delay prof, self.profile_recent = self.profile_recent, profile.create() self.profile_history.append((now, prof)) self.profile_keys_history.append((now, dict(self.profile_keys))) self.profile_keys.clear() def trigger_profile(self): """ Get a frame from all actively computing threads Merge these frames into existing profile counts """ if not self.active_threads: # hope that this is thread-atomic? return start = time() with self.active_threads_lock: active_threads = self.active_threads.copy() frames = sys._current_frames() frames = {ident: frames[ident] for ident in active_threads} llframes = {} if self.low_level_profiler: llframes = {ident: profile.ll_get_stack(ident) for ident in active_threads} for ident, frame in frames.items(): if frame is not None: key = key_split(active_threads[ident]) llframe = llframes.get(ident) state = profile.process( frame, True, self.profile_recent, stop="distributed/worker.py" ) profile.llprocess(llframe, None, state) profile.process( frame, True, self.profile_keys[key], stop="distributed/worker.py" ) stop = time() if self.digests is not None: self.digests["profile-duration"].add(stop - start) def get_profile(self, comm=None, start=None, stop=None, key=None): now = time() + self.scheduler_delay if key is None: history = self.profile_history else: history = [(t, d[key]) for t, d in self.profile_keys_history if key in d] if start is None: istart = 0 else: istart = bisect.bisect_left(history, (start,)) if stop is None: istop = None else: istop = bisect.bisect_right(history, (stop,)) + 1 if istop >= len(history): istop = None # include end if istart == 0 and istop is None: history = list(history) else: iistop = len(history) if istop is None else istop history = [history[i] for i in range(istart, iistop)] prof = profile.merge(*pluck(1, history)) if not history: return profile.create() if istop is None and (start is None or start < now): if key is None: recent = self.profile_recent else: recent = self.profile_keys[key] prof = profile.merge(prof, recent) return prof def get_profile_metadata(self, comm=None, start=0, stop=None): if stop is None: add_recent = True now = time() + self.scheduler_delay stop = stop or now start = start or 0 result = { "counts": [ (t, d["count"]) for t, d in self.profile_history if start < t < stop ], "keys": [ (t, {k: d["count"] for k, d in v.items()}) for t, v in self.profile_keys_history if start < t < stop ], } if add_recent: result["counts"].append((now, self.profile_recent["count"])) result["keys"].append( (now, {k: v["count"] for k, v in self.profile_keys.items()}) ) return result def get_call_stack(self, comm=None, keys=None): with self.active_threads_lock: frames = sys._current_frames() active_threads = self.active_threads.copy() frames = {k: frames[ident] for ident, k in active_threads.items()} if keys is not None: frames = {k: frame for k, frame in frames.items() if k in keys} result = {k: profile.call_stack(frame) for k, frame in frames.items()} return result def _notify_transition(self, key, start, finish, **kwargs): for name, plugin in self.plugins.items(): if hasattr(plugin, "transition"): try: plugin.transition(key, start, finish, **kwargs) except Exception: logger.info( "Plugin '%s' failed with exception" % name, exc_info=True ) ############## # Validation # ############## def validate_key_memory(self, key): assert key in self.data or key in self.actors assert key in self.nbytes assert key not in self.waiting_for_data assert key not in self.executing assert key not in self.ready if key in self.dep_state: assert self.dep_state[key] == "memory" def validate_key_executing(self, key): assert key in self.executing assert key not in self.data assert key not in self.waiting_for_data assert all( dep in self.data or dep in self.actors for dep in self.dependencies[key] ) def validate_key_ready(self, key): assert key in pluck(1, self.ready) assert key not in self.data assert key not in self.executing assert key not in self.waiting_for_data assert all( dep in self.data or dep in self.actors for dep in self.dependencies[key] ) def validate_key_waiting(self, key): assert key not in self.data assert not all(dep in self.data for dep in self.dependencies[key]) def validate_key(self, key): try: state = self.task_state[key] if state == "memory": self.validate_key_memory(key) elif state == "waiting": self.validate_key_waiting(key) elif state == "ready": self.validate_key_ready(key) elif state == "executing": self.validate_key_executing(key) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def validate_dep_waiting(self, dep): assert dep not in self.data assert dep in self.nbytes assert self.dependents[dep] assert not any(key in self.ready for key in self.dependents[dep]) def validate_dep_flight(self, dep): assert dep not in self.data assert dep in self.nbytes assert not any(key in self.ready for key in self.dependents[dep]) peer = self.in_flight_tasks[dep] assert dep in self.in_flight_workers[peer] def validate_dep_memory(self, dep): assert dep in self.data or dep in self.actors assert dep in self.nbytes assert dep in self.types if dep in self.task_state: assert self.task_state[dep] == "memory" def validate_dep(self, dep): try: state = self.dep_state[dep] if state == "waiting": self.validate_dep_waiting(dep) elif state == "flight": self.validate_dep_flight(dep) elif state == "memory": self.validate_dep_memory(dep) else: raise ValueError("Unknown dependent state", state) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def validate_state(self): if self.status != "running": return try: for key, workers in self.who_has.items(): for w in workers: assert key in self.has_what[w] for worker, keys in self.has_what.items(): for k in keys: assert worker in self.who_has[k] for key in self.task_state: self.validate_key(key) for dep in self.dep_state: self.validate_dep(dep) for key, deps in self.waiting_for_data.items(): if key not in self.data_needed: for dep in deps: assert ( dep in self.in_flight_tasks or dep in self._missing_dep_flight or self.who_has[dep].issubset(self.in_flight_workers) ) for key in self.tasks: if self.task_state[key] == "memory": assert isinstance(self.nbytes[key], int) assert key not in self.waiting_for_data assert key in self.data or key in self.actors except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise ####################################### # Worker Clients (advanced workloads) # ####################################### @property def client(self): with self._lock: if self._client: return self._client else: return self._get_client() def _get_client(self, timeout=3): """ Get local client attached to this worker If no such client exists, create one See Also -------- get_client """ try: from .client import default_client client = default_client() except ValueError: # no clients found, need to make a new one pass else: if ( client.scheduler and client.scheduler.address == self.scheduler.address or client._start_arg == self.scheduler.address ): self._client = client if not self._client: from .client import Client asynchronous = self.loop is IOLoop.current() self._client = Client( self.scheduler, loop=self.loop, security=self.security, set_as_default=True, asynchronous=asynchronous, direct_to_workers=True, name="worker", timeout=timeout, ) if not asynchronous: assert self._client.status == "running" return self._client def get_current_task(self): """ Get the key of the task we are currently running This only makes sense to run within a task Examples -------- >>> from dask.distributed import get_worker >>> def f(): ... return get_worker().get_current_task() >>> future = client.submit(f) # doctest: +SKIP >>> future.result() # doctest: +SKIP 'f-1234' See Also -------- get_worker """ return self.active_threads[threading.get_ident()]
[docs]def get_worker(): """ Get the worker currently running this task Examples -------- >>> def f(): ... worker = get_worker() # The worker on which this task is running ... return worker.address >>> future = client.submit(f) # doctest: +SKIP >>> future.result() # doctest: +SKIP 'tcp://127.0.0.1:47373' See Also -------- get_client worker_client """ try: return thread_state.execution_state["worker"] except AttributeError: try: return first(w for w in Worker._instances if w.status == "running") except StopIteration: raise ValueError("No workers found")
[docs]def get_client(address=None, timeout=3, resolve_address=True): """Get a client while within a task. This client connects to the same scheduler to which the worker is connected Parameters ---------- address : str, optional The address of the scheduler to connect to. Defaults to the scheduler the worker is connected to. timeout : int, default 3 Timeout (in seconds) for getting the Client resolve_address : bool, default True Whether to resolve `address` to its canonical form. Returns ------- Client Examples -------- >>> def f(): ... client = get_client() ... futures = client.map(lambda x: x + 1, range(10)) # spawn many tasks ... results = client.gather(futures) ... return sum(results) >>> future = client.submit(f) # doctest: +SKIP >>> future.result() # doctest: +SKIP 55 See Also -------- get_worker worker_client secede """ if address and resolve_address: address = comm.resolve_address(address) try: worker = get_worker() except ValueError: # could not find worker pass else: if not address or worker.scheduler.address == address: return worker._get_client(timeout=timeout) from .client import _get_global_client client = _get_global_client() # TODO: assumes the same scheduler if client and (not address or client.scheduler.address == address): return client elif address: from .client import Client return Client(address, timeout=timeout) else: raise ValueError("No global client found and no address provided")
[docs]def secede(): """ Have this task secede from the worker's thread pool This opens up a new scheduling slot and a new thread for a new task. This enables the client to schedule tasks on this node, which is especially useful while waiting for other jobs to finish (e.g., with ``client.gather``). Examples -------- >>> def mytask(x): ... # do some work ... client = get_client() ... futures = client.map(...) # do some remote work ... secede() # while that work happens, remove ourself from the pool ... return client.gather(futures) # return gathered results See Also -------- get_client get_worker """ worker = get_worker() tpe_secede() # have this thread secede from the thread pool duration = time() - thread_state.start_time worker.loop.add_callback( worker.maybe_transition_long_running, thread_state.key, compute_duration=duration, )
[docs]class Reschedule(Exception): """ Reschedule this task Raising this exception will stop the current execution of the task and ask the scheduler to reschedule this task, possibly on a different machine. This does not guarantee that the task will move onto a different machine. The scheduler will proceed through its normal heuristics to determine the optimal machine to accept this task. The machine will likely change if the load across the cluster has significantly changed since first scheduling the task. """ pass
def parse_memory_limit(memory_limit, nthreads, total_cores=system.CPU_COUNT): if memory_limit is None: return None if memory_limit == "auto": memory_limit = int(system.MEMORY_LIMIT * min(1, nthreads / total_cores)) with ignoring(ValueError, TypeError): memory_limit = float(memory_limit) if isinstance(memory_limit, float) and memory_limit <= 1: memory_limit = int(memory_limit * system.MEMORY_LIMIT) if isinstance(memory_limit, str): memory_limit = parse_bytes(memory_limit) else: memory_limit = int(memory_limit) return min(memory_limit, system.MEMORY_LIMIT) async def get_data_from_worker( rpc, keys, worker, who=None, max_connections=None, serializers=None, deserializers=None, ): """ Get keys from worker The worker has a two step handshake to acknowledge when data has been fully delivered. This function implements that handshake. See Also -------- Worker.get_data Worker.gather_deps utils_comm.gather_data_from_workers """ if serializers is None: serializers = rpc.serializers if deserializers is None: deserializers = rpc.deserializers comm = await rpc.connect(worker) comm.name = "Ephemeral Worker->Worker for gather" try: response = await send_recv( comm, serializers=serializers, deserializers=deserializers, op="get_data", keys=keys, who=who, max_connections=max_connections, ) try: status = response["status"] except KeyError: raise ValueError("Unexpected response", response) else: if status == "OK": await comm.write("OK") finally: rpc.reuse(worker, comm) return response job_counter = [0] def _deserialize(function=None, args=None, kwargs=None, task=no_value): """ Deserialize task inputs and regularize to func, args, kwargs """ if function is not None: function = pickle.loads(function) if args: args = pickle.loads(args) if kwargs: kwargs = pickle.loads(kwargs) if task is not no_value: assert not function and not args and not kwargs function = execute_task args = (task,) return function, args or (), kwargs or {} def execute_task(task): """ Evaluate a nested task >>> inc = lambda x: x + 1 >>> execute_task((inc, 1)) 2 >>> execute_task((sum, [1, 2, (inc, 3)])) 7 """ if istask(task): func, args = task[0], task[1:] return func(*map(execute_task, args)) elif isinstance(task, list): return list(map(execute_task, task)) else: return task try: # a 10 MB cache of deserialized functions and their bytes from zict import LRU cache = LRU(10000000, dict(), weight=lambda k, v: len(v)) except ImportError: cache = dict() def dumps_function(func): """ Dump a function to bytes, cache functions """ try: result = cache[func] except KeyError: result = pickle.dumps(func) if len(result) < 100000: cache[func] = result except TypeError: result = pickle.dumps(func) return result def dumps_task(task): """ Serialize a dask task Returns a dict of bytestrings that can each be loaded with ``loads`` Examples -------- Either returns a task as a function, args, kwargs dict >>> from operator import add >>> dumps_task((add, 1)) # doctest: +SKIP {'function': b'\x80\x04\x95\x00\x8c\t_operator\x94\x8c\x03add\x94\x93\x94.' 'args': b'\x80\x04\x95\x07\x00\x00\x00K\x01K\x02\x86\x94.'} Or as a single task blob if it can't easily decompose the result. This happens either if the task is highly nested, or if it isn't a task at all >>> dumps_task(1) # doctest: +SKIP {'task': b'\x80\x04\x95\x03\x00\x00\x00\x00\x00\x00\x00K\x01.'} """ if istask(task): if task[0] is apply and not any(map(_maybe_complex, task[2:])): d = {"function": dumps_function(task[1]), "args": warn_dumps(task[2])} if len(task) == 4: d["kwargs"] = warn_dumps(task[3]) return d elif not any(map(_maybe_complex, task[1:])): return {"function": dumps_function(task[0]), "args": warn_dumps(task[1:])} return to_serialize(task) _warn_dumps_warned = [False] def warn_dumps(obj, dumps=pickle.dumps, limit=1e6): """ Dump an object to bytes, warn if those bytes are large """ b = dumps(obj) if not _warn_dumps_warned[0] and len(b) > limit: _warn_dumps_warned[0] = True s = str(obj) if len(s) > 70: s = s[:50] + " ... " + s[-15:] warnings.warn( "Large object of size %s detected in task graph: \n" " %s\n" "Consider scattering large objects ahead of time\n" "with client.scatter to reduce scheduler burden and \n" "keep data on workers\n\n" " future = client.submit(func, big_data) # bad\n\n" " big_future = client.scatter(big_data) # good\n" " future = client.submit(func, big_future) # good" % (format_bytes(len(b)), s) ) return b def apply_function( function, args, kwargs, execution_state, key, active_threads, active_threads_lock, time_delay, ): """ Run a function, collect information Returns ------- msg: dictionary with status, result/error, timings, etc.. """ ident = threading.get_ident() with active_threads_lock: active_threads[ident] = key thread_state.start_time = time() thread_state.execution_state = execution_state thread_state.key = key start = time() try: result = function(*args, **kwargs) except Exception as e: msg = error_message(e) msg["op"] = "task-erred" msg["actual-exception"] = e else: msg = { "op": "task-finished", "status": "OK", "result": result, "nbytes": sizeof(result), "type": type(result) if result is not None else None, } finally: end = time() msg["start"] = start + time_delay msg["stop"] = end + time_delay msg["thread"] = ident with active_threads_lock: del active_threads[ident] return msg def apply_function_actor( function, args, kwargs, execution_state, key, active_threads, active_threads_lock ): """ Run a function, collect information Returns ------- msg: dictionary with status, result/error, timings, etc.. """ ident = threading.get_ident() with active_threads_lock: active_threads[ident] = key thread_state.execution_state = execution_state thread_state.key = key result = function(*args, **kwargs) with active_threads_lock: del active_threads[ident] return result def get_msg_safe_str(msg): """ Make a worker msg, which contains args and kwargs, safe to cast to str: allowing for some arguments to raise exceptions during conversion and ignoring them. """ class Repr(object): def __init__(self, f, val): self._f = f self._val = val def __repr__(self): return self._f(self._val) msg = msg.copy() if "args" in msg: msg["args"] = Repr(convert_args_to_str, msg["args"]) if "kwargs" in msg: msg["kwargs"] = Repr(convert_kwargs_to_str, msg["kwargs"]) return msg def convert_args_to_str(args, max_len=None): """ Convert args to a string, allowing for some arguments to raise exceptions during conversion and ignoring them. """ length = 0 strs = ["" for i in range(len(args))] for i, arg in enumerate(args): try: sarg = repr(arg) except Exception: sarg = "< could not convert arg to str >" strs[i] = sarg length += len(sarg) + 2 if max_len is not None and length > max_len: return "({}".format(", ".join(strs[: i + 1]))[:max_len] else: return "({})".format(", ".join(strs)) def convert_kwargs_to_str(kwargs, max_len=None): """ Convert kwargs to a string, allowing for some arguments to raise exceptions during conversion and ignoring them. """ length = 0 strs = ["" for i in range(len(kwargs))] for i, (argname, arg) in enumerate(kwargs.items()): try: sarg = repr(arg) except Exception: sarg = "< could not convert arg to str >" skwarg = repr(argname) + ": " + sarg strs[i] = skwarg length += len(skwarg) + 2 if max_len is not None and length > max_len: return "{{{}".format(", ".join(strs[: i + 1]))[:max_len] else: return "{{{}}}".format(", ".join(strs)) def weight(k, v): return sizeof(v) async def run(server, comm, function, args=(), kwargs={}, is_coro=None, wait=True): function = pickle.loads(function) if is_coro is None: is_coro = iscoroutinefunction(function) else: warnings.warn( "The is_coro= parameter is deprecated. " "We now automatically detect coroutines/async functions" ) assert wait or is_coro, "Combination not supported" if args: args = pickle.loads(args) if kwargs: kwargs = pickle.loads(kwargs) if has_arg(function, "dask_worker"): kwargs["dask_worker"] = server if has_arg(function, "dask_scheduler"): kwargs["dask_scheduler"] = server logger.info("Run out-of-band function %r", funcname(function)) try: if not is_coro: result = function(*args, **kwargs) else: if wait: result = await function(*args, **kwargs) else: server.loop.add_callback(function, *args, **kwargs) result = None except Exception as e: logger.warning( "Run Failed\nFunction: %s\nargs: %s\nkwargs: %s\n", str(funcname(function))[:1000], convert_args_to_str(args, max_len=1000), convert_kwargs_to_str(kwargs, max_len=1000), exc_info=True, ) response = error_message(e) else: response = {"status": "OK", "result": to_serialize(result)} return response _global_workers = Worker._instances try: from .diagnostics import nvml except Exception: pass else: @gen.coroutine def gpu_metric(worker): result = yield offload(nvml.real_time) return result DEFAULT_METRICS["gpu"] = gpu_metric def gpu_startup(worker): return nvml.one_time() DEFAULT_STARTUP_INFORMATION["gpu"] = gpu_startup