Source code for distributed.worker

from __future__ import annotations

import asyncio
import bisect
import builtins
import errno
import heapq
import logging
import os
import random
import sys
import threading
import warnings
import weakref
from collections import defaultdict, deque, namedtuple
from import Callable, Collection, Iterable, Mapping, MutableMapping
from concurrent.futures import Executor
from contextlib import suppress
from datetime import timedelta
from inspect import isawaitable
from pickle import PicklingError
from typing import TYPE_CHECKING, Any, ClassVar, Container

    from typing_extensions import Literal
    from .diagnostics.plugin import WorkerPlugin
    from .actor import Actor
    from .client import Client
    from .nanny import Nanny

from tlz import first, keymap, merge, pluck  # noqa: F401
from tornado.ioloop import IOLoop, PeriodicCallback

import dask
from dask.core import istask
from dask.system import CPU_COUNT
from dask.utils import (

from . import comm, preloading, profile, shuffle, system, utils
from .batched import BatchedSend
from .comm import Comm, connect, get_address_host
from .comm.addressing import address_from_user_args, parse_address
from .comm.utils import OFFLOAD_THRESHOLD
from .core import (
from .diagnostics import nvml
from .diagnostics.plugin import _get_plugin_name
from .diskutils import WorkDir, WorkSpace
from .http import get_handlers
from .metrics import time
from .node import ServerNode
from .proctitle import setproctitle
from .protocol import pickle, to_serialize
from .pubsub import PubSubWorkerExtension
from .security import Security
from .sizeof import safe_sizeof as sizeof
from .threadpoolexecutor import ThreadPoolExecutor
from .threadpoolexecutor import secede as tpe_secede
from .utils import (
from .utils_comm import gather_from_workers, pack_data, retry_operation
from .utils_perf import ThrottledGC, disable_gc_diagnosis, enable_gc_diagnosis
from .versions import get_versions

logger = logging.getLogger(__name__)

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

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

# TaskState.state subsets
READY = {"ready", "constrained"}
FETCH_INTENDED = {"missing", "fetch", "flight", "cancelled", "resumed"}

# Worker.status subsets
RUNNING = {Status.running, Status.paused, Status.closing_gracefully}

DEFAULT_EXTENSIONS: list[type] = [PubSubWorkerExtension]

DEFAULT_METRICS: dict[str, Callable[[Worker], Any]] = {}

DEFAULT_STARTUP_INFORMATION: dict[str, Callable[[Worker], Any]] = {}

DEFAULT_DATA_SIZE = parse_bytes(

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

_taskstate_to_dict_guard = False

class InvalidTransition(Exception):

class TaskState:
    """Holds volatile state relating to an individual Dask task

    * **dependencies**: ``set(TaskState instances)``
        The data needed by this key to run
    * **dependents**: ``set(TaskState instances)``
        The keys that use this dependency.
    * **duration**: ``float``
        Expected duration the a task
    * **priority**: ``tuple``
        The priority this task given by the scheduler.  Determines run order.
    * **state**: ``str``
        The current state of the task. One of ["waiting", "ready", "executing",
        "fetch", "memory", "flight", "long-running", "rescheduled", "error"]
    * **who_has**: ``set(worker)``
        Workers that we believe have this data
    * **coming_from**: ``str``
        The worker that current task data is coming from if task is in flight
    * **waiting_for_data**: ``set(keys of dependencies)``
        A dynamic version of dependencies.  All dependencies that we still don't
        have for a particular key.
    * **resource_restrictions**: ``{str: number}``
        Abstract resources required to run a task
    * **exception**: ``str``
        The exception caused by running a task if it erred
    * **traceback**: ``str``
        The exception caused by running a task if it erred
    * **type**: ``type``
        The type of a particular piece of data
    * **suspicious_count**: ``int``
        The number of times a dependency has not been where we expected it
    * **startstops**: ``[{startstop}]``
        Log of transfer, load, and compute times for a task
    * **start_time**: ``float``
        Time at which task begins running
    * **stop_time**: ``float``
        Time at which task finishes running
    * **metadata**: ``dict``
        Metadata related to task. Stored metadata should be msgpack
        serializable (e.g. int, string, list, dict).
    * **nbytes**: ``int``
        The size of a particular piece of data
    * **annotations**: ``dict``
        Task annotations

    key: str
    runspec: SerializedTask
        A named tuple containing the ``function``, ``args``, ``kwargs`` and
        ``task`` associated with this `TaskState` instance. This defaults to
        ``None`` and can remain empty if it is a dependency that this worker
        will receive from another worker.


    def __init__(self, key, runspec=None):
        assert key is not None
        self.key = key
        self.runspec = runspec
        self.dependencies = set()
        self.dependents = set()
        self.duration = None
        self.priority = None
        self.state = "released"
        self.who_has = set()
        self.coming_from = None
        self.waiting_for_data = set()
        self.waiters = set()
        self.resource_restrictions = {}
        self.exception = None
        self.exception_text = ""
        self.traceback = None
        self.traceback_text = ""
        self.type = None
        self.suspicious_count = 0
        self.startstops = []
        self.start_time = None
        self.stop_time = None
        self.metadata = {}
        self.nbytes = None
        self.annotations = None
        self.done = False
        self._previous = None
        self._next = None

    def __repr__(self):
        return f"<TaskState {self.key!r} {self.state}>"

    def get_nbytes(self) -> int:
        nbytes = self.nbytes
        return nbytes if nbytes is not None else DEFAULT_DATA_SIZE

    def _to_dict(self, *, exclude: Container[str] = ()) -> dict | str:
        A very verbose dictionary representation for debugging purposes.
        Not type stable and not intended for roundtrips.

            A list of attributes which must not be present in the output.

        See also
        # When a task references another task, just print the task repr. All tasks
        # should neatly appear under Worker.tasks. This also prevents a RecursionError
        # during particularly heavy loads, which have been observed to happen whenever
        # there's an acyclic dependency chain of ~200+ tasks.
        global _taskstate_to_dict_guard
        if _taskstate_to_dict_guard:
            return repr(self)
        _taskstate_to_dict_guard = True
            return recursive_to_dict(
                {k: v for k, v in self.__dict__.items() if k not in exclude},
            _taskstate_to_dict_guard = False

    def is_protected(self) -> bool:
        return self.state in PROCESSING or any(
            dep_ts.state in PROCESSING for dep_ts in self.dependents

[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 * **executors:** ``dict[str, concurrent.futures.Executor]``: Executors used to perform computation. Always contains the default executor. * **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 * **comm_threshold_bytes**: ``int`` As long as the total number of bytes in flight is below this threshold we will not limit the number of outgoing connections for a single tasks dependency fetch. * **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** These 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. * **tasks**: ``{key: TaskState}`` The tasks currently executing on this worker (and any dependencies of those tasks) * **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. * **data_needed**: deque(keys) The keys which still require data in order to execute, arranged in a deque * **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_count**: ``int`` A count of tasks currently executing on this worker * **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. * **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**: ``int`` A count of the number of tasks that are coming to us in current peer-to-peer connections * **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 * **threads**: ``{key: int}`` The ID of the thread on which the task ran * **active_threads**: ``{int: key}`` The keys currently running on active threads * **waiting_for_data_count**: ``int`` A count of how many tasks are currently waiting for data * **generation**: ``int`` Counter that decreases every time the compute-task handler is invoked by the Scheduler. It is appended to TaskState.priority and acts as a tie-breaker between tasks that have the same priority on the Scheduler, determining a last-in-first-out order between them. Parameters ---------- scheduler_ip: str, optional scheduler_port: int, optional scheduler_file: str, optional 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 or False Fraction of memory to try to stay beneath (default: read from config key memory_spill_fraction: float or false Fraction of memory at which we start spilling to disk (default: read from config key distributed.worker.memory.spill) memory_pause_fraction: float or False Fraction of memory at which we stop running new tasks (default: read from config key distributed.worker.memory.pause) executor: concurrent.futures.Executor, dict[str, concurrent.futures.Executor], "offload" The executor(s) to use. Depending on the type, it has the following meanings: - Executor instance: The default executor. - Dict[str, Executor]: mapping names to Executor instances. If the "default" key isn't in the dict, a "default" executor will be created using ``ThreadPoolExecutor(nthreads)``. - Str: The string "offload", which refer to the same thread pool used for offloading communications. This results in the same thread being used for deserialization and computation. 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 kwargs: optional Additional parameters to ServerNode constructor Examples -------- Use the command line to start a worker:: $ dask-scheduler Start scheduler at $ dask-worker Start worker at: Registered with scheduler at: See Also -------- distributed.scheduler.Scheduler distributed.nanny.Nanny """ _instances: ClassVar[weakref.WeakSet[Worker]] = weakref.WeakSet() _initialized_clients: ClassVar[weakref.WeakSet[Client]] = weakref.WeakSet() tasks: dict[str, TaskState] waiting_for_data_count: int has_what: defaultdict[str, set[str]] # {worker address: {ts.key, ...} pending_data_per_worker: defaultdict[str, deque[str]] nanny: Nanny | None _lock: threading.Lock data_needed: list[tuple[int, str]] # heap[(ts.priority, ts.key)] in_flight_workers: dict[str, set[str]] # {worker address: {ts.key, ...}} total_out_connections: int total_in_connections: int comm_threshold_bytes: int comm_nbytes: int _missing_dep_flight: set[TaskState] threads: dict[str, int] # {ts.key: thread ID} active_threads_lock: threading.Lock active_threads: dict[int, str] # {thread ID: ts.key} active_keys: set[str] profile_keys: defaultdict[str, dict[str, Any]] profile_keys_history: deque[tuple[float, dict[str, dict[str, Any]]]] profile_recent: dict[str, Any] profile_history: deque[tuple[float, dict[str, Any]]] generation: int ready: list[str] constrained: deque[str] _executing: set[TaskState] _in_flight_tasks: set[TaskState] executed_count: int long_running: set[TaskState] log: deque[tuple] incoming_transfer_log: deque[dict[str, Any]] outgoing_transfer_log: deque[dict[str, Any]] target_message_size: int validate: bool _transitions_table: dict[tuple[str, str], Callable] _transition_counter: int incoming_count: int outgoing_count: int outgoing_current_count: int repetitively_busy: int bandwidth: float latency: float profile_cycle_interval: float workspace: WorkSpace _workdir: WorkDir local_directory: str _client: Client | None bandwidth_workers: defaultdict[str, tuple[float, int]] bandwidth_types: defaultdict[type, tuple[float, int]] preloads: list[preloading.Preload] contact_address: str | None _start_port: int | None _start_host: str | None _interface: str | None _protocol: str _dashboard_address: str | None _dashboard: bool _http_prefix: str nthreads: int total_resources: dict[str, float] available_resources: dict[str, float] death_timeout: float | None lifetime: float | None lifetime_stagger: float | None lifetime_restart: bool extensions: dict security: Security connection_args: dict[str, Any] memory_limit: int | None memory_target_fraction: float | Literal[False] memory_spill_fraction: float | Literal[False] memory_pause_fraction: float | Literal[False] data: MutableMapping[str, Any] # {task key: task payload} actors: dict[str, Actor | None] loop: IOLoop reconnect: bool executors: dict[str, Executor] batched_stream: BatchedSend name: Any scheduler_delay: float stream_comms: dict[str, BatchedSend] heartbeat_active: bool _ipython_kernel: Any | None = None services: dict[str, Any] = {} service_specs: dict[str, Any] metrics: dict[str, Callable[[Worker], Any]] startup_information: dict[str, Callable[[Worker], Any]] low_level_profiler: bool scheduler: Any execution_state: dict[str, Any] memory_monitor_interval: float | None _memory_monitoring: bool _throttled_gc: ThrottledGC plugins: dict[str, WorkerPlugin] _pending_plugins: tuple[WorkerPlugin, ...] def __init__( self, scheduler_ip: str | None = None, scheduler_port: int | None = None, *, scheduler_file: str | None = None, ncores: None = None, # Deprecated, use nthreads instead nthreads: int | None = None, loop: IOLoop | None = None, local_dir: None = None, # Deprecated, use local_directory instead local_directory: str | None = None, services: dict | None = None, name: Any | None = None, reconnect: bool = True, memory_limit: str | float = "auto", executor: Executor | dict[str, Executor] | Literal["offload"] | None = None, resources: dict[str, float] | None = None, silence_logs: int | None = None, death_timeout: Any | None = None, preload: list[str] | None = None, preload_argv: list[str] | list[list[str]] | None = None, security: Security | dict[str, Any] | None = None, contact_address: str | None = None, memory_monitor_interval: Any = "200ms", memory_target_fraction: float | Literal[False] | None = None, memory_spill_fraction: float | Literal[False] | None = None, memory_pause_fraction: float | Literal[False] | None = None, extensions: list[type] | None = None, metrics: Mapping[str, Callable[[Worker], Any]] = DEFAULT_METRICS, startup_information: Mapping[ str, Callable[[Worker], Any] ] = DEFAULT_STARTUP_INFORMATION, data: ( MutableMapping[str, Any] # pre-initialised | Callable[[], MutableMapping[str, Any]] # constructor | tuple[ Callable[..., MutableMapping[str, Any]], dict[str, Any] ] # (constructor, kwargs to constructor) | None # create internatlly ) = None, interface: str | None = None, host: str | None = None, port: int | None = None, protocol: str | None = None, dashboard_address: str | None = None, dashboard: bool = False, http_prefix: str = "/", nanny: Nanny | None = None, plugins: tuple[WorkerPlugin, ...] = (), low_level_profiler: bool | None = None, validate: bool | None = None, profile_cycle_interval=None, lifetime: Any | None = None, lifetime_stagger: Any | None = None, lifetime_restart: bool | None = None, **kwargs, ): self.tasks = {} self.waiting_for_data_count = 0 self.has_what = defaultdict(set) self.pending_data_per_worker = defaultdict(deque) self.nanny = nanny self._lock = threading.Lock() self.data_needed = [] self.in_flight_workers = {} self.total_out_connections = dask.config.get( "distributed.worker.connections.outgoing" ) self.total_in_connections = dask.config.get( "distributed.worker.connections.incoming" ) self.comm_threshold_bytes = int(10e6) self.comm_nbytes = 0 self._missing_dep_flight = set() self.threads = {} self.active_threads_lock = threading.Lock() self.active_threads = {} self.active_keys = set() 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.generation = 0 self.ready = [] self.constrained = deque() self._executing = set() self._in_flight_tasks = set() self.executed_count = 0 self.long_running = set() self.target_message_size = int(50e6) # 50 MB self.log = deque(maxlen=100000) if validate is None: validate = dask.config.get("distributed.scheduler.validate") self.validate = validate self._transitions_table = { ("cancelled", "resumed"): self.transition_cancelled_resumed, ("cancelled", "fetch"): self.transition_cancelled_fetch, ("cancelled", "released"): self.transition_cancelled_released, ("cancelled", "waiting"): self.transition_cancelled_waiting, ("cancelled", "forgotten"): self.transition_cancelled_forgotten, ("cancelled", "memory"): self.transition_cancelled_memory, ("cancelled", "error"): self.transition_cancelled_error, ("resumed", "memory"): self.transition_generic_memory, ("resumed", "error"): self.transition_generic_error, ("resumed", "released"): self.transition_generic_released, ("resumed", "waiting"): self.transition_resumed_waiting, ("resumed", "fetch"): self.transition_resumed_fetch, ("constrained", "executing"): self.transition_constrained_executing, ("constrained", "released"): self.transition_generic_released, ("error", "released"): self.transition_generic_released, ("executing", "error"): self.transition_executing_error, ("executing", "long-running"): self.transition_executing_long_running, ("executing", "memory"): self.transition_executing_memory, ("executing", "released"): self.transition_executing_released, ("executing", "rescheduled"): self.transition_executing_rescheduled, ("fetch", "flight"): self.transition_fetch_flight, ("fetch", "missing"): self.transition_fetch_missing, ("fetch", "released"): self.transition_generic_released, ("flight", "error"): self.transition_flight_error, ("flight", "fetch"): self.transition_flight_fetch, ("flight", "memory"): self.transition_flight_memory, ("flight", "released"): self.transition_flight_released, ("long-running", "error"): self.transition_generic_error, ("long-running", "memory"): self.transition_long_running_memory, ("long-running", "rescheduled"): self.transition_executing_rescheduled, ("long-running", "released"): self.transition_executing_released, ("memory", "released"): self.transition_memory_released, ("missing", "fetch"): self.transition_missing_fetch, ("missing", "released"): self.transition_missing_released, ("missing", "error"): self.transition_generic_error, ("ready", "error"): self.transition_generic_error, ("ready", "executing"): self.transition_ready_executing, ("ready", "released"): self.transition_generic_released, ("released", "error"): self.transition_generic_error, ("released", "fetch"): self.transition_released_fetch, ("released", "forgotten"): self.transition_released_forgotten, ("released", "memory"): self.transition_released_memory, ("released", "waiting"): self.transition_released_waiting, ("waiting", "constrained"): self.transition_waiting_constrained, ("waiting", "ready"): self.transition_waiting_ready, ("waiting", "released"): self.transition_generic_released, } self._transition_counter = 0 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") assert profile_cycle_interval self._setup_logging(logger) if local_dir is not None: warnings.warn("The local_dir keyword has moved to local_directory") local_directory = local_dir if not local_directory: local_directory = dask.config.get("temporary-directory") or os.getcwd() os.makedirs(local_directory, exist_ok=True) 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 if not preload: preload = dask.config.get("distributed.worker.preload") if not preload_argv: preload_argv = dask.config.get("distributed.worker.preload-argv") assert preload is not None assert preload_argv is not None self.preloads = preloading.process_preloads( self, preload, preload_argv, file_dir=self.local_directory ) 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 if protocol is None: protocol_address = scheduler_addr.split("://") if len(protocol_address) == 2: protocol = protocol_address[0] assert protocol self._start_port = port self._start_host = host if host: # Helpful error message if IPv6 specified incorrectly _, host_address = parse_address(host) if host_address.count(":") > 1 and not host_address.startswith("["): raise ValueError( "Host address with IPv6 must be bracketed like '[::1]'; " f"got {host_address}" ) self._interface = interface self._protocol = protocol if ncores is not None: warnings.warn("the ncores= parameter has moved to nthreads=") nthreads = ncores self.nthreads = nthreads or CPU_COUNT if resources is None: resources = dask.config.get("distributed.worker.resources", None) assert isinstance(resources, dict) self.total_resources = resources or {} self.available_resources = (resources or {}).copy() self.death_timeout = parse_timedelta(death_timeout) self.extensions = {} if silence_logs: silence_logging(level=silence_logs) if isinstance(security, dict): security = Security(**security) = security or Security() assert isinstance(, Security) self.connection_args ="worker") self.memory_limit = parse_memory_limit(memory_limit, self.nthreads) self.memory_target_fraction = ( memory_target_fraction if memory_target_fraction is not None else dask.config.get("") ) self.memory_spill_fraction = ( memory_spill_fraction if memory_spill_fraction is not None else dask.config.get("distributed.worker.memory.spill") ) self.memory_pause_fraction = ( memory_pause_fraction if memory_pause_fraction is not None else dask.config.get("distributed.worker.memory.pause") ) if isinstance(data, MutableMapping): = data elif callable(data): = data() elif isinstance(data, tuple): = data[0](**data[1]) elif self.memory_limit and ( self.memory_target_fraction or self.memory_spill_fraction ): from .spill import SpillBuffer = SpillBuffer( os.path.join(self.local_directory, "storage"), target=int( self.memory_limit * (self.memory_target_fraction or self.memory_spill_fraction) ) or sys.maxsize, ) else: = {} self.actors = {} self.loop = loop or IOLoop.current() self.reconnect = reconnect # Common executors always available self.executors = { "offload": utils._offload_executor, "actor": ThreadPoolExecutor(1, thread_name_prefix="Dask-Actor-Threads"), } if nvml.device_get_count() > 0: self.executors["gpu"] = ThreadPoolExecutor( 1, thread_name_prefix="Dask-GPU-Threads" ) # Find the default executor if executor == "offload": self.executors["default"] = self.executors["offload"] elif isinstance(executor, dict): self.executors.update(executor) elif executor is not None: self.executors["default"] = executor if "default" not in self.executors: self.executors["default"] = ThreadPoolExecutor( self.nthreads, thread_name_prefix="Dask-Default-Threads" ) self.batched_stream = BatchedSend(interval="2ms", loop=self.loop) = name self.scheduler_delay = 0 self.stream_comms = {} self.heartbeat_active = False self._ipython_kernel = None if self.local_directory not in sys.path: sys.path.insert(0, self.local_directory) = {} self.service_specs = services or {} self._dashboard_address = dashboard_address self._dashboard = dashboard self._http_prefix = http_prefix self.metrics = dict(metrics) if metrics else {} self.startup_information = ( dict(startup_information) if startup_information else {} ) if low_level_profiler is None: low_level_profiler = dask.config.get("distributed.worker.profile.low-level") self.low_level_profiler = low_level_profiler handlers = { "gather": self.gather, "run":, "run_coroutine": self.run_coroutine, "get_data": self.get_data, "update_data": self.update_data, "free_keys": self.handle_free_keys, "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, "plugin-remove": self.plugin_remove, "get_monitor_info": self.get_monitor_info, } stream_handlers = { "close": self.close, "cancel-compute": self.handle_cancel_compute, "acquire-replicas": self.handle_acquire_replicas, "compute-task": self.handle_compute_task, "free-keys": self.handle_free_keys, "remove-replicas": self.handle_remove_replicas, "steal-request": self.handle_steal_request, } super().__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) self.periodic_callbacks["heartbeat"] = pc pc = PeriodicCallback( lambda: self.batched_stream.send({"op": "keep-alive"}), 60000 ) self.periodic_callbacks["keep-alive"] = pc pc = PeriodicCallback(self.find_missing, 1000) self.periodic_callbacks["find-missing"] = pc self._address = contact_address self.memory_monitor_interval = parse_timedelta( memory_monitor_interval, default="ms" ) self._memory_monitoring = False if self.memory_limit: assert self.memory_monitor_interval is not None pc = PeriodicCallback( self.memory_monitor, self.memory_monitor_interval * 1000 ) 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]") profile_trigger_interval = parse_timedelta( dask.config.get("distributed.worker.profile.interval"), default="ms" ) pc = PeriodicCallback(self.trigger_profile, profile_trigger_interval * 1000) self.periodic_callbacks["profile"] = pc pc = PeriodicCallback(self.cycle_profile, profile_cycle_interval * 1000) self.periodic_callbacks["profile-cycle"] = pc self.plugins = {} self._pending_plugins = plugins if lifetime is None: lifetime = dask.config.get("distributed.worker.lifetime.duration") self.lifetime = parse_timedelta(lifetime) if lifetime_stagger is None: lifetime_stagger = dask.config.get("distributed.worker.lifetime.stagger") lifetime_stagger = parse_timedelta(lifetime_stagger) if lifetime_restart is None: lifetime_restart = dask.config.get("distributed.worker.lifetime.restart") self.lifetime_restart = lifetime_restart 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: %r, %s, %s, stored: %d, running: %d/%d, ready: %d, comm: %d, waiting: %d>" % ( self.__class__.__name__, self.address,, self.status, len(, self.executing_count, self.nthreads, len(self.ready), self.in_flight_tasks, self.waiting_for_data_count, ) @property def logs(self): return self._deque_handler.deque def log_event(self, topic, msg): self.batched_stream.send( { "op": "log-event", "topic": topic, "msg": msg, } ) @property def executing_count(self) -> int: return len(self._executing) @property def in_flight_tasks(self) -> int: return len(self._in_flight_tasks) @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 @property def executor(self): return self.executors["default"] @ServerNode.status.setter # type: ignore def status(self, value): """Override Server.status to notify the Scheduler of status changes""" ServerNode.status.__set__(self, value) self._send_worker_status_change() def _send_worker_status_change(self) -> None: if ( self.batched_stream and self.batched_stream.comm and not self.batched_stream.comm.closed() ): self.batched_stream.send( {"op": "worker-status-change", "status":} ) elif self._status != Status.closed: self.loop.call_later(0.05, self._send_worker_status_change) async def get_metrics(self): out = dict( executing=self.executing_count, in_memory=len(, ready=len(self.ready), in_flight=self.in_flight_tasks, bandwidth={ "total": self.bandwidth, "workers": dict(self.bandwidth_workers), "types": keymap(typename, self.bandwidth_types), }, spilled_nbytes=getattr(, "spilled_total", 0), ) out.update(self.monitor.recent()) for k, metric in self.metrics.items(): try: result = metric(self) if isawaitable(result): result = await result # In case of collision, prefer core metrics out.setdefault(k, result) except Exception: # TODO: log error once pass return out async def get_startup_information(self): result = {} for k, f in self.startup_information.items(): try: v = f(self) if isawaitable(v): 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":, "scheduler": self.scheduler.address, "nthreads": self.nthreads, "ncores": self.nthreads, # backwards compatibility "memory_limit": self.memory_limit, } def _to_dict( self, comm: Comm | None = None, *, exclude: Container[str] = () ) -> dict: """ A very verbose dictionary representation for debugging purposes. Not type stable and not inteded for roundtrips. Parameters ---------- comm: exclude: A list of attributes which must not be present in the output. See also -------- Worker.identity Client.dump_cluster_state """ info = super()._to_dict(exclude=exclude) extra = { "status": self.status, "ready": self.ready, "constrained": self.constrained, "long_running": self.long_running, "executing_count": self.executing_count, "in_flight_tasks": self.in_flight_tasks, "in_flight_workers": self.in_flight_workers, "log": self.log, "tasks": self.tasks, "memory_limit": self.memory_limit, "memory_target_fraction": self.memory_target_fraction, "memory_spill_fraction": self.memory_spill_fraction, "memory_pause_fraction": self.memory_pause_fraction, "logs": self.get_logs(), "config": dask.config.config, "incoming_transfer_log": self.incoming_transfer_log, "outgoing_transfer_log": self.outgoing_transfer_log, } info.update(extra) return recursive_to_dict(info, exclude=exclude) ##################### # External Services # ##################### async def _register_with_scheduler(self): self.periodic_callbacks["keep-alive"].stop() self.periodic_callbacks["heartbeat"].stop() start = time() if self.contact_address is None: self.contact_address = self.address"-" * 49) while True: try: _start = time() comm = await connect(self.scheduler.address, **self.connection_args) = "Worker->Scheduler" comm._server = weakref.ref(self) await comm.write( dict( op="register-worker", reply=False, address=self.contact_address,, keys=list(, nthreads=self.nthreads,, nbytes={ ts.key: ts.get_nbytes() for ts in self.tasks.values() # Only if the task is in memory this is a sensible # result since otherwise it simply submits the # default value if ts.state == "memory" }, types={k: typename(v) for k, v in}, 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(), versions=get_versions(), metrics=await self.get_metrics(), extra=await self.get_startup_information(), ), serializers=["msgpack"], ) future =["msgpack"]) response = await future if response.get("warning"): logger.warning(response["warning"]) _end = time() middle = (_start + _end) / 2 self._update_latency(_end - start) self.scheduler_delay = response["time"] - middle self.status = Status.running break except OSError:"Waiting to connect to: %26s", self.scheduler.address) await asyncio.sleep(0.1) except TimeoutError:"Timed out when connecting to scheduler") if response["status"] != "OK": raise ValueError(f"Unexpected response from register: {response!r}") else: await asyncio.gather( *( self.plugin_add(name=name, plugin=plugin) for name, plugin in response["worker-plugins"].items() ) )" Registered to: %26s", self.scheduler.address)"-" * 49) self.batched_stream.start(comm) self.periodic_callbacks["keep-alive"].start() self.periodic_callbacks["heartbeat"].start() self.loop.add_callback(self.handle_scheduler, comm) def _update_latency(self, latency): self.latency = latency * 0.05 + self.latency * 0.95 if self.digests is not None: self.digests["latency"].add(latency) async def heartbeat(self): if self.heartbeat_active: logger.debug("Heartbeat skipped: channel busy") return self.heartbeat_active = True logger.debug("Heartbeat: %s", self.address) try: start = time() response = await retry_operation( self.scheduler.heartbeat_worker, address=self.contact_address, now=start, metrics=await self.get_metrics(), executing={ key: start - self.tasks[key].start_time for key in self.active_keys if key in self.tasks }, ) end = time() middle = (start + end) / 2 self._update_latency(end - start) if response["status"] == "missing": # If running, wait up to 0.5s and then re-register self. # Otherwise just exit. start = time() while self.status in RUNNING and time() < start + 0.5: await asyncio.sleep(0.01) if self.status in RUNNING: 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", exc_info=True) if not self.reconnect: await self.close(report=False) except OSError as e: # Scheduler is gone. Respect distributed.comm.timeouts.connect if "Timed out trying to connect" in str(e): await self.close(report=False) else: raise e finally: self.heartbeat_active = False 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 in RUNNING:"Connection to scheduler broken. Reconnecting...") self.loop.add_callback(self.heartbeat) else: await self.close(report=False)
[docs] 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) raise e return {"status": "OK", "nbytes": len(data)} def keys(self, comm=None): return list( 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 } result, missing_keys, missing_workers = await gather_from_workers( who_has, rpc=self.rpc, who=self.address ) self.update_data(data=result, report=False) if missing_keys: logger.warning( "Could not find data: %s on workers: %s (who_has: %s)", missing_keys, missing_workers, who_has, ) return {"status": "partial-fail", "keys": missing_keys} else: return {"status": "OK"} def get_monitor_info(self, comm=None, recent=False, start=0): result = dict( range_query=( self.monitor.recent() if recent else self.monitor.range_query(start=start) ), count=self.monitor.count, last_time=self.monitor.last_time, ) if nvml.device_get_count() > 0: result["gpu_name"] = self.monitor.gpu_name result["gpu_memory_total"] = self.monitor.gpu_memory_total return result ############# # Lifecycle # ############# async def start(self): if self.status and self.status in ( Status.closed, Status.closing, Status.closing_gracefully, ): return assert self.status is Status.undefined, self.status await super().start() enable_gc_diagnosis() ports = parse_ports(self._start_port) for port in ports: start_address = address_from_user_args( host=self._start_host, port=port, interface=self._interface, protocol=self._protocol,, ) kwargs ="worker") if self._protocol in ("tcp", "tls"): kwargs = kwargs.copy() kwargs["default_host"] = get_ip( get_address_host(self.scheduler.address) ) try: await self.listen(start_address, **kwargs) except OSError as e: if len(ports) > 1 and e.errno == errno.EADDRINUSE: continue else: raise else: self._start_address = start_address break else: raise ValueError( f"Could not start Worker on host {self._start_host}" f"with port {self._start_port}" ) # Start HTTP server associated with this Worker node routes = get_handlers( server=self, modules=dask.config.get("distributed.worker.http.routes"), prefix=self._http_prefix, ) self.start_http_server(routes, self._dashboard_address) if self._dashboard: try: import distributed.dashboard.worker except ImportError: logger.debug("To start diagnostics web server please install Bokeh") else: distributed.dashboard.worker.connect( self.http_application, self.http_server, self, prefix=self._http_prefix, ) self.ip = get_address_host(self.address) if is None: = self.address for preload in self.preloads: await preload.start() # 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 = f"{self.listener.prefix}{self.ip}"" Start worker at: %26s", self.address)" Listening to: %26s", listening_address) for k, v in self.service_ports.items():" {:>16} at: {:>26}".format(k, self.ip + ":" + str(v)))"Waiting to connect to: %26s", self.scheduler.address)"-" * 49)" Threads: %26d", self.nthreads) if self.memory_limit:" Memory: %26s", format_bytes(self.memory_limit))" Local Directory: %26s", self.local_directory) setproctitle("dask-worker [%s]" % self.address) plugins_msgs = await asyncio.gather( *( self.plugin_add(plugin=plugin, catch_errors=False) for plugin in self._pending_plugins ), return_exceptions=True, ) plugins_exceptions = [msg for msg in plugins_msgs if isinstance(msg, Exception)] if len(plugins_exceptions) >= 1: if len(plugins_exceptions) > 1: logger.error( "Multiple plugin exceptions raised. All exceptions will be logged, the first is raised." ) for exc in plugins_exceptions: logger.error(repr(exc)) raise plugins_exceptions[0] 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=30, nanny=True, executor_wait=True, safe=False ): with log_errors(): if self.status in (Status.closed, Status.closing): await self.finished() return self.reconnect = False disable_gc_diagnosis() try:"Stopping worker at %s", self.address) except ValueError: # address not available if already closed"Stopping worker") if self.status not in RUNNING:"Closed worker has not yet started: %s", self.status) self.status = Status.closing for preload in self.preloads: await preload.teardown() 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 isawaitable(td))) for pc in self.periodic_callbacks.values(): pc.stop() if self._client: # If this worker is the last one alive, clean up the worker # initialized clients if not any( w for w in Worker._instances if w != self and w.status in RUNNING ): for c in Worker._initialized_clients: # Regardless of what the client was initialized with # we'll require the result as a future. This is # necessary since the heursitics of asynchronous are not # reliable and we might deadlock here c._asynchronous = True if c.asynchronous: await c.close() else: # There is still the chance that even with us # telling the client to be async, itself will decide # otherwise c.close() with suppress(EnvironmentError, TimeoutError): if report and self.contact_address is not None: await asyncio.wait_for( self.scheduler.unregister( address=self.contact_address, safe=safe ), timeout, ) await self.scheduler.close_rpc() self._workdir.release() self.stop_services() # Give some time for a UCX scheduler to complete closing endpoints # before closing self.batched_stream, otherwise the local endpoint # may be closed too early and errors be raised on the scheduler when # trying to send closing message. if self._protocol == "ucx": await asyncio.sleep(0.2) if ( self.batched_stream and self.batched_stream.comm and not self.batched_stream.comm.closed() ): self.batched_stream.send({"op": "close-stream"}) if self.batched_stream: with suppress(TimeoutError): await self.batched_stream.close(timedelta(seconds=timeout)) for executor in self.executors.values(): if executor is utils._offload_executor: continue # Never shutdown the offload executor if isinstance(executor, ThreadPoolExecutor): executor._work_queue.queue.clear() executor.shutdown(wait=executor_wait, timeout=timeout) else: executor.shutdown(wait=executor_wait) self.stop() await self.rpc.close() self.status = Status.closed await super().close() setproctitle("dask-worker [closed]") return "OK"
[docs] async def close_gracefully(self, restart=None): """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 in (Status.closing, Status.closing_gracefully): await self.finished() if self.status == Status.closed: return if restart is None: restart = self.lifetime_restart"Closing worker gracefully: %s", self.address) self.status = Status.closing_gracefully await self.scheduler.retire_workers(workers=[self.address], remove=False) await self.close(safe=True, nanny=not restart)
async def terminate(self, comm=None, 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 == 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, **self.connection_args # TODO, serialization ) = "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 self.status == Status.paused: max_connections = 1 throttle_msg = " Throttling outgoing connections because worker is paused." else: throttle_msg = "" if ( max_connections is not False and self.outgoing_current_count >= max_connections ): logger.debug( "Worker %s has too many open connections to respond to data request " "from %s (%d/%d).%s", self.address, who, self.outgoing_current_count, max_connections, throttle_msg, ) return {"status": "busy"} self.outgoing_current_count += 1 data = {k:[k] for k in keys if k in} 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, worker=self) msg = {"status": "OK", "data": {k: to_serialize(v) for k, v in data.items()}} nbytes = {k: self.tasks[k].nbytes for k in data if k in self.tasks} 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 assert response == "OK", response except OSError: 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 Status.dont_reply ################### # Local Execution # ################### def update_data( self, comm=None, data=None, report=True, serializers=None, stimulus_id=None ): if stimulus_id is None: stimulus_id = f"update-data-{time()}" recommendations = {} scheduler_messages = [] for key, value in data.items(): try: ts = self.tasks[key] recommendations[ts] = ("memory", value) except KeyError: self.tasks[key] = ts = TaskState(key) try: recs = self._put_key_in_memory(ts, value, stimulus_id=stimulus_id) except Exception as e: msg = error_message(e) recommendations = {ts: tuple(msg.values())} else: recommendations.update(recs) self.log.append((key, "receive-from-scatter", stimulus_id, time())) if report: scheduler_messages.append( {"op": "add-keys", "keys": list(data), "stimulus_id": stimulus_id} ) self.transitions(recommendations, stimulus_id=stimulus_id) for msg in scheduler_messages: self.batched_stream.send(msg) return {"nbytes": {k: sizeof(v) for k, v in data.items()}, "status": "OK"}
[docs] def handle_free_keys(self, comm=None, keys=None, stimulus_id=None): """ Handler to be called by the scheduler. The given keys are no longer referred to and required by the scheduler. The worker is now allowed to release the key, if applicable. This does not guarantee that the memory is released since the worker may still decide to hold on to the data and task since it is required by an upstream dependency. """ self.log.append(("free-keys", keys, stimulus_id, time())) recommendations = {} for key in keys: ts = self.tasks.get(key) if ts: recommendations[ts] = "released" self.transitions(recommendations, stimulus_id=stimulus_id)
[docs] def handle_remove_replicas(self, keys, stimulus_id): """Stream handler notifying the worker that it might be holding unreferenced, superfluous data. This should not actually happen during ordinary operations and is only intended to correct any erroneous state. An example where this is necessary is if a worker fetches data for a downstream task but that task is released before the data arrives. In this case, the scheduler will notify the worker that it may be holding this unnecessary data, if the worker hasn't released the data itself, already. This handler does not guarantee the task nor the data to be actually released but only asks the worker to release the data on a best effort guarantee. This protects from race conditions where the given keys may already have been rescheduled for compute in which case the compute would win and this handler is ignored. For stronger guarantees, see handler free_keys """ self.log.append(("remove-replicas", keys, stimulus_id, time())) recommendations = {} rejected = [] for key in keys: ts = self.tasks.get(key) if ts is None or ts.state != "memory": continue if not ts.is_protected(): self.log.append( (ts.key, "remove-replica-confirmed", stimulus_id, time()) ) recommendations[ts] = "released" else: rejected.append(key) if rejected: self.log.append(("remove-replica-rejected", rejected, stimulus_id, time())) self.batched_stream.send( {"op": "add-keys", "keys": rejected, "stimulus_id": stimulus_id} ) self.transitions(recommendations, stimulus_id=stimulus_id) 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 retry_operation( self.scheduler.set_resources, resources=self.total_resources, worker=self.contact_address, ) ################### # Task Management # ###################
[docs] def handle_cancel_compute(self, key, reason): """ Cancel a task on a best effort basis. This is only possible while a task is in state `waiting` or `ready`. Nothing will happen otherwise. """ ts = self.tasks.get(key) if ts and ts.state in READY | {"waiting"}: self.log.append((key, "cancel-compute", reason, time())) ts.scheduler_holds_ref = False # All possible dependents of TS should not be in state Processing on # scheduler side and therefore should not be assigned to a worker, # yet. assert not ts.dependents self.transition(ts, "released", stimulus_id=reason)
def handle_acquire_replicas( self, comm=None, *, keys: Collection[str], who_has: dict[str, Collection[str]], stimulus_id: str, ): recommendations = {} for key in keys: ts = self.ensure_task_exists( key=key, # Transfer this data after all dependency tasks of computations with # default or explicitly high (>0) user priority and before all # computations with low priority (<0). Note that the priority= parameter # of compute() is multiplied by -1 before it reaches TaskState.priority. priority=(1,), stimulus_id=stimulus_id, ) if ts.state != "memory": recommendations[ts] = "fetch" self.update_who_has(who_has, stimulus_id=stimulus_id) self.transitions(recommendations, stimulus_id=stimulus_id) def ensure_task_exists( self, key: str, *, priority: tuple[int, ...], stimulus_id: str ) -> TaskState: try: ts = self.tasks[key] logger.debug("Data task %s already known (stimulus_id=%s)", ts, stimulus_id) except KeyError: self.tasks[key] = ts = TaskState(key) if not ts.priority: assert priority ts.priority = priority self.log.append((key, "ensure-task-exists", ts.state, stimulus_id, time())) return ts def handle_compute_task( self, *, key: str, who_has: dict[str, Collection[str]], priority: tuple[int, ...], duration: float, function=None, args=None, kwargs=None, task=no_value, nbytes: dict[str, int] | None = None, resource_restrictions=None, actor: bool = False, annotations=None, stimulus_id: str, ): self.log.append((key, "compute-task", stimulus_id, time())) try: ts = self.tasks[key] logger.debug( "Asked to compute an already known task %s", {"task": ts, "stimulus_id": stimulus_id}, ) except KeyError: self.tasks[key] = ts = TaskState(key) ts.runspec = SerializedTask(function, args, kwargs, task) assert isinstance(priority, tuple) priority = priority + (self.generation,) self.generation -= 1 if actor: self.actors[ts.key] = None ts.exception = None ts.traceback = None ts.exception_text = "" ts.traceback_text = "" ts.priority = priority ts.duration = duration if resource_restrictions: ts.resource_restrictions = resource_restrictions ts.annotations = annotations recommendations = {} scheduler_msgs = [] for dependency in who_has: dep_ts = self.ensure_task_exists( key=dependency, priority=priority, stimulus_id=stimulus_id, ) # link up to child / parents ts.dependencies.add(dep_ts) dep_ts.dependents.add(ts) if ts.state in READY | {"executing", "waiting", "resumed"}: pass elif ts.state == "memory": recommendations[ts] = "memory" scheduler_msgs.append(self._get_task_finished_msg(ts)) elif ts.state in { "released", "fetch", "flight", "missing", "cancelled", "error", }: recommendations[ts] = "waiting" else: raise RuntimeError(f"Unexpected task state encountered {ts} {stimulus_id}") for msg in scheduler_msgs: self.batched_stream.send(msg) self.transitions(recommendations, stimulus_id=stimulus_id) # We received new info, that's great but not related to the compute-task # instruction self.update_who_has(who_has, stimulus_id=stimulus_id) if nbytes is not None: for key, value in nbytes.items(): self.tasks[key].nbytes = value def transition_missing_fetch(self, ts, *, stimulus_id): if self.validate: assert ts.state == "missing" assert ts.priority is not None self._missing_dep_flight.discard(ts) ts.state = "fetch" ts.done = False heapq.heappush(self.data_needed, (ts.priority, ts.key)) return {}, [] def transition_missing_released(self, ts, *, stimulus_id): self._missing_dep_flight.discard(ts) recommendations, smsgs = self.transition_generic_released( ts, stimulus_id=stimulus_id ) assert ts.key in self.tasks return recommendations, smsgs def transition_fetch_missing(self, ts, *, stimulus_id): # handle_missing will append to self.data_needed if new workers are found ts.state = "missing" self._missing_dep_flight.add(ts) return {}, [] def transition_released_fetch(self, ts, *, stimulus_id): if self.validate: assert ts.state == "released" assert ts.priority is not None for w in ts.who_has: self.pending_data_per_worker[w].append(ts.key) ts.state = "fetch" ts.done = False heapq.heappush(self.data_needed, (ts.priority, ts.key)) return {}, [] def transition_generic_released(self, ts, *, stimulus_id): self.release_key(ts.key, reason=stimulus_id) recs = {} for dependency in ts.dependencies: if ( not dependency.waiters and dependency.state not in READY | PROCESSING | {"memory"} ): recs[dependency] = "released" if not ts.dependents: recs[ts] = "forgotten" return recs, [] def transition_released_waiting(self, ts, *, stimulus_id): if self.validate: assert ts.state == "released" assert all(d.key in self.tasks for d in ts.dependencies) recommendations = {} ts.waiting_for_data.clear() for dep_ts in ts.dependencies: if not dep_ts.state == "memory": ts.waiting_for_data.add(dep_ts) dep_ts.waiters.add(ts) if dep_ts.state not in {"fetch", "flight"}: recommendations[dep_ts] = "fetch" if ts.waiting_for_data: self.waiting_for_data_count += 1 elif ts.resource_restrictions: recommendations[ts] = "constrained" else: recommendations[ts] = "ready" ts.state = "waiting" return recommendations, [] def transition_fetch_flight(self, ts, worker, *, stimulus_id): if self.validate: assert ts.state == "fetch" assert ts.who_has assert ts.key not in self.data_needed ts.done = False ts.state = "flight" ts.coming_from = worker self._in_flight_tasks.add(ts) return {}, [] def transition_memory_released(self, ts, *, stimulus_id): recs, smsgs = self.transition_generic_released(ts, stimulus_id=stimulus_id) smsgs.append({"op": "release-worker-data", "key": ts.key}) return recs, smsgs def transition_waiting_constrained(self, ts, *, stimulus_id): if self.validate: assert ts.state == "waiting" assert not ts.waiting_for_data assert all( dep.key in or dep.key in self.actors for dep in ts.dependencies ) assert all(dep.state == "memory" for dep in ts.dependencies) assert ts.key not in self.ready ts.state = "constrained" self.constrained.append(ts.key) return {}, [] def transition_long_running_rescheduled(self, ts, *, stimulus_id): recs = {ts: "released"} smsgs = [{"op": "reschedule", "key": ts.key, "worker": self.address}] return recs, smsgs def transition_executing_rescheduled(self, ts, *, stimulus_id): for resource, quantity in ts.resource_restrictions.items(): self.available_resources[resource] += quantity self._executing.discard(ts) recs = {ts: "released"} smsgs = [{"op": "reschedule", "key": ts.key, "worker": self.address}] return recs, smsgs def transition_waiting_ready(self, ts, *, stimulus_id): if self.validate: assert ts.state == "waiting" assert ts.key not in self.ready assert not ts.waiting_for_data assert ts.priority is not None for dep in ts.dependencies: assert dep.key in or dep.key in self.actors assert dep.state == "memory" ts.state = "ready" heapq.heappush(self.ready, (ts.priority, ts.key)) return {}, [] def transition_cancelled_error( self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id ): recs, msgs = {}, [] if ts._previous == "executing": recs, msgs = self.transition_executing_error( ts, exception, traceback, exception_text, traceback_text, stimulus_id=stimulus_id, ) elif ts._previous == "flight": recs, msgs = self.transition_flight_error( ts, exception, traceback, exception_text, traceback_text, stimulus_id=stimulus_id, ) if ts._next: recs[ts] = ts._next return recs, msgs def transition_generic_error( self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id ): ts.exception = exception ts.traceback = traceback ts.exception_text = exception_text ts.traceback_text = traceback_text ts.state = "error" smsg = { "op": "task-erred", "status": "error", "key": ts.key, "thread": self.threads.get(ts.key), "exception": ts.exception, "traceback": ts.traceback, "exception_text": ts.exception_text, "traceback_text": ts.traceback_text, } if ts.startstops: smsg["startstops"] = ts.startstops return {}, [smsg] def transition_executing_error( self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id ): for resource, quantity in ts.resource_restrictions.items(): self.available_resources[resource] += quantity self._executing.discard(ts) return self.transition_generic_error( ts, exception, traceback, exception_text, traceback_text, stimulus_id=stimulus_id, )
[docs] def transition_resumed_fetch(self, ts, *, stimulus_id): """`resumed` is an intermediate degenerate state which splits further up into two states depending on what the last signal / next state is intended to be. There are only two viable choices depending on whether the task is required to be fetched from another worker `resumed(fetch)` or the task shall be computed on this worker `resumed(waiting)`. The only viable state transitions ending up here are flight -> cancelled -> resumed(waiting) or executing -> cancelled -> resumed(fetch) depending on the origin. Equally, only `fetch`, `waiting` or `released` are allowed output states. See also `transition_resumed_waiting` """ # if the next state is already intended to be fetch or if the # coro/thread is still running (ts.done==False), this is a noop if ts._next == "fetch": return {}, [] ts._next = "fetch" if ts.done: next_state = ts._next recs, smsgs = self.transition_generic_released(ts, stimulus_id=stimulus_id) recs[ts] = next_state return recs, smsgs return {}, []
[docs] def transition_resumed_waiting(self, ts, *, stimulus_id): """ See transition_resumed_fetch """ if ts._next == "waiting": return {}, [] ts._next = "waiting" if ts.done: next_state = ts._next recs, smsgs = self.transition_generic_released(ts, stimulus_id=stimulus_id) recs[ts] = next_state return recs, smsgs return {}, []
def transition_cancelled_fetch(self, ts, *, stimulus_id): if ts.done: return {ts: "released"}, [] elif ts._previous == "flight": ts.state = ts._previous return {}, [] else: assert ts._previous == "executing" return {ts: ("resumed", "fetch")}, [] def transition_cancelled_resumed(self, ts, next, *, stimulus_id): ts._next = next ts.state = "resumed" return {}, [] def transition_cancelled_waiting(self, ts, *, stimulus_id): if ts.done: return {ts: "released"}, [] elif ts._previous == "executing": ts.state = ts._previous return {}, [] else: assert ts._previous == "flight" return {ts: ("resumed", "waiting")}, [] def transition_cancelled_forgotten(self, ts, *, stimulus_id): ts._next = "forgotten" if not ts.done: return {}, [] return {ts: "released"}, [] def transition_cancelled_released(self, ts, *, stimulus_id): if not ts.done: ts._next = "released" return {}, [] next_state = ts._next self._executing.discard(ts) self._in_flight_tasks.discard(ts) for resource, quantity in ts.resource_restrictions.items(): self.available_resources[resource] += quantity recommendations, smsgs = self.transition_generic_released( ts, stimulus_id=stimulus_id ) if next_state != "released": recommendations[ts] = next_state return recommendations, smsgs def transition_executing_released(self, ts, *, stimulus_id): ts._previous = ts.state ts._next = "released" # See ts.state = "cancelled" ts.done = False return {}, [] def transition_long_running_memory(self, ts, value=no_value, *, stimulus_id): self.executed_count += 1 return self.transition_generic_memory(ts, value=value, stimulus_id=stimulus_id) def transition_generic_memory(self, ts, value=no_value, *, stimulus_id): if value is no_value and ts.key not in raise RuntimeError( f"Tried to transition task {ts} to `memory` without data available" ) if ts.resource_restrictions is not None: for resource, quantity in ts.resource_restrictions.items(): self.available_resources[resource] += quantity self._executing.discard(ts) self._in_flight_tasks.discard(ts) ts.coming_from = None try: recs = self._put_key_in_memory(ts, value, stimulus_id=stimulus_id) except Exception as e: msg = error_message(e) recs = {ts: tuple(msg.values())} return recs, [] assert ts.key in or ts.key in self.actors smsgs = [self._get_task_finished_msg(ts)] return recs, smsgs def transition_executing_memory(self, ts, value=no_value, *, stimulus_id): if self.validate: assert ts.state == "executing" or ts.key in self.long_running assert not ts.waiting_for_data assert ts.key not in self.ready self._executing.discard(ts) self.executed_count += 1 return self.transition_generic_memory(ts, value=value, stimulus_id=stimulus_id) def transition_constrained_executing(self, ts, *, stimulus_id): if self.validate: assert not ts.waiting_for_data assert ts.key not in assert ts.state in READY assert ts.key not in self.ready for dep in ts.dependencies: assert dep.key in or dep.key in self.actors for resource, quantity in ts.resource_restrictions.items(): self.available_resources[resource] -= quantity ts.state = "executing" self._executing.add(ts) self.loop.add_callback(self.execute, ts.key, stimulus_id=stimulus_id) return {}, [] def transition_ready_executing(self, ts, *, stimulus_id): if self.validate: assert not ts.waiting_for_data assert ts.key not in assert ts.state in READY assert ts.key not in self.ready assert all( dep.key in or dep.key in self.actors for dep in ts.dependencies ) ts.state = "executing" self._executing.add(ts) self.loop.add_callback(self.execute, ts.key, stimulus_id=stimulus_id) return {}, [] def transition_flight_fetch(self, ts, *, stimulus_id): # If this transition is called after the flight coroutine has finished, # we can reset the task and transition to fetch again. If it is not yet # finished, this should be a no-op if ts.done: recommendations, smsgs = self.transition_generic_released( ts, stimulus_id=stimulus_id ) recommendations[ts] = "fetch" return recommendations, smsgs else: return {}, [] def transition_flight_error( self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id ): self._in_flight_tasks.discard(ts) ts.coming_from = None return self.transition_generic_error( ts, exception, traceback, exception_text, traceback_text, stimulus_id=stimulus_id, ) def transition_flight_released(self, ts, *, stimulus_id): if ts.done: # FIXME: Is this even possible? Would an assert instead be more # sensible? return self.transition_generic_released(ts, stimulus_id=stimulus_id) else: ts._previous = "flight" ts._next = "released" # See ts.state = "cancelled" return {}, [] def transition_cancelled_memory(self, ts, value, *, stimulus_id): return {ts: ts._next}, [] def transition_executing_long_running(self, ts, compute_duration, *, stimulus_id): ts.state = "long-running" self._executing.discard(ts) self.long_running.add(ts.key) smsgs = [ { "op": "long-running", "key": ts.key, "compute_duration": compute_duration, } ] self.io_loop.add_callback(self.ensure_computing) return {}, smsgs def transition_released_memory(self, ts, value, *, stimulus_id): recommendations = {} try: recommendations = self._put_key_in_memory( ts, value, stimulus_id=stimulus_id ) except Exception as e: msg = error_message(e) recommendations[ts] = ( "error", msg["exception"], msg["traceback"], msg["exception_text"], msg["traceback_text"], ) return recommendations, [] smsgs = [{"op": "add-keys", "keys": [ts.key], "stimulus_id": stimulus_id}] return recommendations, smsgs def transition_flight_memory(self, ts, value, *, stimulus_id): self._in_flight_tasks.discard(ts) ts.coming_from = None recommendations = {} try: recommendations = self._put_key_in_memory( ts, value, stimulus_id=stimulus_id ) except Exception as e: msg = error_message(e) recommendations[ts] = ( "error", msg["exception"], msg["traceback"], msg["exception_text"], msg["traceback_text"], ) return recommendations, [] smsgs = [{"op": "add-keys", "keys": [ts.key], "stimulus_id": stimulus_id}] return recommendations, smsgs def transition_released_forgotten(self, ts, *, stimulus_id): recommendations = {} # Dependents _should_ be released by the scheduler before this if self.validate: assert not any(d.state != "forgotten" for d in ts.dependents) for dep in ts.dependencies: dep.dependents.discard(ts) if dep.state == "released" and not dep.dependents: recommendations[dep] = "forgotten" # Mark state as forgotten in case it is still referenced ts.state = "forgotten" self.tasks.pop(ts.key, None) return recommendations, [] def _transition(self, ts, finish, *args, stimulus_id, **kwargs): if isinstance(finish, tuple): # the concatenated transition path might need to access the tuple assert not args finish, *args = finish if ts is None or ts.state == finish: return {}, [] start = ts.state func = self._transitions_table.get((start, finish)) if func is not None: self._transition_counter += 1 recs, smsgs = func(ts, *args, stimulus_id=stimulus_id, **kwargs) self._notify_plugins("transition", ts.key, start, finish, **kwargs) elif "released" not in (start, finish): # start -> "released" -> finish try: recs, smsgs = self._transition(ts, "released", stimulus_id=stimulus_id) v = recs.get(ts, (finish, *args)) if isinstance(v, tuple): v_state, *v_args = v else: v_state, v_args = v, () b_recs, b_smsgs = self._transition( ts, v_state, *v_args, stimulus_id=stimulus_id ) recs.update(b_recs) smsgs += b_smsgs except InvalidTransition: raise InvalidTransition( f"Impossible transition from {start} to {finish} for {ts.key}" ) from None else: raise InvalidTransition( f"Impossible transition from {start} to {finish} for {ts.key}" ) self.log.append( ( # key ts.key, # initial start, # recommended finish, # final ts.state, # new recommendations {ts.key: new for ts, new in recs.items()}, stimulus_id, time(), ) ) return recs, smsgs
[docs] def transition(self, ts, finish: str, *, stimulus_id, **kwargs): """Transition a key from its current state to the finish state Examples -------- >>> self.transition('x', 'waiting') {'x': 'processing'} Returns ------- Dictionary of recommendations for future transitions See Also -------- Scheduler.transitions: transitive version of this function """ recs, smsgs = self._transition(ts, finish, stimulus_id=stimulus_id, **kwargs) for msg in smsgs: self.batched_stream.send(msg) self.transitions(recs, stimulus_id=stimulus_id)
[docs] def transitions(self, recommendations: dict, *, stimulus_id): """Process transitions until none are left This includes feedback from previous transitions and continues until we reach a steady state """ smsgs = [] remaining_recs = recommendations.copy() tasks = set() while remaining_recs: ts, finish = remaining_recs.popitem() tasks.add(ts) a_recs, a_smsgs = self._transition(ts, finish, stimulus_id=stimulus_id) remaining_recs.update(a_recs) smsgs += a_smsgs if self.validate: # Full state validation is very expensive for ts in tasks: self.validate_task(ts) if not self.batched_stream.closed(): for msg in smsgs: self.batched_stream.send(msg) else: logger.debug( "BatchedSend closed while transitioning tasks. %d tasks not sent.", len(smsgs), )
def maybe_transition_long_running(self, ts, *, stimulus_id, compute_duration=None): if ts.state == "executing": self.transition( ts, "long-running", compute_duration=compute_duration, stimulus_id=stimulus_id, ) assert ts.state == "long-running" def stateof(self, key): ts = self.tasks[key] return { "executing": ts.state == "executing", "waiting_for_data": bool(ts.waiting_for_data), "heap": key in pluck(1, self.ready), "data": key in, } def story(self, *keys): keys = [key.key if isinstance(key, TaskState) else key for key in 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): stimulus_id = f"ensure-communicating-{time()}" skipped_worker_in_flight = [] while self.data_needed and ( len(self.in_flight_workers) < self.total_out_connections or self.comm_nbytes < self.comm_threshold_bytes ): logger.debug( "Ensure communicating. Pending: %d. Connections: %d/%d", len(self.data_needed), len(self.in_flight_workers), self.total_out_connections, ) _, key = heapq.heappop(self.data_needed) try: ts = self.tasks[key] except KeyError: continue if ts.state != "fetch": continue if not ts.who_has: self.transition(ts, "missing", stimulus_id=stimulus_id) continue workers = [w for w in ts.who_has if w not in self.in_flight_workers] if not workers: assert ts.priority is not None skipped_worker_in_flight.append((ts.priority, ts.key)) 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)) assert worker != self.address to_gather, total_nbytes = self.select_keys_for_gather(worker, ts.key) self.log.append( ("gather-dependencies", worker, to_gather, stimulus_id, time()) ) self.comm_nbytes += total_nbytes self.in_flight_workers[worker] = to_gather recommendations = {self.tasks[d]: ("flight", worker) for d in to_gather} self.transitions(recommendations, stimulus_id=stimulus_id) self.loop.add_callback( self.gather_dep, worker=worker, to_gather=to_gather, total_nbytes=total_nbytes, stimulus_id=stimulus_id, ) for el in skipped_worker_in_flight: heapq.heappush(self.data_needed, el) def _get_task_finished_msg(self, ts): if ts.key not in and ts.key not in self.actors: raise RuntimeError(f"Task {ts} not ready") typ = ts.type if ts.nbytes is None or typ is None: try: value =[ts.key] except KeyError: value = self.actors[ts.key] ts.nbytes = sizeof(value) typ = ts.type = 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__, protocol=4) d = { "op": "task-finished", "status": "OK", "key": ts.key, "nbytes": ts.nbytes, "thread": self.threads.get(ts.key), "type": typ_serialized, "typename": typename(typ), "metadata": ts.metadata, } if ts.startstops: d["startstops"] = ts.startstops return d def _put_key_in_memory(self, ts, value, *, stimulus_id): """ Put a key into memory and set data related task state attributes. On success, generate recommendations for dependents. This method does not generate any scheduler messages since this method cannot distinguish whether it has to be an `add-task` or a `task-finished` signal. The caller is required to generate this message on success. Raises ------ TypeError: In case the data is put into the in memory buffer and an exception occurs during spilling, this raises an exception. This has to be handled by the caller since most callers generate scheduler messages on success (see comment above) but we need to signal that this was not successful. Can only trigger if spill to disk is enabled and the task is not an actor. """ if ts.key in ts.state = "memory" return {} recommendations = {} if ts.key in self.actors: self.actors[ts.key] = value else: start = time()[ts.key] = value stop = time() if stop - start > 0.020: ts.startstops.append( {"action": "disk-write", "start": start, "stop": stop} ) ts.state = "memory" if ts.nbytes is None: ts.nbytes = sizeof(value) ts.type = type(value) for dep in ts.dependents: dep.waiting_for_data.discard(ts) if not dep.waiting_for_data and dep.state == "waiting": self.waiting_for_data_count -= 1 recommendations[dep] = "ready" self.log.append((ts.key, "put-in-memory", stimulus_id, time())) return recommendations def select_keys_for_gather(self, worker, dep): assert isinstance(dep, str) deps = {dep} total_bytes = self.tasks[dep].get_nbytes() L = self.pending_data_per_worker[worker] while L: d = L.popleft() ts = self.tasks.get(d) if ts is None or ts.state != "fetch": continue if total_bytes + ts.get_nbytes() > self.target_message_size: break deps.add(d) total_bytes += ts.get_nbytes() return deps, total_bytes @property def total_comm_bytes(self): warnings.warn( "The attribute `Worker.total_comm_bytes` has been renamed to `comm_threshold_bytes`. " "Future versions will only support the new name.", DeprecationWarning, ) return self.comm_threshold_bytes def _filter_deps_for_fetch( self, to_gather_keys: Iterable[str] ) -> tuple[set[str], set[str], TaskState | None]: """Filter a list of keys before scheduling coroutines to fetch data from workers. Returns ------- in_flight_keys: The subset of keys in to_gather_keys in state `flight` or `resumed` cancelled_keys: The subset of tasks in to_gather_keys in state `cancelled` or `memory` cause: The task to attach startstops of this transfer to """ in_flight_tasks: set[TaskState] = set() cancelled_keys: set[str] = set() for key in to_gather_keys: ts = self.tasks.get(key) if ts is None: continue # At this point, a task has been transitioned fetch->flight # flight is only allowed to be transitioned into # {memory, resumed, cancelled} # resumed and cancelled will block any further transition until this # coro has been finished if ts.state in ("flight", "resumed"): in_flight_tasks.add(ts) # If the key is already in memory, the fetch should not happen which # is signalled by the cancelled_keys elif ts.state in {"cancelled", "memory"}: cancelled_keys.add(key) else: raise RuntimeError( f"Task {ts.key} found in illegal state {ts.state}. " "Only states `flight`, `resumed` and `cancelled` possible." ) # For diagnostics we want to attach the transfer to a single task. this # task is typically the next to be executed but since we're fetching # tasks for potentially many dependents, an exact match is not possible. # If there are no dependents, this is a pure replica fetch cause = None for ts in in_flight_tasks: if ts.dependents: cause = next(iter(ts.dependents)) break else: cause = ts in_flight_keys = {ts.key for ts in in_flight_tasks} return in_flight_keys, cancelled_keys, cause def _update_metrics_received_data( self, start: float, stop: float, data: dict, cause: TaskState, worker: str ) -> None: total_bytes = sum(self.tasks[key].get_nbytes() for key in data) cause.startstops.append( { "action": "transfer", "start": start + self.scheduler_delay, "stop": stop + self.scheduler_delay, "source": worker, } ) 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": {key: self.tasks[key].nbytes for key in data}, "total": total_bytes, "bandwidth": bandwidth, "who": worker, } ) if total_bytes > 1_000_000: 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, 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(data)) self.incoming_count += 1
[docs] async def gather_dep( self, worker: str, to_gather: Iterable[str], total_nbytes: int, *, stimulus_id, ): """Gather dependencies for a task from a worker who has them Parameters ---------- worker : str Address of worker to gather dependencies from to_gather : list Keys of dependencies to gather from worker -- this is not necessarily equivalent to the full list of dependencies of ``dep`` as some dependencies may already be present on this worker. total_nbytes : int Total number of bytes for all the dependencies in to_gather combined """ if self.status not in RUNNING: return recommendations: dict[TaskState, str | tuple] = {} with log_errors(): response = {} to_gather_keys: set[str] = set() cancelled_keys: set[str] = set() try: to_gather_keys, cancelled_keys, cause = self._filter_deps_for_fetch( to_gather ) if not to_gather_keys: self.log.append( ("nothing-to-gather", worker, to_gather, stimulus_id, time()) ) return assert cause # Keep namespace clean since this func is long and has many # dep*, *ts* variables del to_gather self.log.append( ("request-dep", worker, to_gather_keys, stimulus_id, time()) ) logger.debug( "Request %d keys for task %s from %s", len(to_gather_keys), cause, worker, ) start = time() response = await get_data_from_worker( self.rpc, to_gather_keys, worker, who=self.address ) stop = time() if response["status"] == "busy": return self._update_metrics_received_data( start=start, stop=stop, data=response["data"], cause=cause, worker=worker, ) self.log.append( ("receive-dep", worker, set(response["data"]), stimulus_id, time()) ) except OSError: logger.exception("Worker stream died during communication: %s", worker) has_what = self.has_what.pop(worker) self.pending_data_per_worker.pop(worker) self.log.append( ("receive-dep-failed", worker, has_what, stimulus_id, time()) ) for d in has_what: ts = self.tasks[d] ts.who_has.remove(worker) except Exception as e: logger.exception(e) if self.batched_stream and LOG_PDB: import pdb pdb.set_trace() msg = error_message(e) for k in self.in_flight_workers[worker]: ts = self.tasks[k] recommendations[ts] = tuple(msg.values()) raise finally: self.comm_nbytes -= total_nbytes busy = response.get("status", "") == "busy" data = response.get("data", {}) if busy: self.log.append( ("busy-gather", worker, to_gather_keys, stimulus_id, time()) ) for d in self.in_flight_workers.pop(worker): ts = self.tasks[d] ts.done = True if d in cancelled_keys: if ts.state == "cancelled": recommendations[ts] = "released" else: recommendations[ts] = "fetch" elif d in data: recommendations[ts] = ("memory", data[d]) elif busy: recommendations[ts] = "fetch" elif ts not in recommendations: ts.who_has.discard(worker) self.has_what[worker].discard(ts.key) self.log.append((d, "missing-dep", stimulus_id, time())) self.batched_stream.send( {"op": "missing-data", "errant_worker": worker, "key": d} ) recommendations[ts] = "fetch" del data, response self.transitions(recommendations, stimulus_id=stimulus_id) self.ensure_computing() if not busy: self.repetitively_busy = 0 else: # Exponential backoff to avoid hammering scheduler/worker self.repetitively_busy += 1 await asyncio.sleep(0.100 * 1.5 ** self.repetitively_busy) await self.query_who_has(*to_gather_keys, stimulus_id=stimulus_id) self.ensure_communicating()
async def find_missing(self): with log_errors(): if not self._missing_dep_flight: return try: if self.validate: for ts in self._missing_dep_flight: assert not ts.who_has stimulus_id = f"find-missing-{time()}" who_has = await retry_operation( self.scheduler.who_has, keys=[ts.key for ts in self._missing_dep_flight], ) who_has = {k: v for k, v in who_has.items() if v} self.update_who_has(who_has, stimulus_id=stimulus_id) finally: # This is quite arbitrary but the heartbeat has scaling implemented self.periodic_callbacks[ "find-missing" ].callback_time = self.periodic_callbacks["heartbeat"].callback_time self.ensure_communicating() self.ensure_computing() async def query_who_has( self, *deps: str, stimulus_id: str ) -> dict[str, Collection[str]]: with log_errors(): who_has = await retry_operation(self.scheduler.who_has, keys=deps) self.update_who_has(who_has, stimulus_id=stimulus_id) return who_has def update_who_has( self, who_has: dict[str, Collection[str]], *, stimulus_id: str ) -> None: try: recommendations = {} for dep, workers in who_has.items(): if not workers: continue if dep in self.tasks: if self.address in workers and self.tasks[dep].state != "memory": logger.debug( "Scheduler claims worker %s holds data for task %s which is not true.",, dep, ) # Do not mutate the input dict. That's rude workers = set(workers) - {self.address} dep_ts = self.tasks[dep] if dep_ts.state in FETCH_INTENDED: dep_ts.who_has.update(workers) if dep_ts.state == "missing": recommendations[dep_ts] = "fetch" for worker in workers: self.has_what[worker].add(dep) self.pending_data_per_worker[worker].append(dep_ts.key) self.transitions(recommendations, stimulus_id=stimulus_id) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise def handle_steal_request(self, key, stimulus_id): # There may be a race condition between stealing and releasing a task. # In this case the self.tasks is already cleared. The `None` will be # registered as `already-computing` on the other end ts = self.tasks.get(key) state = ts.state if ts is not None else None response = { "op": "steal-response", "key": key, "state": state, "stimulus_id": stimulus_id, } self.batched_stream.send(response) if state in READY | {"waiting"}: # If task is marked as "constrained" we haven't yet assigned it an # `available_resources` to run on, that happens in # `transition_constrained_executing` self.transition(ts, "released", stimulus_id=stimulus_id) def release_key( self, key: str, cause: TaskState | None = None, reason: str | None = None, report: bool = True, ) -> None: try: if self.validate: assert not isinstance(key, TaskState) ts = self.tasks[key] # needed for legacy notification support state_before = ts.state ts.state = "released" logger.debug( "Release key %s", {"key": key, "cause": cause, "reason": reason} ) if cause: self.log.append((key, "release-key", {"cause": cause}, reason, time())) else: self.log.append((key, "release-key", reason, time())) if key in try: del[key] except FileNotFoundError: logger.error("Tried to delete %s but no file found", exc_info=True) if key in self.actors: del self.actors[key] for worker in ts.who_has: self.has_what[worker].discard(ts.key) ts.who_has.clear() if key in self.threads: del self.threads[key] if ts.resource_restrictions is not None: if ts.state == "executing": for resource, quantity in ts.resource_restrictions.items(): self.available_resources[resource] += quantity for d in ts.dependencies: ts.waiting_for_data.discard(d) d.waiters.discard(ts) ts.waiting_for_data.clear() ts.nbytes = None ts._previous = None ts._next = None ts.done = False self._executing.discard(ts) self._in_flight_tasks.discard(ts) self._notify_plugins( "release_key", key, state_before, cause, reason, report ) except CommClosedError: # Batched stream send might raise if it was already closed pass except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise ################ # Execute Task # ################ def run(self, comm, function, args=(), wait=True, kwargs=None): 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, catch_errors=True): with log_errors(pdb=False): if isinstance(plugin, bytes): plugin = pickle.loads(plugin) if name is None: name = _get_plugin_name(plugin) assert name if name in self.plugins: await self.plugin_remove(comm=comm, name=name) self.plugins[name] = plugin"Starting Worker plugin %s" % name) if hasattr(plugin, "setup"): try: result = plugin.setup(worker=self) if isawaitable(result): result = await result except Exception as e: if not catch_errors: raise msg = error_message(e) return msg return {"status": "OK"} async def plugin_remove(self, comm=None, name=None): with log_errors(pdb=False):"Removing Worker plugin {name}") try: plugin = self.plugins.pop(name) if hasattr(plugin, "teardown"): result = plugin.teardown(worker=self) if isawaitable(result): 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: dict | None = None, ): kwargs = kwargs or {} separate_thread = kwargs.pop("separate_thread", True) key = actor actor = self.actors[key] func = getattr(actor, function) name = key_split(key) + "." + function try: if iscoroutinefunction(func): result = await func(*args, **kwargs) elif separate_thread: result = await self.loop.run_in_executor( self.executors["actor"], apply_function_actor, func, args, kwargs, self.execution_state, name, self.active_threads, self.active_threads_lock, ) else: result = func(*args, **kwargs) return {"status": "OK", "result": to_serialize(result)} except Exception as ex: return {"status": "error", "exception": to_serialize(ex)} def actor_attribute(self, comm=None, actor=None, attribute=None): try: value = getattr(self.actors[actor], attribute) return {"status": "OK", "result": to_serialize(value)} except Exception as ex: return {"status": "error", "exception": to_serialize(ex)} def meets_resource_constraints(self, key: str) -> bool: ts = self.tasks[key] if not ts.resource_restrictions: return True for resource, needed in ts.resource_restrictions.items(): if self.available_resources[resource] < needed: return False return True async def _maybe_deserialize_task(self, ts, *, stimulus_id): if not isinstance(ts.runspec, SerializedTask): return ts.runspec try: start = time() # Offload deserializing large tasks if sizeof(ts.runspec) > OFFLOAD_THRESHOLD: function, args, kwargs = await offload(_deserialize, *ts.runspec) else: function, args, kwargs = _deserialize(*ts.runspec) stop = time() if stop - start > 0.010: ts.startstops.append( {"action": "deserialize", "start": start, "stop": stop} ) return function, args, kwargs except Exception as e: logger.error("Could not deserialize task", exc_info=True) self.log.append((ts.key, "deserialize-error", stimulus_id, time())) emsg = error_message(e) emsg.pop("status") self.transition( ts, "error", **emsg, stimulus_id=stimulus_id, ) raise def ensure_computing(self): if self.status == Status.paused: return try: stimulus_id = f"ensure-computing-{time()}" while self.constrained and self.executing_count < self.nthreads: key = self.constrained[0] ts = self.tasks.get(key, None) if ts is None or ts.state != "constrained": self.constrained.popleft() continue if self.meets_resource_constraints(key): self.constrained.popleft() self.transition(ts, "executing", stimulus_id=stimulus_id) else: break while self.ready and self.executing_count < self.nthreads: priority, key = heapq.heappop(self.ready) ts = self.tasks.get(key) if ts is None: # It is possible for tasks to be released while still remaining on # `ready` The scheduler might have re-routed to a new worker and # told this worker to release. If the task has "disappeared" just # continue through the heap continue elif ts.key in self.transition(ts, "memory", stimulus_id=stimulus_id) elif ts.state in READY: self.transition(ts, "executing", stimulus_id=stimulus_id) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise async def execute(self, key, *, stimulus_id): if self.status in (Status.closing, Status.closed, Status.closing_gracefully): return if key not in self.tasks: return ts = self.tasks[key] try: if ts.state == "cancelled": # This might happen if keys are canceled logger.debug( "Trying to execute task %s which is not in executing state anymore", ts, ) ts.done = True self.transition(ts, "released", stimulus_id=stimulus_id) return if self.validate: assert not ts.waiting_for_data assert ts.state == "executing" assert ts.runspec is not None function, args, kwargs = await self._maybe_deserialize_task( ts, stimulus_id=stimulus_id ) args2, kwargs2 = self._prepare_args_for_execution(ts, args, kwargs) if ts.annotations is not None and "executor" in ts.annotations: executor = ts.annotations["executor"] else: executor = "default" assert executor in self.executors assert key == ts.key self.active_keys.add(ts.key) result: dict try: e = self.executors[executor] ts.start_time = time() if iscoroutinefunction(function): result = await apply_function_async( function, args2, kwargs2, self.scheduler_delay, ) elif "ThreadPoolExecutor" in str(type(e)): result = await self.loop.run_in_executor( e, apply_function, function, args2, kwargs2, self.execution_state, ts.key, self.active_threads, self.active_threads_lock, self.scheduler_delay, ) else: result = await self.loop.run_in_executor( e, apply_function_simple, function, args2, kwargs2, self.scheduler_delay, ) finally: self.active_keys.discard(ts.key) key = ts.key # key *must* be still in tasks. Releasing it direclty is forbidden # without going through cancelled ts = self.tasks.get(key) assert ts, self.story(key) ts.done = True result["key"] = ts.key value = result.pop("result", None) ts.startstops.append( {"action": "compute", "start": result["start"], "stop": result["stop"]} ) self.threads[ts.key] = result["thread"] recommendations = {} if result["op"] == "task-finished": ts.nbytes = result["nbytes"] ts.type = result["type"] recommendations[ts] = ("memory", value) if self.digests is not None: self.digests["task-duration"].add(result["stop"] - result["start"]) elif isinstance(result.pop("actual-exception"), Reschedule): recommendations[ts] = "rescheduled" else: logger.warning( "Compute Failed\n" "Function: %s\n" "args: %s\n" "kwargs: %s\n" "Exception: %r\n", str(funcname(function))[:1000], convert_args_to_str(args2, max_len=1000), convert_kwargs_to_str(kwargs2, max_len=1000), result["exception_text"], ) recommendations[ts] = ( "error", result["exception"], result["traceback"], result["exception_text"], result["traceback_text"], ) self.transitions(recommendations, stimulus_id=stimulus_id) logger.debug("Send compute response to scheduler: %s, %s", ts.key, result) if self.validate: assert ts.state != "executing" assert not ts.waiting_for_data except Exception as exc: assert ts logger.error( "Exception during execution of task %s.", ts.key, exc_info=True ) emsg = error_message(exc) emsg.pop("status") self.transition( ts, "error", **emsg, stimulus_id=stimulus_id, ) finally: self.ensure_computing() self.ensure_communicating() def _prepare_args_for_execution(self, ts, args, kwargs): start = time() data = {} for dep in ts.dependencies: k = dep.key try: data[k] =[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: ts.startstops.append({"action": "disk-read", "start": start, "stop": stop}) if self.digests is not None: self.digests["disk-load-duration"].add(stop - start) return args2, kwargs2 ################## # Administrative # ##################
[docs] 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 def check_pause(memory): 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 self.status == Status.running: logger.warning( "Worker is at %d%% memory usage. Pausing worker. " "Process memory: %s -- Worker memory limit: %s", int(frac * 100), format_bytes(memory), format_bytes(self.memory_limit) if self.memory_limit is not None else "None", ) self.status = Status.paused elif self.status == Status.paused: logger.warning( "Worker is at %d%% memory usage. Resuming worker. " "Process memory: %s -- Worker memory limit: %s", int(frac * 100), format_bytes(memory), format_bytes(self.memory_limit) if self.memory_limit is not None else "None", ) self.status = Status.running self.ensure_computing() check_pause(memory) # Dump data to disk if above 70% if self.memory_spill_fraction and frac > self.memory_spill_fraction: logger.debug( "Worker is at %.0f%% memory usage. Start spilling data to disk.", frac * 100, ) start = time() target = self.memory_limit * self.memory_target_fraction count = 0 need = memory - target while memory > target: if not logger.warning( "Unmanaged memory use is high. This may indicate a memory leak " "or the memory may not be released to the OS; see " " " "for more information. " "-- Unmanaged memory: %s -- Worker memory limit: %s", format_bytes(memory), format_bytes(self.memory_limit), ) break k, v, weight = del k, v total += weight count += 1 # If the current buffer is filled with a lot of small values, # evicting one at a time is very slow and the worker might # generate new data faster than it is able to evict. Therefore, # only pass on control if we spent at least 0.5s evicting if time() - start > 0.5: await asyncio.sleep(0) start = time() 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 check_pause(memory) if count: logger.debug( "Moved %d tasks worth %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()
[docs] 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/" ) profile.llprocess(llframe, None, state) profile.process( frame, True, self.profile_keys[key], stop="distributed/" ) stop = time() if self.digests is not None: self.digests["profile-duration"].add(stop - start)
async def get_profile( self, comm=None, start=None, stop=None, key=None, server=False ): now = time() + self.scheduler_delay if server: history = self.io_loop.profile elif 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 async def get_profile_metadata(self, comm=None, start=0, stop=None): add_recent = stop is None 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_plugins(self, method_name, *args, **kwargs): for name, plugin in self.plugins.items(): if hasattr(plugin, method_name): if method_name == "release_key": warnings.warn( "The `WorkerPlugin.release_key` hook is depreacted and will be " "removed in a future version. A similar event can now be " "caught by filtering for a `finish=='released'` event in the " "`WorkerPlugin.transition` hook.", DeprecationWarning, ) try: getattr(plugin, method_name)(*args, **kwargs) except Exception: "Plugin '%s' failed with exception", name, exc_info=True ) ############## # Validation # ############## def validate_task_memory(self, ts): assert ts.key in or ts.key in self.actors assert isinstance(ts.nbytes, int) assert not ts.waiting_for_data assert ts.key not in self.ready assert ts.state == "memory" def validate_task_executing(self, ts): assert ts.state == "executing" assert ts.runspec is not None assert ts.key not in assert not ts.waiting_for_data for dep in ts.dependencies: assert dep.state == "memory", self.story(dep) assert dep.key in or dep.key in self.actors def validate_task_ready(self, ts): assert ts.key in pluck(1, self.ready) assert ts.key not in assert ts.state != "executing" assert not ts.done assert not ts.waiting_for_data assert all( dep.key in or dep.key in self.actors for dep in ts.dependencies ) def validate_task_waiting(self, ts): assert ts.key not in assert ts.state == "waiting" assert not ts.done if ts.dependencies and ts.runspec: assert not all(dep.key in for dep in ts.dependencies) def validate_task_flight(self, ts): assert ts.key not in assert ts in self._in_flight_tasks assert not any(dep.key in self.ready for dep in ts.dependents) assert ts.coming_from assert ts.coming_from in self.in_flight_workers assert ts.key in self.in_flight_workers[ts.coming_from] def validate_task_fetch(self, ts): assert ts.key not in assert self.address not in ts.who_has assert not ts.done for w in ts.who_has: assert ts.key in self.has_what[w] def validate_task_missing(self, ts): assert ts.key not in assert not ts.who_has assert not ts.done assert not any(ts.key in has_what for has_what in self.has_what.values()) assert ts.key in self._missing_dep_flight def validate_task_cancelled(self, ts): assert ts.key not in assert ts._previous assert ts._next def validate_task_resumed(self, ts): assert ts.key not in assert ts._next assert ts._previous def validate_task_released(self, ts): assert ts.key not in assert not ts._next assert not ts._previous assert ts not in self._executing assert ts not in self._in_flight_tasks assert ts not in self._missing_dep_flight assert ts not in self._missing_dep_flight assert not ts.who_has assert not any(ts.key in has_what for has_what in self.has_what.values()) assert not ts.waiting_for_data assert not ts.done assert not ts.exception assert not ts.traceback def validate_task(self, ts): try: if ts.key in self.tasks: assert self.tasks[ts.key] == ts if ts.state == "memory": self.validate_task_memory(ts) elif ts.state == "waiting": self.validate_task_waiting(ts) elif ts.state == "missing": self.validate_task_missing(ts) elif ts.state == "cancelled": self.validate_task_cancelled(ts) elif ts.state == "resumed": self.validate_task_resumed(ts) elif ts.state == "ready": self.validate_task_ready(ts) elif ts.state == "executing": self.validate_task_executing(ts) elif ts.state == "flight": self.validate_task_flight(ts) elif ts.state == "fetch": self.validate_task_fetch(ts) elif ts.state == "released": self.validate_task_released(ts) except Exception as e: logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise AssertionError( f"Invalid TaskState encountered for {ts!r}.\nStory:\n{self.story(ts)}\n" ) from e def validate_state(self): if self.status not in RUNNING: return try: assert self.executing_count >= 0 waiting_for_data_count = 0 for ts in self.tasks.values(): assert ts.state is not None # check that worker has task for worker in ts.who_has: assert ts.key in self.has_what[worker] # check that deps have a set state and that dependency<->dependent links # are there for dep in ts.dependencies: # self.tasks was just a dict of tasks # and this check was originally that the key was in `task_state` # so we may have popped the key out of `self.tasks` but the # dependency can still be in `memory` before GC grabs it...? # Might need better bookkeeping assert dep.state is not None assert ts in dep.dependents, ts if ts.waiting_for_data: waiting_for_data_count += 1 for ts_wait in ts.waiting_for_data: assert ts_wait.key in self.tasks assert ( ts_wait.state in READY | {"executing", "flight", "fetch", "missing"} or ts_wait.key in self._missing_dep_flight or ts_wait.who_has.issubset(self.in_flight_workers) ), (ts, ts_wait, self.story(ts), self.story(ts_wait)) if ts.state == "memory": assert isinstance(ts.nbytes, int) assert not ts.waiting_for_data assert ts.key in or ts.key in self.actors assert self.waiting_for_data_count == waiting_for_data_count for worker, keys in self.has_what.items(): for k in keys: assert worker in self.tasks[k].who_has for ts in self.tasks.values(): self.validate_task(ts) except Exception as e: self.loop.add_callback(self.close) logger.exception(e) if LOG_PDB: import pdb pdb.set_trace() raise ####################################### # Worker Clients (advanced workloads) # ####################################### @property def client(self) -> Client: with self._lock: if self._client: return self._client else: return self._get_client() def _get_client(self, timeout: float | None = None) -> Client: """Get local client attached to this worker If no such client exists, create one See Also -------- get_client """ if timeout is None: timeout = dask.config.get("distributed.comm.timeouts.connect") timeout = parse_timedelta(timeout, "s") try: from .client import default_client client = default_client() except ValueError: # no clients found, need to make a new one pass else: # must be lazy import otherwise cyclic import from distributed.deploy.cluster import Cluster if ( client.scheduler and client.scheduler.address == self.scheduler.address # The below conditions should only happen in case a second # cluster is alive, e.g. if a submitted task spawned its onwn # LocalCluster, see gh4565 or ( isinstance(client._start_arg, str) and client._start_arg == self.scheduler.address or isinstance(client._start_arg, Cluster) and client._start_arg.scheduler_address == self.scheduler.address ) ): self._client = client if not self._client: from .client import Client asynchronous = in_async_call(self.loop) self._client = Client( self.scheduler, loop=self.loop,, set_as_default=True, asynchronous=asynchronous, direct_to_workers=True, name="worker", timeout=timeout, ) Worker._initialized_clients.add(self._client) if not asynchronous: assert self._client.status == "running" return self._client
[docs] def get_current_task(self) -> str: """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()]
def get_worker() -> 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://' 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 in RUNNING) except StopIteration: raise ValueError("No workers found")
[docs]def get_client(address=None, timeout=None, resolve_address=True) -> Client: """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 or str Timeout (in seconds) for getting the Client. Defaults to the ``distributed.comm.timeouts.connect`` configuration value. resolve_address : bool, default True Whether to resolve `address` to its canonical form. Returns ------- Client Examples -------- >>> def f(): ... client = get_client(timeout="10s") ... futures = 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 timeout is None: timeout = dask.config.get("distributed.comm.timeouts.connect") timeout = parse_timedelta(timeout, "s") 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 Client try: client = Client.current() # TODO: assumes the same scheduler except ValueError: client = None if client and (not address or client.scheduler.address == address): return client elif address: 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 = # 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, worker.tasks[thread_state.key], compute_duration=duration, stimulus_id=f"secede-{thread_state.key}-{time()}", )
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. """ def parse_memory_limit(memory_limit, nthreads, total_cores=CPU_COUNT) -> int | None: if memory_limit is None: return None if memory_limit == "auto": memory_limit = int(system.MEMORY_LIMIT * min(1, nthreads / total_cores)) with suppress(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_dep utils_comm.gather_data_from_workers """ if serializers is None: serializers = rpc.serializers if deserializers is None: deserializers = rpc.deserializers async def _get_data(): comm = await rpc.connect(worker) = "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") return response finally: rpc.reuse(worker, comm) return await retry_operation(_get_data, operation="get_data_from_worker") job_counter = [0] cache_loads = LRU(maxsize=100) def loads_function(bytes_object): """Load a function from bytes, cache bytes""" if len(bytes_object) < 100000: try: result = cache_loads[bytes_object] except KeyError: result = pickle.loads(bytes_object) cache_loads[bytes_object] = result return result return pickle.loads(bytes_object) 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 = loads_function(function) if args and isinstance(args, bytes): args = pickle.loads(args) if kwargs and isinstance(kwargs, bytes): 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 cache_dumps = LRU(maxsize=100) _cache_lock = threading.Lock() def dumps_function(func) -> bytes: """Dump a function to bytes, cache functions""" try: with _cache_lock: result = cache_dumps[func] except KeyError: result = pickle.dumps(func, protocol=4) if len(result) < 100000: with _cache_lock: cache_dumps[func] = result except TypeError: # Unhashable function result = pickle.dumps(func, protocol=4) 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, protocol=4) 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 msg = apply_function_simple(function, args, kwargs, time_delay) with active_threads_lock: del active_threads[ident] return msg def apply_function_simple( function, args, kwargs, time_delay, ): """Run a function, collect information Returns ------- msg: dictionary with status, result/error, timings, etc.. """ ident = threading.get_ident() 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 return msg async def apply_function_async( function, args, kwargs, time_delay, ): """Run a function, collect information Returns ------- msg: dictionary with status, result/error, timings, etc.. """ ident = threading.get_ident() start = time() try: result = await 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 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 = True 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: 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: int | None = None) -> str: """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: dict, max_len: int | None = None) -> str: """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)) async def run(server, comm, function, args=(), kwargs=None, is_coro=None, wait=True): kwargs = kwargs or {} 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"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: if nvml.device_get_count() < 1: raise RuntimeError except (Exception, RuntimeError): pass else: async def gpu_metric(worker): result = await 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 def print(*args, **kwargs): """Dask print function This prints both wherever this function is run, and also in the user's client session """ try: worker = get_worker() except ValueError: pass else: msg = { "args": tuple(stringify(arg) for arg in args), "kwargs": {k: stringify(v) for k, v in kwargs.items()}, } worker.log_event("print", msg) builtins.print(*args, **kwargs) def warn(*args, **kwargs): """Dask warn function This raises a warning both wherever this function is run, and also in the user's client session """ try: worker = get_worker() except ValueError: pass else: worker.log_event("warn", {"args": args, "kwargs": kwargs}) warnings.warn(*args, **kwargs)