from __future__ import annotations
import asyncio
import bisect
import builtins
import contextlib
import contextvars
import errno
import logging
import math
import os
import pathlib
import random
import sys
import threading
import warnings
import weakref
from collections import defaultdict, deque
from collections.abc import (
Callable,
Collection,
Container,
Hashable,
Iterable,
Mapping,
MutableMapping,
)
from concurrent.futures import Executor
from contextlib import suppress
from datetime import timedelta
from functools import wraps
from inspect import isawaitable
from typing import (
TYPE_CHECKING,
Any,
ClassVar,
Literal,
TextIO,
TypedDict,
TypeVar,
cast,
)
from tlz import keymap, pluck
from tornado.ioloop import IOLoop
import dask
from dask.core import istask
from dask.system import CPU_COUNT
from dask.utils import (
apply,
format_bytes,
funcname,
key_split,
parse_bytes,
parse_timedelta,
tmpdir,
typename,
)
from distributed import preloading, profile, utils
from distributed.batched import BatchedSend
from distributed.collections import LRU
from distributed.comm import Comm, connect, get_address_host, parse_address
from distributed.comm import resolve_address as comm_resolve_address
from distributed.comm.addressing import address_from_user_args
from distributed.compatibility import PeriodicCallback
from distributed.core import (
ConnectionPool,
ErrorMessage,
OKMessage,
PooledRPCCall,
Status,
coerce_to_address,
context_meter_to_server_digest,
error_message,
pingpong,
)
from distributed.core import rpc as RPCType
from distributed.core import send_recv
from distributed.diagnostics import nvml, rmm
from distributed.diagnostics.plugin import WorkerPlugin, _get_plugin_name
from distributed.diskutils import WorkSpace
from distributed.exceptions import Reschedule
from distributed.http import get_handlers
from distributed.metrics import context_meter, thread_time, time
from distributed.node import ServerNode
from distributed.proctitle import setproctitle
from distributed.protocol import pickle, to_serialize
from distributed.protocol.serialize import _is_dumpable
from distributed.pubsub import PubSubWorkerExtension
from distributed.security import Security
from distributed.sizeof import safe_sizeof as sizeof
from distributed.spans import SpansWorkerExtension
from distributed.threadpoolexecutor import ThreadPoolExecutor
from distributed.threadpoolexecutor import secede as tpe_secede
from distributed.utils import (
TimeoutError,
_maybe_complex,
get_ip,
has_arg,
in_async_call,
is_python_shutting_down,
iscoroutinefunction,
json_load_robust,
log_errors,
offload,
parse_ports,
recursive_to_dict,
run_in_executor_with_context,
set_thread_state,
silence_logging_cmgr,
thread_state,
wait_for,
)
from distributed.utils_comm import gather_from_workers, pack_data, retry_operation
from distributed.utils_perf import disable_gc_diagnosis, enable_gc_diagnosis
from distributed.versions import get_versions
from distributed.worker_memory import (
DeprecatedMemoryManagerAttribute,
DeprecatedMemoryMonitor,
WorkerDataParameter,
WorkerMemoryManager,
)
from distributed.worker_state_machine import (
AcquireReplicasEvent,
BaseWorker,
CancelComputeEvent,
ComputeTaskEvent,
DeprecatedWorkerStateAttribute,
ExecuteFailureEvent,
ExecuteSuccessEvent,
FindMissingEvent,
FreeKeysEvent,
GatherDepBusyEvent,
GatherDepFailureEvent,
GatherDepNetworkFailureEvent,
GatherDepSuccessEvent,
PauseEvent,
RefreshWhoHasEvent,
RemoveReplicasEvent,
RemoveWorkerEvent,
RescheduleEvent,
RetryBusyWorkerEvent,
SecedeEvent,
StateMachineEvent,
StealRequestEvent,
TaskState,
UnpauseEvent,
UpdateDataEvent,
WorkerState,
)
if TYPE_CHECKING:
# FIXME import from typing (needs Python >=3.10)
from typing_extensions import ParamSpec
# Circular imports
from distributed.client import Client
from distributed.nanny import Nanny
from distributed.scheduler import T_runspec
P = ParamSpec("P")
T = TypeVar("T")
logger = logging.getLogger(__name__)
LOG_PDB = dask.config.get("distributed.admin.pdb-on-err")
DEFAULT_EXTENSIONS: dict[str, type] = {
"pubsub": PubSubWorkerExtension,
"spans": SpansWorkerExtension,
}
DEFAULT_METRICS: dict[str, Callable[[Worker], Any]] = {}
DEFAULT_STARTUP_INFORMATION: dict[str, Callable[[Worker], Any]] = {}
WORKER_ANY_RUNNING = {
Status.running,
Status.paused,
Status.closing_gracefully,
}
class GetDataBusy(TypedDict):
status: Literal["busy"]
class GetDataSuccess(TypedDict):
status: Literal["OK"]
data: dict[str, object]
def fail_hard(method: Callable[P, T]) -> Callable[P, T]:
"""
Decorator to close the worker if this method encounters an exception.
"""
reason = f"worker-{method.__name__}-fail-hard"
if iscoroutinefunction(method):
@wraps(method)
async def wrapper(self, *args: P.args, **kwargs: P.kwargs) -> Any:
try:
return await method(self, *args, **kwargs) # type: ignore
except Exception as e:
if self.status not in (Status.closed, Status.closing):
self.log_event("worker-fail-hard", error_message(e))
logger.exception(e)
await _force_close(self, reason)
raise
else:
@wraps(method)
def wrapper(self, *args: P.args, **kwargs: P.kwargs) -> T:
try:
return method(self, *args, **kwargs)
except Exception as e:
if self.status not in (Status.closed, Status.closing):
self.log_event("worker-fail-hard", error_message(e))
logger.exception(e)
self.loop.add_callback(_force_close, self, reason)
raise
return wrapper # type: ignore
async def _force_close(self, reason: str):
"""
Used with the fail_hard decorator defined above
1. Wait for a worker to close
2. If it doesn't, log and kill the process
"""
try:
await wait_for(
self.close(nanny=False, executor_wait=False, reason=reason),
30,
)
except (KeyboardInterrupt, SystemExit): # pragma: nocover
raise
except BaseException: # pragma: nocover
# Worker is in a very broken state if closing fails. We need to shut down
# immediately, to ensure things don't get even worse and this worker potentially
# deadlocks the cluster.
from distributed import Scheduler
if Scheduler._instances:
# We're likely in a unit test. Don't kill the whole test suite!
raise
logger.critical(
"Error trying close worker in response to broken internal state. "
"Forcibly exiting worker NOW",
exc_info=True,
)
# use `os._exit` instead of `sys.exit` because of uncertainty
# around propagating `SystemExit` from asyncio callbacks
os._exit(1)
[docs]class Worker(BaseWorker, 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 that 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:** ``PooledRPCCall``:
Location of scheduler. See ``.ip/.port`` attributes.
* **name:** ``string``:
Alias
* **services:** ``{str: Server}``:
Auxiliary web servers running on this worker
* **service_ports:** ``{str: port}``:
* **transfer_outgoing_count_limit**: ``int``
The maximum number of concurrent outgoing data transfers.
See also
:attr:`distributed.worker_state_machine.WorkerState.transfer_incoming_count_limit`.
* **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.
* **threads**: ``{key: int}``
The ID of the thread on which the task ran
* **active_threads**: ``{int: key}``
The keys currently running on active threads
* **state**: ``WorkerState``
Encapsulated state machine. See
:class:`~distributed.worker_state_machine.BaseWorker` and
:class:`~distributed.worker_state_machine.WorkerState`
Parameters
----------
scheduler_ip: str, optional
scheduler_port: int, optional
scheduler_file: str, optional
host: str, optional
data: MutableMapping, type, None
The object to use for storage, builds a disk-backed LRU dict by default.
If a callable to construct the storage object is provided, it
will receive the worker's attr:``local_directory`` as an
argument if the calling signature has an argument named
``worker_local_directory``.
nthreads: int, optional
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 distributed.worker.memory.target)
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)
max_spill: int, string or False
Limit of number of bytes to be spilled on disk.
(default: read from config key distributed.worker.memory.max-spill)
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 127.0.0.1:8786
$ dask worker 127.0.0.1:8786
Start worker at: 127.0.0.1:1234
Registered with scheduler at: 127.0.0.1:8786
See Also
--------
distributed.scheduler.Scheduler
distributed.nanny.Nanny
"""
_instances: ClassVar[weakref.WeakSet[Worker]] = weakref.WeakSet()
_initialized_clients: ClassVar[weakref.WeakSet[Client]] = weakref.WeakSet()
nanny: Nanny | None
_lock: threading.Lock
transfer_outgoing_count_limit: int
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]]]
transfer_incoming_log: deque[dict[str, Any]]
transfer_outgoing_log: deque[dict[str, Any]]
#: Total number of data transfers to other workers since the worker was started
transfer_outgoing_count_total: int
#: Total size of data transfers to other workers (including in-progress and failed transfers)
transfer_outgoing_bytes_total: int
#: Current total size of open data transfers to other workers
transfer_outgoing_bytes: int
#: Current number of open data transfers to other workers
transfer_outgoing_count: int
bandwidth: float
latency: float
profile_cycle_interval: float
workspace: WorkSpace
_client: Client | None
bandwidth_workers: defaultdict[str, tuple[float, int]]
bandwidth_types: defaultdict[type, tuple[float, int]]
preloads: preloading.PreloadManager
contact_address: str | None
_start_port: int | str | Collection[int] | None = None
_start_host: str | None
_interface: str | None
_protocol: str
_dashboard_address: str | None
_dashboard: bool
_http_prefix: str
death_timeout: float | None
lifetime: float | None
lifetime_stagger: float | None
lifetime_restart: bool
extensions: dict
security: Security
connection_args: dict[str, Any]
loop: IOLoop
executors: dict[str, Executor]
batched_stream: BatchedSend
name: Any
scheduler_delay: float
stream_comms: dict[str, BatchedSend]
heartbeat_interval: float
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: PooledRPCCall
execution_state: dict[str, Any]
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,
nthreads: int | None = None,
loop: IOLoop | None = None, # Deprecated
local_directory: str | None = None,
services: dict | None = None,
name: Any | None = None,
reconnect: bool | None = None,
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,
heartbeat_interval: Any = "1s",
extensions: dict[str, type] | None = None,
metrics: Mapping[str, Callable[[Worker], Any]] = DEFAULT_METRICS,
startup_information: Mapping[
str, Callable[[Worker], Any]
] = DEFAULT_STARTUP_INFORMATION,
interface: str | None = None,
host: str | None = None,
port: int | str | Collection[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,
transition_counter_max: int | Literal[False] = False,
###################################
# Parameters to WorkerMemoryManager
memory_limit: str | float = "auto",
# Allow overriding the dict-like that stores the task outputs.
# This is meant for power users only. See WorkerMemoryManager for details.
data: WorkerDataParameter = None,
# Deprecated parameters; please use dask config instead.
memory_target_fraction: float | Literal[False] | None = None,
memory_spill_fraction: float | Literal[False] | None = None,
memory_pause_fraction: float | Literal[False] | None = None,
###################################
# Parameters to Server
scheduler_sni: str | None = None,
**kwargs,
):
if reconnect is not None:
if reconnect:
raise ValueError(
"The `reconnect=True` option for `Worker` has been removed. "
"To improve cluster stability, workers now always shut down in the face of network disconnects. "
"For details, or if this is an issue for you, see https://github.com/dask/distributed/issues/6350."
)
else:
warnings.warn(
"The `reconnect` argument to `Worker` is deprecated, and will be removed in a future release. "
"Worker reconnection is now always disabled, so passing `reconnect=False` is unnecessary. "
"See https://github.com/dask/distributed/issues/6350 for details.",
DeprecationWarning,
stacklevel=2,
)
if loop is not None:
warnings.warn(
"The `loop` argument to `Worker` is ignored, and will be removed in a future release. "
"The Worker always binds to the current loop",
DeprecationWarning,
stacklevel=2,
)
self.__exit_stack = stack = contextlib.ExitStack()
self.nanny = nanny
self._lock = threading.Lock()
transfer_incoming_count_limit = dask.config.get(
"distributed.worker.connections.outgoing"
)
self.transfer_outgoing_count_limit = dask.config.get(
"distributed.worker.connections.incoming"
)
transfer_message_bytes_limit = parse_bytes(
dask.config.get("distributed.worker.transfer.message-bytes-limit")
)
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)
if validate is None:
validate = dask.config.get("distributed.worker.validate")
self.transfer_incoming_log = deque(maxlen=100000)
self.transfer_outgoing_log = deque(maxlen=100000)
self.transfer_outgoing_count_total = 0
self.transfer_outgoing_bytes_total = 0
self.transfer_outgoing_bytes = 0
self.transfer_outgoing_count = 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)
self.death_timeout = parse_timedelta(death_timeout)
self.contact_address = contact_address
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
nthreads = nthreads or CPU_COUNT
if resources is None:
resources = dask.config.get("distributed.worker.resources")
assert isinstance(resources, dict)
self.extensions = {}
if silence_logs:
stack.enter_context(silence_logging_cmgr(level=silence_logs))
if isinstance(security, dict):
security = Security(**security)
self.security = security or Security()
assert isinstance(self.security, Security)
self.connection_args = self.security.get_connection_args("worker")
self.loop = self.io_loop = IOLoop.current()
if scheduler_sni:
self.connection_args["server_hostname"] = scheduler_sni
# 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(
nthreads, thread_name_prefix="Dask-Default-Threads"
)
self.batched_stream = BatchedSend(interval="2ms", loop=self.loop)
self.name = name
self.scheduler_delay = 0
self.stream_comms = {}
self.plugins = {}
self._pending_plugins = plugins
self.services = {}
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": self.run,
"run_coroutine": self.run_coroutine,
"get_data": self.get_data,
"update_data": self.update_data,
"free_keys": self._handle_remote_stimulus(FreeKeysEvent),
"terminate": self.close,
"ping": pingpong,
"upload_file": self.upload_file,
"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,
"benchmark_disk": self.benchmark_disk,
"benchmark_memory": self.benchmark_memory,
"benchmark_network": self.benchmark_network,
"get_story": self.get_story,
}
stream_handlers = {
"close": self.close,
"cancel-compute": self._handle_remote_stimulus(CancelComputeEvent),
"acquire-replicas": self._handle_remote_stimulus(AcquireReplicasEvent),
"compute-task": self._handle_remote_stimulus(ComputeTaskEvent),
"free-keys": self._handle_remote_stimulus(FreeKeysEvent),
"remove-replicas": self._handle_remote_stimulus(RemoveReplicasEvent),
"steal-request": self._handle_remote_stimulus(StealRequestEvent),
"refresh-who-has": self._handle_remote_stimulus(RefreshWhoHasEvent),
"worker-status-change": self.handle_worker_status_change,
"remove-worker": self._handle_remove_worker,
}
ServerNode.__init__(
self,
handlers=handlers,
stream_handlers=stream_handlers,
connection_args=self.connection_args,
local_directory=local_directory,
**kwargs,
)
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, timeout=self.death_timeout)
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))
if protocol is None:
protocol_address = scheduler_addr.split("://")
if len(protocol_address) == 2:
protocol = protocol_address[0]
assert protocol
self._protocol = protocol
self.memory_manager = WorkerMemoryManager(
self,
data=data,
nthreads=nthreads,
memory_limit=memory_limit,
memory_target_fraction=memory_target_fraction,
memory_spill_fraction=memory_spill_fraction,
memory_pause_fraction=memory_pause_fraction,
)
transfer_incoming_bytes_limit = math.inf
transfer_incoming_bytes_fraction = dask.config.get(
"distributed.worker.memory.transfer"
)
if (
self.memory_manager.memory_limit is not None
and transfer_incoming_bytes_fraction is not False
):
transfer_incoming_bytes_limit = int(
self.memory_manager.memory_limit * transfer_incoming_bytes_fraction
)
state = WorkerState(
nthreads=nthreads,
data=self.memory_manager.data,
threads=self.threads,
plugins=self.plugins,
resources=resources,
transfer_incoming_count_limit=transfer_incoming_count_limit,
validate=validate,
transition_counter_max=transition_counter_max,
transfer_incoming_bytes_limit=transfer_incoming_bytes_limit,
transfer_message_bytes_limit=transfer_message_bytes_limit,
)
BaseWorker.__init__(self, state)
self.scheduler = self.rpc(scheduler_addr)
self.execution_state = {
"scheduler": self.scheduler.address,
"ioloop": self.loop,
"worker": self,
}
self.heartbeat_interval = parse_timedelta(heartbeat_interval, default="ms")
pc = PeriodicCallback(self.heartbeat, self.heartbeat_interval * 1000)
self.periodic_callbacks["heartbeat"] = pc
pc = PeriodicCallback(lambda: self.batched_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
if extensions is None:
extensions = DEFAULT_EXTENSIONS
self.extensions = {
name: extension(self) for name, extension in extensions.items()
}
setproctitle("dask worker [not started]")
if dask.config.get("distributed.worker.profile.enabled"):
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
if lifetime is None:
lifetime = dask.config.get("distributed.worker.lifetime.duration")
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 lifetime:
lifetime += (random.random() * 2 - 1) * lifetime_stagger
self.io_loop.call_later(
lifetime, self.close_gracefully, reason="worker-lifetime-reached"
)
self.lifetime = lifetime
Worker._instances.add(self)
################
# Memory manager
################
memory_manager: WorkerMemoryManager
@property
def data(self) -> MutableMapping[str, object]:
"""{task key: task payload} of all completed tasks, whether they were computed
on this Worker or computed somewhere else and then transferred here over the
network.
When using the default configuration, this is a zict buffer that automatically
spills to disk whenever the target threshold is exceeded.
If spilling is disabled, it is a plain dict instead.
It could also be a user-defined arbitrary dict-like passed when initialising
the Worker or the Nanny.
Worker logic should treat this opaquely and stick to the MutableMapping API.
.. note::
This same collection is also available at ``self.state.data`` and
``self.memory_manager.data``.
"""
return self.memory_manager.data
# Deprecated attributes moved to self.memory_manager.<name>
memory_limit = DeprecatedMemoryManagerAttribute()
memory_target_fraction = DeprecatedMemoryManagerAttribute()
memory_spill_fraction = DeprecatedMemoryManagerAttribute()
memory_pause_fraction = DeprecatedMemoryManagerAttribute()
memory_monitor = DeprecatedMemoryMonitor()
###########################
# State machine accessors #
###########################
# Deprecated attributes moved to self.state.<name>
actors = DeprecatedWorkerStateAttribute()
available_resources = DeprecatedWorkerStateAttribute()
busy_workers = DeprecatedWorkerStateAttribute()
comm_nbytes = DeprecatedWorkerStateAttribute(target="transfer_incoming_bytes")
comm_threshold_bytes = DeprecatedWorkerStateAttribute(
target="transfer_incoming_bytes_throttle_threshold"
)
constrained = DeprecatedWorkerStateAttribute()
data_needed_per_worker = DeprecatedWorkerStateAttribute(target="data_needed")
executed_count = DeprecatedWorkerStateAttribute()
executing_count = DeprecatedWorkerStateAttribute()
generation = DeprecatedWorkerStateAttribute()
has_what = DeprecatedWorkerStateAttribute()
incoming_count = DeprecatedWorkerStateAttribute(
target="transfer_incoming_count_total"
)
in_flight_tasks = DeprecatedWorkerStateAttribute(target="in_flight_tasks_count")
in_flight_workers = DeprecatedWorkerStateAttribute()
log = DeprecatedWorkerStateAttribute()
long_running = DeprecatedWorkerStateAttribute()
nthreads = DeprecatedWorkerStateAttribute()
stimulus_log = DeprecatedWorkerStateAttribute()
stimulus_story = DeprecatedWorkerStateAttribute()
story = DeprecatedWorkerStateAttribute()
ready = DeprecatedWorkerStateAttribute()
tasks = DeprecatedWorkerStateAttribute()
target_message_size = DeprecatedWorkerStateAttribute(
target="transfer_message_bytes_limit"
)
total_out_connections = DeprecatedWorkerStateAttribute(
target="transfer_incoming_count_limit"
)
total_resources = DeprecatedWorkerStateAttribute()
transition_counter = DeprecatedWorkerStateAttribute()
transition_counter_max = DeprecatedWorkerStateAttribute()
validate = DeprecatedWorkerStateAttribute()
validate_task = DeprecatedWorkerStateAttribute()
@property
def data_needed(self) -> set[TaskState]:
warnings.warn(
"The `Worker.data_needed` attribute has been removed; "
"use `Worker.state.data_needed[address]`",
FutureWarning,
)
return {ts for tss in self.state.data_needed.values() for ts in tss}
@property
def waiting_for_data_count(self) -> int:
warnings.warn(
"The `Worker.waiting_for_data_count` attribute has been removed; "
"use `len(Worker.state.waiting)`",
FutureWarning,
)
return len(self.state.waiting)
##################
# Administrative #
##################
def __repr__(self) -> str:
name = f", name: {self.name}" if self.name != self.address_safe else ""
return (
f"<{self.__class__.__name__} {self.address_safe!r}{name}, "
f"status: {self.status.name}, "
f"stored: {len(self.data)}, "
f"running: {self.state.executing_count}/{self.state.nthreads}, "
f"ready: {len(self.state.ready)}, "
f"comm: {self.state.in_flight_tasks_count}, "
f"waiting: {len(self.state.waiting)}>"
)
@property
def logs(self):
return self._deque_handler.deque
[docs] def log_event(self, topic: str | Collection[str], msg: Any) -> None:
"""Log an event under a given topic
Parameters
----------
topic : str, list[str]
Name of the topic under which to log an event. To log the same
event under multiple topics, pass a list of topic names.
msg
Event message to log. Note this must be msgpack serializable.
See also
--------
Client.log_event
"""
if not _is_dumpable(msg):
raise TypeError(
f"Message must be msgpack serializable. Got {type(msg)=} instead."
)
full_msg = {
"op": "log-event",
"topic": topic,
"msg": msg,
}
if self.thread_id == threading.get_ident():
self.batched_send(full_msg)
else:
self.loop.add_callback(self.batched_send, full_msg)
@property
def worker_address(self):
"""For API compatibility with Nanny"""
return self.address
@property
def executor(self):
return self.executors["default"]
@ServerNode.status.setter # type: ignore
def status(self, value: Status) -> None:
"""Override Server.status to notify the Scheduler of status changes.
Also handles pausing/unpausing.
"""
prev_status = self.status
ServerNode.status.__set__(self, value) # type: ignore
stimulus_id = f"worker-status-change-{time()}"
self._send_worker_status_change(stimulus_id)
if prev_status == Status.running and value != Status.running:
self.handle_stimulus(PauseEvent(stimulus_id=stimulus_id))
elif value == Status.running and prev_status in (
Status.paused,
Status.closing_gracefully,
):
self.handle_stimulus(UnpauseEvent(stimulus_id=stimulus_id))
def _send_worker_status_change(self, stimulus_id: str) -> None:
self.batched_send(
{
"op": "worker-status-change",
"status": self._status.name,
"stimulus_id": stimulus_id,
},
)
async def get_metrics(self) -> dict:
try:
spilled_memory, spilled_disk = self.data.spilled_total # type: ignore
except AttributeError:
# spilling is disabled
spilled_memory, spilled_disk = 0, 0
# Send Fine Performance Metrics
# Make sure we do not yield the event loop between the moment we parse
# self.digests_total_since_heartbeat to send it to the scheduler and the moment
# we clear it!
spans_ext: SpansWorkerExtension | None = self.extensions.get("spans")
if spans_ext:
# Send metrics with disaggregated span_id
spans_ext.collect_digests()
# Send metrics with squashed span_id
digests: defaultdict[Hashable, float] = defaultdict(float)
for k, v in self.digests_total_since_heartbeat.items():
if isinstance(k, tuple) and k[0] == "execute":
k = k[:1] + k[2:]
digests[k] += v
self.digests_total_since_heartbeat.clear()
out = dict(
task_counts=self.state.task_counter.current_count(by_prefix=False),
bandwidth={
"total": self.bandwidth,
"workers": dict(self.bandwidth_workers),
"types": keymap(typename, self.bandwidth_types),
},
digests_total_since_heartbeat=dict(digests),
managed_bytes=self.state.nbytes,
spilled_bytes={
"memory": spilled_memory,
"disk": spilled_disk,
},
transfer={
"incoming_bytes": self.state.transfer_incoming_bytes,
"incoming_count": self.state.transfer_incoming_count,
"incoming_count_total": self.state.transfer_incoming_count_total,
"outgoing_bytes": self.transfer_outgoing_bytes,
"outgoing_count": self.transfer_outgoing_count,
"outgoing_count_total": self.transfer_outgoing_count_total,
},
event_loop_interval=self._tick_interval_observed,
)
monitor_recent = self.monitor.recent()
# Convert {foo.bar: 123} to {foo: {bar: 123}}
for k, v in monitor_recent.items():
if "." in k:
k0, _, k1 = k.partition(".")
out.setdefault(k0, {})[k1] = v
else:
out[k] = v
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):
return {
"type": type(self).__name__,
"id": self.id,
"scheduler": self.scheduler.address,
"nthreads": self.state.nthreads,
"memory_limit": self.memory_manager.memory_limit,
}
def _to_dict(self, *, exclude: Container[str] = ()) -> dict:
"""Dictionary representation for debugging purposes.
Not type stable and not intended for roundtrips.
See also
--------
Worker.identity
Client.dump_cluster_state
distributed.utils.recursive_to_dict
"""
info = super()._to_dict(exclude=exclude)
extra = {
"status": self.status,
"logs": self.get_logs(),
"config": dask.config.config,
"transfer_incoming_log": self.transfer_incoming_log,
"transfer_outgoing_log": self.transfer_outgoing_log,
}
extra = {k: v for k, v in extra.items() if k not in exclude}
info.update(extra)
info.update(self.state._to_dict(exclude=exclude))
info.update(self.memory_manager._to_dict(exclude=exclude))
return recursive_to_dict(info, exclude=exclude)
#####################
# External Services #
#####################
[docs] def batched_send(self, msg: dict[str, Any]) -> None:
"""Implements BaseWorker abstract method.
Send a fire-and-forget message to the scheduler through bulk comms.
If we're not currently connected to the scheduler, the message will be silently
dropped!
See also
--------
distributed.worker_state_machine.BaseWorker.batched_send
"""
if (
self.batched_stream
and self.batched_stream.comm
and not self.batched_stream.comm.closed()
):
self.batched_stream.send(msg)
async def _register_with_scheduler(self) -> None:
self.periodic_callbacks["keep-alive"].stop()
self.periodic_callbacks["heartbeat"].stop()
start = time()
if self.contact_address is None:
self.contact_address = self.address
logger.info("-" * 49)
# Worker reconnection is not supported
assert not self.data
assert not self.state.tasks
while True:
try:
_start = time()
comm = await connect(self.scheduler.address, **self.connection_args)
comm.name = "Worker->Scheduler"
comm._server = weakref.ref(self)
await comm.write(
dict(
op="register-worker",
reply=False,
address=self.contact_address,
status=self.status.name,
nthreads=self.state.nthreads,
name=self.name,
now=time(),
resources=self.state.total_resources,
memory_limit=self.memory_manager.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(),
stimulus_id=f"worker-connect-{time()}",
server_id=self.id,
),
serializers=["msgpack"],
)
future = comm.read(deserializers=["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
break
except OSError:
logger.info("Waiting to connect to: %26s", self.scheduler.address)
await asyncio.sleep(0.1)
except TimeoutError: # pragma: no cover
logger.info("Timed out when connecting to scheduler")
if response["status"] != "OK":
await comm.close()
msg = response["message"] if "message" in response else repr(response)
logger.error(f"Unable to connect to scheduler: {msg}")
raise ValueError(f"Unexpected response from register: {response!r}")
self.batched_stream.start(comm)
self.status = Status.running
await asyncio.gather(
*(
self.plugin_add(name=name, plugin=plugin)
for name, plugin in response["worker-plugins"].items()
)
)
logger.info(" Registered to: %26s", self.scheduler.address)
logger.info("-" * 49)
self.periodic_callbacks["keep-alive"].start()
self.periodic_callbacks["heartbeat"].start()
self.loop.add_callback(self.handle_scheduler, comm)
def _update_latency(self, latency: float) -> None:
self.latency = latency * 0.05 + self.latency * 0.95
self.digest_metric("latency", latency)
async def heartbeat(self) -> None:
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 - cast(float, self.state.tasks[key].start_time)
for key in self.active_keys
if key in self.state.tasks
},
extensions={
name: extension.heartbeat()
for name, extension in self.extensions.items()
if hasattr(extension, "heartbeat")
},
)
end = time()
middle = (start + end) / 2
self._update_latency(end - start)
if response["status"] == "missing":
# Scheduler thought we left. Reconnection is not supported, so just shut down.
logger.error(
f"Scheduler was unaware of this worker {self.address!r}. Shutting down."
)
# Something is out of sync; have the nanny restart us if possible.
await self.close(nanny=False)
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 OSError:
logger.exception("Failed to communicate with scheduler during heartbeat.")
except Exception:
logger.exception("Unexpected exception during heartbeat. Closing worker.")
await self.close()
raise
@fail_hard
async def handle_scheduler(self, comm: Comm) -> None:
try:
await self.handle_stream(comm)
finally:
await self.close(reason="worker-handle-scheduler-connection-broken")
def keys(self) -> list[str]:
return list(self.data)
[docs] async def gather(self, who_has: dict[str, list[str]]) -> dict[str, Any]:
"""Endpoint used by Scheduler.rebalance() and Scheduler.replicate()"""
missing_keys = [k for k in who_has if k not in self.data]
failed_keys = []
missing_workers: set[str] = set()
stimulus_id = f"gather-{time()}"
while missing_keys:
to_gather = {}
for k in missing_keys:
workers = set(who_has[k]) - missing_workers
if workers:
to_gather[k] = workers
else:
failed_keys.append(k)
if not to_gather:
break
(
data,
missing_keys,
new_failed_keys,
new_missing_workers,
) = await gather_from_workers(
who_has=to_gather, rpc=self.rpc, who=self.address
)
self.update_data(data, stimulus_id=stimulus_id)
del data
failed_keys += new_failed_keys
missing_workers.update(new_missing_workers)
if missing_keys:
who_has = await retry_operation(
self.scheduler.who_has, keys=missing_keys
)
if failed_keys:
logger.error("Could not find data: %s", failed_keys)
return {"status": "partial-fail", "keys": list(failed_keys)}
else:
return {"status": "OK"}
def get_monitor_info(
self, recent: bool = False, start: int = 0
) -> dict[str, float]:
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 #
#############
[docs] async def start_unsafe(self):
await super().start_unsafe()
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,
security=self.security,
)
kwargs = self.security.get_listen_args("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 self.name is None:
self.name = self.address
await self.preloads.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}"
logger.info(" Start worker at: %26s", self.address)
logger.info(" Listening to: %26s", listening_address)
if self.name != self.address_safe:
# only if name was not None
logger.info(" Worker name: %26s", self.name)
for k, v in self.service_ports.items():
logger.info(" {:>16} at: {:>26}".format(k, self.ip + ":" + str(v)))
logger.info("Waiting to connect to: %26s", self.scheduler.address)
logger.info("-" * 49)
logger.info(" Threads: %26d", self.state.nthreads)
if self.memory_manager.memory_limit:
logger.info(
" Memory: %26s",
format_bytes(self.memory_manager.memory_limit),
)
logger.info(" 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 = ()
self.state.address = self.address
await self._register_with_scheduler()
self.start_periodic_callbacks()
return self
[docs] @log_errors
async def close( # type: ignore
self,
timeout: float = 30,
executor_wait: bool = True,
nanny: bool = True,
reason: str = "worker-close",
) -> str | None:
"""Close the worker
Close asynchronous operations running on the worker, stop all executors and
comms. If requested, this also closes the nanny.
Parameters
----------
timeout
Timeout in seconds for shutting down individual instructions
executor_wait
If True, shut down executors synchronously, otherwise asynchronously
nanny
If True, close the nanny
reason
Reason for closing the worker
Returns
-------
str | None
None if worker already in closing state or failed, "OK" otherwise
"""
# FIXME: The worker should not be allowed to close the nanny. Ownership
# is the other way round. If an external caller wants to close
# nanny+worker, the nanny must be notified first. ==> Remove kwarg
# nanny, see also Scheduler.retire_workers
if self.status in (Status.closed, Status.closing, Status.failed):
logger.debug(
"Attempted to close worker that is already %s. Reason: %s",
self.status,
reason,
)
await self.finished()
return None
if self.status == Status.init:
# If the worker is still in startup/init and is started by a nanny,
# this means the nanny itself is not up, yet. If the Nanny isn't up,
# yet, it's server will not accept any incoming RPC requests and
# will block until the startup is finished.
# Therefore, this worker trying to communicate with the Nanny during
# startup is not possible and we cannot close it.
# In this case, the Nanny will automatically close after inspecting
# the worker status
nanny = False
disable_gc_diagnosis()
try:
self.log_event(self.address, {"action": "closing-worker", "reason": reason})
except Exception:
# This can happen when the Server is not up yet
logger.exception("Failed to log closing event")
try:
logger.info("Stopping worker at %s. Reason: %s", self.address, reason)
except ValueError: # address not available if already closed
logger.info("Stopping worker. Reason: %s", reason)
if self.status not in WORKER_ANY_RUNNING:
logger.info("Closed worker has not yet started: %s", self.status)
if not executor_wait:
logger.info("Not waiting on executor to close")
# This also informs the scheduler about the status update
self.status = Status.closing
setproctitle("dask worker [closing]")
if nanny and self.nanny:
with self.rpc(self.nanny) as r:
await r.close_gracefully(reason=reason)
# Cancel async instructions
await BaseWorker.close(self, timeout=timeout)
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 extension in self.extensions.values():
if hasattr(extension, "close"):
result = extension.close()
if isawaitable(result):
await result
self.stop_services()
await self.preloads.teardown()
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 WORKER_ANY_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 heuristics 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()
await self._stop_listeners()
await self.rpc.close()
# 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": # pragma: no cover
await asyncio.sleep(0.2)
self.batched_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
def _close(executor, wait):
if isinstance(executor, ThreadPoolExecutor):
executor._work_queue.queue.clear()
executor.shutdown(wait=wait, timeout=timeout)
else:
executor.shutdown(wait=wait)
# Waiting for the shutdown can block the event loop causing
# weird deadlocks particularly if the task that is executing in
# the thread is waiting for a server reply, e.g. when using
# worker clients, semaphores, etc.
if is_python_shutting_down():
# If we're shutting down there is no need to wait for daemon
# threads to finish
_close(executor=executor, wait=False)
else:
try:
await asyncio.to_thread(
_close, executor=executor, wait=executor_wait
)
except RuntimeError: # Are we shutting down the process?
logger.error(
"Could not close executor %r by dispatching to thread. Trying synchronously.",
executor,
exc_info=True,
)
_close(
executor=executor, wait=executor_wait
) # Just run it directly
self.stop()
self.status = Status.closed
setproctitle("dask worker [closed]")
await ServerNode.close(self)
self.__exit_stack.__exit__(None, None, None)
return "OK"
[docs] async def close_gracefully(
self, restart=None, reason: str = "worker-close-gracefully"
):
"""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
logger.info("Closing worker gracefully: %s. Reason: %s", self.address, reason)
# Wait for all tasks to leave the worker and don't accept any new ones.
# Scheduler.retire_workers will set the status to closing_gracefully and push it
# back to this worker.
await self.scheduler.retire_workers(
workers=[self.address],
close_workers=False,
remove=False,
stimulus_id=f"worker-close-gracefully-{time()}",
)
if restart is None:
restart = self.lifetime_restart
await self.close(nanny=not restart, reason=reason)
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
)
comm.name = "Worker->Worker"
await comm.write({"op": "connection_stream"})
bcomm.start(comm)
self._ongoing_background_tasks.call_soon(batched_send_connect)
self.stream_comms[address].send(msg)
@context_meter_to_server_digest("get-data")
async def get_data(
self,
comm: Comm,
keys: Collection[str],
who: str | None = None,
serializers: list[str] | None = None,
) -> GetDataBusy | Literal[Status.dont_reply]:
max_connections = self.transfer_outgoing_count_limit
# Allow same-host connections more liberally
if 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 data transfers because worker is paused."
)
else:
throttle_msg = ""
if (
max_connections is not False
and self.transfer_outgoing_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.transfer_outgoing_count,
max_connections,
throttle_msg,
)
return {"status": "busy"}
self.transfer_outgoing_count += 1
self.transfer_outgoing_count_total += 1
# This may potentially take many seconds if it involves unspilling
data = {k: self.data[k] for k in keys if k in self.data}
if len(data) < len(keys):
for k in set(keys) - data.keys():
if k in self.state.actors:
from distributed.actor import Actor
data[k] = Actor(
type(self.state.actors[k]), self.address, k, worker=self
)
msg = {"status": "OK", "data": {k: to_serialize(v) for k, v in data.items()}}
# Note: `if k in self.data` above guarantees that
# k is in self.state.tasks too and that nbytes is non-None
bytes_per_task = {k: self.state.tasks[k].nbytes or 0 for k in data}
total_bytes = sum(bytes_per_task.values())
self.transfer_outgoing_bytes += total_bytes
self.transfer_outgoing_bytes_total += total_bytes
try:
with context_meter.meter("network", func=time) as m:
compressed = await comm.write(msg, serializers=serializers)
response = await comm.read(deserializers=serializers)
assert response == "OK", response
except OSError:
logger.exception(
"failed during get data with %s -> %s",
self.address,
who,
)
comm.abort()
raise
finally:
self.transfer_outgoing_bytes -= total_bytes
self.transfer_outgoing_count -= 1
# Not the same as m.delta, which doesn't include time spent
# serializing/deserializing
duration = max(0.001, m.stop - m.start)
self.transfer_outgoing_log.append(
{
"start": m.start + self.scheduler_delay,
"stop": m.stop + self.scheduler_delay,
"middle": (m.start + m.stop) / 2,
"duration": duration,
"who": who,
"keys": bytes_per_task,
"total": total_bytes,
"compressed": compressed,
"bandwidth": total_bytes / duration,
}
)
return Status.dont_reply
###################
# Local Execution #
###################
def update_data(
self,
data: dict[str, object],
stimulus_id: str | None = None,
) -> dict[str, Any]:
if stimulus_id is None:
stimulus_id = f"update-data-{time()}"
self.handle_stimulus(UpdateDataEvent(data=data, stimulus_id=stimulus_id))
return {"nbytes": {k: sizeof(v) for k, v in data.items()}, "status": "OK"}
async def set_resources(self, **resources: float) -> None:
for r, quantity in resources.items():
if r in self.state.total_resources:
self.state.available_resources[r] += (
quantity - self.state.total_resources[r]
)
else:
self.state.available_resources[r] = quantity
self.state.total_resources[r] = quantity
await retry_operation(
self.scheduler.set_resources,
resources=self.state.total_resources,
worker=self.contact_address,
)
@log_errors
async def plugin_add(
self,
plugin: WorkerPlugin | bytes,
name: str | None = None,
catch_errors: bool = True,
) -> ErrorMessage | OKMessage:
if isinstance(plugin, bytes):
plugin = pickle.loads(plugin)
if not isinstance(plugin, WorkerPlugin):
warnings.warn(
"Registering duck-typed plugins has been deprecated. "
"Please make sure your plugin subclasses `WorkerPlugin`.",
DeprecationWarning,
stacklevel=2,
)
plugin = cast(WorkerPlugin, plugin)
if name is None:
name = _get_plugin_name(plugin)
assert name
if name in self.plugins:
await self.plugin_remove(name=name)
self.plugins[name] = plugin
logger.info("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
return error_message(e)
return {"status": "OK"}
@log_errors
async def plugin_remove(self, name: str) -> ErrorMessage | OKMessage:
logger.info(f"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:
return error_message(e)
return {"status": "OK"}
def handle_worker_status_change(self, status: str, stimulus_id: str) -> None:
new_status = Status.lookup[status] # type: ignore
if (
new_status == Status.closing_gracefully
and self._status not in WORKER_ANY_RUNNING
):
logger.error(
"Invalid Worker.status transition: %s -> %s", self._status, new_status
)
# Reiterate the current status to the scheduler to restore sync
self._send_worker_status_change(stimulus_id)
else:
# Update status and send confirmation to the Scheduler (see status.setter)
self.status = new_status
###################
# Task Management #
###################
def _handle_remote_stimulus(
self, cls: type[StateMachineEvent]
) -> Callable[..., None]:
def _(**kwargs):
event = cls(**kwargs)
self.handle_stimulus(event)
_.__name__ = f"_handle_remote_stimulus({cls.__name__})"
return _
[docs] @fail_hard
def handle_stimulus(self, *stims: StateMachineEvent) -> None:
"""Override BaseWorker method for added validation
See also
--------
distributed.worker_state_machine.BaseWorker.handle_stimulus
distributed.worker_state_machine.WorkerState.handle_stimulus
"""
try:
super().handle_stimulus(*stims)
except Exception as e:
if hasattr(e, "to_event"):
topic, msg = e.to_event()
self.log_event(topic, msg)
raise
def stateof(self, key: str) -> dict[str, Any]:
ts = self.state.tasks[key]
return {
"executing": ts.state == "executing",
"waiting_for_data": bool(ts.waiting_for_data),
"heap": ts in self.state.ready or ts in self.state.constrained,
"data": key in self.data,
}
async def get_story(self, keys_or_stimuli: Iterable[str]) -> list[tuple]:
return self.state.story(*keys_or_stimuli)
##########################
# Dependencies gathering #
##########################
def _get_cause(self, keys: Iterable[str]) -> TaskState:
"""For diagnostics, we want to attach a 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. Additionally, if a key was
fetched through acquire-replicas, dependents may not be known at all.
Returns
-------
The task to attach startstops of this transfer to
"""
cause = None
for key in keys:
ts = self.state.tasks[key]
if ts.dependents:
return next(iter(ts.dependents))
cause = ts
assert cause # Always at least one key
return cause
def _update_metrics_received_data(
self,
start: float,
stop: float,
data: dict[str, object],
cause: TaskState,
worker: str,
) -> None:
total_bytes = sum(self.state.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 = max(0.001, stop - start)
bandwidth = total_bytes / duration
self.transfer_incoming_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.state.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)
self.digest_metric("transfer-bandwidth", total_bytes / duration)
self.digest_metric("transfer-duration", duration)
self.counters["transfer-count"].add(len(data))
[docs] @fail_hard
async def gather_dep(
self,
worker: str,
to_gather: Collection[str],
total_nbytes: int,
*,
stimulus_id: str,
) -> StateMachineEvent:
"""Implements BaseWorker abstract method
See also
--------
distributed.worker_state_machine.BaseWorker.gather_dep
"""
if self.status not in WORKER_ANY_RUNNING:
# This is only for the sake of coherence of the WorkerState;
# it should never actually reach the scheduler.
return GatherDepFailureEvent.from_exception(
RuntimeError("Worker is shutting down"),
worker=worker,
total_nbytes=total_nbytes,
stimulus_id=f"worker-closing-{time()}",
)
self.state.log.append(("request-dep", worker, to_gather, stimulus_id, time()))
logger.debug("Request %d keys from %s", len(to_gather), worker)
try:
with context_meter.meter("network", func=time) as m:
response = await get_data_from_worker(
rpc=self.rpc, keys=to_gather, worker=worker, who=self.address
)
if response["status"] == "busy":
self.state.log.append(
("gather-dep-busy", worker, to_gather, stimulus_id, time())
)
return GatherDepBusyEvent(
worker=worker,
total_nbytes=total_nbytes,
stimulus_id=f"gather-dep-busy-{time()}",
)
assert response["status"] == "OK"
cause = self._get_cause(to_gather)
self._update_metrics_received_data(
start=m.start,
stop=m.stop,
data=response["data"],
cause=cause,
worker=worker,
)
self.state.log.append(
("receive-dep", worker, set(response["data"]), stimulus_id, time())
)
return GatherDepSuccessEvent(
worker=worker,
total_nbytes=total_nbytes,
data=response["data"],
stimulus_id=f"gather-dep-success-{time()}",
)
except OSError:
logger.exception("Worker stream died during communication: %s", worker)
self.state.log.append(
("gather-dep-failed", worker, to_gather, stimulus_id, time())
)
return GatherDepNetworkFailureEvent(
worker=worker,
total_nbytes=total_nbytes,
stimulus_id=f"gather-dep-network-failure-{time()}",
)
except Exception as e:
# e.g. data failed to deserialize
# FIXME this will deadlock the cluster
# https://github.com/dask/distributed/issues/6705
logger.exception(e)
self.state.log.append(
("gather-dep-failed", worker, to_gather, stimulus_id, time())
)
if self.batched_stream and LOG_PDB:
import pdb
pdb.set_trace()
return GatherDepFailureEvent.from_exception(
e,
worker=worker,
total_nbytes=total_nbytes,
stimulus_id=f"gather-dep-failed-{time()}",
)
[docs] async def retry_busy_worker_later(self, worker: str) -> StateMachineEvent:
"""Wait some time, then take a peer worker out of busy state.
Implements BaseWorker abstract method.
See Also
--------
distributed.worker_state_machine.BaseWorker.retry_busy_worker_later
"""
await asyncio.sleep(0.15)
return RetryBusyWorkerEvent(
worker=worker, stimulus_id=f"retry-busy-worker-{time()}"
)
[docs] def digest_metric(self, name: Hashable, value: float) -> None:
"""Implement BaseWorker.digest_metric by calling Server.digest_metric"""
ServerNode.digest_metric(self, name, value)
@log_errors
def find_missing(self) -> None:
self.handle_stimulus(FindMissingEvent(stimulus_id=f"find-missing-{time()}"))
# This is quite arbitrary but the heartbeat has scaling implemented
self.periodic_callbacks["find-missing"].callback_time = self.periodic_callbacks[
"heartbeat"
].callback_time
################
# 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 actor_execute(
self,
actor=None,
function=None,
args=(),
kwargs: dict | None = None,
) -> dict[str, Any]:
kwargs = kwargs or {}
separate_thread = kwargs.pop("separate_thread", True)
key = actor
actor = self.state.actors[key]
func = getattr(actor, function)
name = key_split(key) + "." + function
try:
if iscoroutinefunction(func):
token = _worker_cvar.set(self)
try:
result = await func(*args, **kwargs)
finally:
_worker_cvar.reset(token)
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:
token = _worker_cvar.set(self)
try:
result = func(*args, **kwargs)
finally:
_worker_cvar.reset(token)
return {"status": "OK", "result": to_serialize(result)}
except Exception as ex:
return {"status": "error", "exception": to_serialize(ex)}
def actor_attribute(self, actor=None, attribute=None) -> dict[str, Any]:
try:
value = getattr(self.state.actors[actor], attribute)
return {"status": "OK", "result": to_serialize(value)}
except Exception as ex:
return {"status": "error", "exception": to_serialize(ex)}
[docs] @fail_hard
async def execute(self, key: str, *, stimulus_id: str) -> StateMachineEvent:
"""Execute a task. Implements BaseWorker abstract method.
See also
--------
distributed.worker_state_machine.BaseWorker.execute
"""
if self.status not in WORKER_ANY_RUNNING:
# This is just for internal coherence of the WorkerState; the reschedule
# message should not ever reach the Scheduler.
# It is still OK if it does though.
return RescheduleEvent(key=key, stimulus_id=f"worker-closing-{time()}")
# The key *must* be in the worker state thanks to the cancelled state
ts = self.state.tasks[key]
run_id = ts.run_id
try:
if self.state.validate:
assert not ts.waiting_for_data
assert ts.state in ("executing", "cancelled", "resumed"), ts
assert ts.run_spec is not None
function, args, kwargs = ts.run_spec
args2, kwargs2 = self._prepare_args_for_execution(ts, args, kwargs)
assert ts.annotations is not None
executor = ts.annotations.get("executor", "default")
try:
e = self.executors[executor]
except KeyError:
raise ValueError(
f"Invalid executor {executor!r}; "
f"expected one of: {sorted(self.executors)}"
)
self.active_keys.add(key)
# Propagate span (see distributed.spans). This is useful when spawning
# more tasks using worker_client() and for logging.
span_ctx = (
dask.annotate(span=ts.annotations["span"])
if "span" in ts.annotations
else contextlib.nullcontext()
)
span_ctx.__enter__()
try:
ts.start_time = time()
if iscoroutinefunction(function):
token = _worker_cvar.set(self)
try:
result = await apply_function_async(
function,
args2,
kwargs2,
self.scheduler_delay,
)
finally:
_worker_cvar.reset(token)
elif "ThreadPoolExecutor" in str(type(e)):
# The 'executor' time metric should be almost zero most of the time,
# e.g. thread synchronization overhead only, since thread-noncpu and
# thread-cpu inside the thread detract from it. However, it may
# become substantial in case of misalignment between the size of the
# thread pool and the number of running tasks in the worker state
# machine (e.g. https://github.com/dask/distributed/issues/5882)
with context_meter.meter("executor"):
result = await run_in_executor_with_context(
e,
apply_function,
function,
args2,
kwargs2,
self.execution_state,
key,
self.active_threads,
self.active_threads_lock,
self.scheduler_delay,
)
else:
# Can't capture contextvars across processes. If this is a
# ProcessPoolExecutor, the 'executor' time metric will show the
# whole runtime inside the executor.
with context_meter.meter("executor"):
result = await self.loop.run_in_executor(
e,
apply_function_simple,
function,
args2,
kwargs2,
self.scheduler_delay,
)
finally:
self.active_keys.discard(key)
span_ctx.__exit__(None, None, None)
self.threads[key] = result["thread"]
if result["op"] == "task-finished":
if self.digests is not None:
duration = max(0, result["stop"] - result["start"])
self.digests["task-duration"].add(duration)
return ExecuteSuccessEvent(
key=key,
run_id=run_id,
value=result["result"],
start=result["start"],
stop=result["stop"],
nbytes=result["nbytes"],
type=result["type"],
stimulus_id=f"task-finished-{time()}",
)
task_exc = result["actual-exception"]
if isinstance(task_exc, Reschedule):
return RescheduleEvent(key=ts.key, stimulus_id=f"reschedule-{time()}")
if (
self.status == Status.closing
and isinstance(task_exc, asyncio.CancelledError)
and iscoroutinefunction(function)
):
# `Worker.cancel` will cause async user tasks to raise `CancelledError`.
# Since we cancelled those tasks, we shouldn't treat them as failures.
# This is just a heuristic; it's _possible_ the task happened to
# fail independently with `CancelledError`.
logger.info(
f"Async task {key!r} cancelled during worker close; rescheduling."
)
return RescheduleEvent(
key=ts.key, stimulus_id=f"cancelled-by-worker-close-{time()}"
)
logger.warning(
"Compute Failed\n"
"Key: %s\n"
"Function: %s\n"
"args: %s\n"
"kwargs: %s\n"
"Exception: %r\n",
key,
str(funcname(function))[:1000],
convert_args_to_str(args2, max_len=1000),
convert_kwargs_to_str(kwargs2, max_len=1000),
result["exception_text"],
)
return ExecuteFailureEvent.from_exception(
result,
key=key,
run_id=run_id,
start=result["start"],
stop=result["stop"],
stimulus_id=f"task-erred-{time()}",
)
except Exception as exc:
logger.error("Exception during execution of task %s.", key, exc_info=True)
return ExecuteFailureEvent.from_exception(
exc,
key=key,
run_id=run_id,
stimulus_id=f"execute-unknown-error-{time()}",
)
def _prepare_args_for_execution(
self, ts: TaskState, args: tuple, kwargs: dict[str, Any]
) -> tuple[tuple[object, ...], dict[str, object]]:
start = time()
data = {}
for dep in ts.dependencies:
k = dep.key
try:
data[k] = self.data[k]
except KeyError:
from distributed.actor import Actor # TODO: create local actor
data[k] = Actor(type(self.state.actors[k]), self.address, k, self)
args2 = pack_data(args, data, key_types=(bytes, str, tuple))
kwargs2 = pack_data(kwargs, data, key_types=(bytes, str, tuple))
stop = time()
if stop - start > 0.005:
ts.startstops.append({"action": "disk-read", "start": start, "stop": stop})
return args2, kwargs2
##################
# Administrative #
##################
def cycle_profile(self) -> None:
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) -> None:
"""
Get a frame from all actively computing threads
Merge these frames into existing profile counts
"""
if not self.active_threads: # hope that this is thread-atomic?
return
start = time()
with self.active_threads_lock:
active_threads = self.active_threads.copy()
frames = sys._current_frames()
frames = {ident: frames[ident] for ident in active_threads}
llframes = {}
if self.low_level_profiler:
llframes = {ident: profile.ll_get_stack(ident) for ident in active_threads}
for ident, frame in frames.items():
if frame is not None:
key = key_split(active_threads[ident])
llframe = llframes.get(ident)
state = profile.process(
frame, True, self.profile_recent, stop="distributed/worker.py"
)
profile.llprocess(llframe, None, state)
profile.process(
frame, True, self.profile_keys[key], stop="distributed/worker.py"
)
stop = time()
self.digest_metric("profile-duration", stop - start)
async def get_profile(
self,
start=None,
stop=None,
key=None,
server: bool = 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, start: float = 0, stop: float | None = None
) -> dict[str, Any]:
add_recent = stop is None
now = time() + self.scheduler_delay
stop = stop or now
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, keys: Collection[str] | None = None) -> dict[str, Any]:
with self.active_threads_lock:
sys_frames = sys._current_frames()
frames = {key: sys_frames[tid] for tid, key in self.active_threads.items()}
if keys is not None:
frames = {key: frames[key] for key in keys if key in frames}
return {key: profile.call_stack(frame) for key, frame in frames.items()}
async def benchmark_disk(self) -> dict[str, float]:
return await self.loop.run_in_executor(
self.executor, benchmark_disk, self.local_directory
)
async def benchmark_memory(self) -> dict[str, float]:
return await self.loop.run_in_executor(self.executor, benchmark_memory)
async def benchmark_network(self, address: str) -> dict[str, float]:
return await benchmark_network(rpc=self.rpc, address=address)
#######################################
# 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 distributed.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 distributed.client import Client
asynchronous = in_async_call(self.loop)
self._client = Client(
self.scheduler,
loop=self.loop,
security=self.security,
set_as_default=True,
asynchronous=asynchronous,
direct_to_workers=True,
name="worker",
timeout=timeout,
)
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 _handle_remove_worker(self, worker: str, stimulus_id: str) -> None:
self.rpc.remove(worker)
self.handle_stimulus(RemoveWorkerEvent(worker=worker, stimulus_id=stimulus_id))
def validate_state(self) -> None:
try:
self.state.validate_state()
except Exception as e:
logger.error("Validate state failed", exc_info=e)
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
if hasattr(e, "to_event"):
topic, msg = e.to_event()
self.log_event(topic, msg)
raise
@property
def incoming_transfer_log(self):
warnings.warn(
"The `Worker.incoming_transfer_log` attribute has been renamed to "
"`Worker.transfer_incoming_log`",
DeprecationWarning,
stacklevel=2,
)
return self.transfer_incoming_log
@property
def outgoing_count(self):
warnings.warn(
"The `Worker.outgoing_count` attribute has been renamed to "
"`Worker.transfer_outgoing_count_total`",
DeprecationWarning,
stacklevel=2,
)
return self.transfer_outgoing_count_total
@property
def outgoing_current_count(self):
warnings.warn(
"The `Worker.outgoing_current_count` attribute has been renamed to "
"`Worker.transfer_outgoing_count`",
DeprecationWarning,
stacklevel=2,
)
return self.transfer_outgoing_count
@property
def outgoing_transfer_log(self):
warnings.warn(
"The `Worker.outgoing_transfer_log` attribute has been renamed to "
"`Worker.transfer_outgoing_log`",
DeprecationWarning,
stacklevel=2,
)
return self.transfer_outgoing_log
@property
def total_in_connections(self):
warnings.warn(
"The `Worker.total_in_connections` attribute has been renamed to "
"`Worker.transfer_outgoing_count_limit`",
DeprecationWarning,
stacklevel=2,
)
return self.transfer_outgoing_count_limit
_worker_cvar: contextvars.ContextVar[Worker] = contextvars.ContextVar("_worker_cvar")
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://127.0.0.1:47373'
See Also
--------
get_client
worker_client
"""
try:
return _worker_cvar.get()
except LookupError:
raise ValueError("No worker found") from None
[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 = client.map(lambda x: x + 1, range(10)) # spawn many tasks
... results = client.gather(futures)
... return sum(results)
>>> future = client.submit(f) # doctest: +SKIP
>>> future.result() # doctest: +SKIP
55
See Also
--------
get_worker
worker_client
secede
"""
if 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 distributed.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 = client.map(...) # do some remote work
... secede() # while that work happens, remove ourself from the pool
... return client.gather(futures) # return gathered results
See Also
--------
get_client
get_worker
"""
worker = get_worker()
tpe_secede() # have this thread secede from the thread pool
duration = time() - thread_state.start_time
worker.loop.add_callback(
worker.handle_stimulus,
SecedeEvent(
key=thread_state.key,
compute_duration=duration,
stimulus_id=f"secede-{time()}",
),
)
async def get_data_from_worker(
rpc: ConnectionPool,
keys: Collection[str],
worker: str,
*,
who: str | None = None,
serializers: list[str] | None = None,
deserializers: list[str] | None = None,
) -> GetDataBusy | GetDataSuccess:
"""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
comm = await rpc.connect(worker)
comm.name = "Ephemeral Worker->Worker for gather"
try:
response = await send_recv(
comm,
serializers=serializers,
deserializers=deserializers,
op="get_data",
keys=keys,
who=who,
)
try:
status = response["status"]
except KeyError: # pragma: no cover
raise ValueError("Unexpected response", response)
else:
if status == "OK":
await comm.write("OK")
return response
finally:
rpc.reuse(worker, comm)
def _normalize_task(task: Any) -> T_runspec:
if istask(task):
if task[0] is apply and not any(map(_maybe_complex, task[2:])):
return task[1], task[2], task[3] if len(task) == 4 else {}
elif not any(map(_maybe_complex, task[1:])):
return task[0], task[1:], {}
return execute_task, (task,), {}
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[Callable[..., Any], bytes] = 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)
if len(result) < 100000:
with _cache_lock:
cache_dumps[func] = result
except TypeError: # Unhashable function
result = pickle.dumps(func)
return result
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
with set_thread_state(
start_time=time(),
execution_state=execution_state,
key=key,
):
token = _worker_cvar.set(execution_state["worker"])
try:
msg = apply_function_simple(function, args, kwargs, time_delay)
finally:
_worker_cvar.reset(token)
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()
try:
# meter("thread-cpu").delta
# difference in thread_time() before and after function call, minus user calls
# to context_meter inside the function. Published to Server.digests as
# {("execute", <prefix>, "thread-cpu", "seconds"): <value>}
# m.delta
# difference in wall time before and after function call, minus thread-cpu,
# minus user calls to context_meter. Published to Server.digests as
# {("execute", <prefix>, "thread-noncpu", "seconds"): <value>}
# m.stop - m.start
# difference in wall time before and after function call, without subtracting
# anything. This is used in scheduler heuristics, e.g. task stealing.
with context_meter.meter("thread-noncpu", func=time) as m:
with context_meter.meter("thread-cpu", func=thread_time):
result = function(*args, **kwargs)
except (SystemExit, KeyboardInterrupt):
# Special-case these, just like asyncio does all over the place. They will pass
# through `fail_hard` and `_handle_stimulus_from_task`, and eventually be caught
# by special-case logic in asyncio:
# https://github.com/python/cpython/blob/v3.9.4/Lib/asyncio/events.py#L81-L82
# Any other `BaseException` types would ultimately be ignored by asyncio if
# raised here, after messing up the worker state machine along their way.
raise
except BaseException as e:
# Users _shouldn't_ use `BaseException`s, but if they do, we can assume they
# aren't a reason to shut down the whole system (since we allow the
# system-shutting-down `SystemExit` and `KeyboardInterrupt` to pass through)
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,
}
msg["start"] = m.start + time_delay
msg["stop"] = m.stop + 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()
try:
with context_meter.meter("thread-noncpu", func=time) as m:
result = await function(*args, **kwargs)
except (SystemExit, KeyboardInterrupt):
# Special-case these, just like asyncio does all over the place. They will pass
# through `fail_hard` and `_handle_stimulus_from_task`, and eventually be caught
# by special-case logic in asyncio:
# https://github.com/python/cpython/blob/v3.9.4/Lib/asyncio/events.py#L81-L82
# Any other `BaseException` types would ultimately be ignored by asyncio if
# raised here, after messing up the worker state machine along their way.
raise
except BaseException as e:
# NOTE: this includes `CancelledError`! Since it's a user task, that's _not_ a
# reason to shut down the worker.
# Users _shouldn't_ use `BaseException`s, but if they do, we can assume they
# aren't a reason to shut down the whole system (since we allow the
# system-shutting-down `SystemExit` and `KeyboardInterrupt` to pass through)
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,
}
msg["start"] = m.start + time_delay
msg["stop"] = m.stop + 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
with set_thread_state(
start_time=time(),
execution_state=execution_state,
key=key,
actor=True,
):
token = _worker_cvar.set(execution_state["worker"])
try:
result = function(*args, **kwargs)
finally:
_worker_cvar.reset(token)
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, wait=True):
kwargs = kwargs or {}
function = pickle.loads(function)
is_coro = iscoroutinefunction(function)
assert wait or is_coro, "Combination not supported"
if args:
args = pickle.loads(args)
if kwargs:
kwargs = pickle.loads(kwargs)
if has_arg(function, "dask_worker"):
kwargs["dask_worker"] = server
if has_arg(function, "dask_scheduler"):
kwargs["dask_scheduler"] = server
logger.info("Run out-of-band function %r", funcname(function))
try:
if not is_coro:
result = function(*args, **kwargs)
else:
if wait:
result = await function(*args, **kwargs)
else:
server._ongoing_background_tasks.call_soon(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
def add_gpu_metrics():
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
try:
import rmm as _rmm
except Exception:
pass
else:
async def rmm_metric(worker):
result = await offload(rmm.real_time)
return result
DEFAULT_METRICS["rmm"] = rmm_metric
del _rmm
def print(
*args,
sep: str | None = " ",
end: str | None = "\n",
file: TextIO | None = None,
flush: bool = False,
) -> None:
"""
A drop-in replacement of the built-in ``print`` function for remote printing
from workers to clients. If called from outside a dask worker, its arguments
are passed directly to ``builtins.print()``. If called by code running on a
worker, then in addition to printing locally, any clients connected
(possibly remotely) to the scheduler managing this worker will receive an
event instructing them to print the same output to their own standard output
or standard error streams. For example, the user can perform simple
debugging of remote computations by including calls to this ``print``
function in the submitted code and inspecting the output in a local Jupyter
notebook or interpreter session.
All arguments behave the same as those of ``builtins.print()``, with the
exception that the ``file`` keyword argument, if specified, must either be
``sys.stdout`` or ``sys.stderr``; arbitrary file-like objects are not
allowed.
All non-keyword arguments are converted to strings using ``str()`` and
written to the stream, separated by ``sep`` and followed by ``end``. Both
``sep`` and ``end`` must be strings; they can also be ``None``, which means
to use the default values. If no objects are given, ``print()`` will just
write ``end``.
Parameters
----------
sep : str, optional
String inserted between values, default a space.
end : str, optional
String appended after the last value, default a newline.
file : ``sys.stdout`` or ``sys.stderr``, optional
Defaults to the current sys.stdout.
flush : bool, default False
Whether to forcibly flush the stream.
Examples
--------
>>> from dask.distributed import Client, print
>>> client = distributed.Client(...)
>>> def worker_function():
... print("Hello from worker!")
>>> client.submit(worker_function)
<Future: finished, type: NoneType, key: worker_function-...>
Hello from worker!
"""
try:
worker = get_worker()
except ValueError:
pass
else:
# We are in a worker: prepare all of the print args and kwargs to be
# serialized over the wire to the client.
msg = {
# According to the Python stdlib docs, builtin print() simply calls
# str() on each positional argument, so we do the same here.
"args": tuple(map(str, args)),
"sep": sep,
"end": end,
"flush": flush,
}
if file == sys.stdout:
msg["file"] = 1 # type: ignore
elif file == sys.stderr:
msg["file"] = 2 # type: ignore
elif file is not None:
raise TypeError(
f"Remote printing to arbitrary file objects is not supported. file "
f"kwarg must be one of None, sys.stdout, or sys.stderr; got: {file!r}"
)
worker.log_event("print", msg)
builtins.print(*args, sep=sep, end=end, file=file, flush=flush)
def warn(
message: str | Warning,
category: type[Warning] | None = UserWarning,
stacklevel: int = 1,
source: Any = None,
) -> None:
"""
A drop-in replacement of the built-in ``warnings.warn()`` function for
issuing warnings remotely from workers to clients.
If called from outside a dask worker, its arguments are passed directly to
``warnings.warn()``. If called by code running on a worker, then in addition
to emitting a warning locally, any clients connected (possibly remotely) to
the scheduler managing this worker will receive an event instructing them to
emit the same warning (subject to their own local filters, etc.). When
implementing computations that may run on a worker, the user can call this
``warn`` function to ensure that any remote client sessions will see their
warnings, for example in a Jupyter output cell.
While all of the arguments are respected by the locally emitted warning
(with same meanings as in ``warnings.warn()``), ``stacklevel`` and
``source`` are ignored by clients because they would not be meaningful in
the client's thread.
Examples
--------
>>> from dask.distributed import Client, warn
>>> client = Client()
>>> def do_warn():
... warn("A warning from a worker.")
>>> client.submit(do_warn).result()
/path/to/distributed/client.py:678: UserWarning: A warning from a worker.
"""
try:
worker = get_worker()
except ValueError: # pragma: no cover
pass
else:
# We are in a worker: log a warn event with args serialized to the
# client. We have to pickle message and category into bytes ourselves
# because msgpack cannot handle them. The expectations is that these are
# always small objects.
worker.log_event(
"warn",
{
"message": pickle.dumps(message),
"category": pickle.dumps(category),
# We ignore stacklevel because it will be meaningless in the
# client's thread/process.
# We ignore source because we don't want to serialize arbitrary
# objects.
},
)
# add 1 to stacklevel so that, at least in the worker's local stderr, we'll
# see the source line that called us
warnings.warn(message, category, stacklevel + 1, source)
def benchmark_disk(
rootdir: str | None = None,
sizes: Iterable[str] = ("1 kiB", "100 kiB", "1 MiB", "10 MiB", "100 MiB"),
duration="1 s",
) -> dict[str, float]:
"""
Benchmark disk bandwidth
Returns
-------
out: dict
Maps sizes of outputs to measured bandwidths
"""
duration = parse_timedelta(duration)
out = {}
for size_str in sizes:
with tmpdir(dir=rootdir) as dir:
dir = pathlib.Path(dir)
names = list(map(str, range(100)))
size = parse_bytes(size_str)
data = random.randbytes(size)
start = time()
total = 0
while time() < start + duration:
with open(dir / random.choice(names), mode="ab") as f:
f.write(data)
f.flush()
os.fsync(f.fileno())
total += size
out[size_str] = total / (time() - start)
return out
def benchmark_memory(
sizes: Iterable[str] = ("2 kiB", "10 kiB", "100 kiB", "1 MiB", "10 MiB"),
duration="200 ms",
) -> dict[str, float]:
"""
Benchmark memory bandwidth
Returns
-------
out: dict
Maps sizes of outputs to measured bandwidths
"""
duration = parse_timedelta(duration)
out = {}
for size_str in sizes:
size = parse_bytes(size_str)
data = random.randbytes(size)
start = time()
total = 0
while time() < start + duration:
_ = data[:-1]
del _
total += size
out[size_str] = total / (time() - start)
return out
async def benchmark_network(
address: str,
rpc: ConnectionPool | Callable[[str], RPCType],
sizes: Iterable[str] = ("1 kiB", "10 kiB", "100 kiB", "1 MiB", "10 MiB", "50 MiB"),
duration="1 s",
) -> dict[str, float]:
"""
Benchmark network communications to another worker
Returns
-------
out: dict
Maps sizes of outputs to measured bandwidths
"""
duration = parse_timedelta(duration)
out = {}
async with rpc(address) as r:
for size_str in sizes:
size = parse_bytes(size_str)
data = to_serialize(random.randbytes(size))
start = time()
total = 0
while time() < start + duration:
await r.echo(data=data)
total += size * 2
out[size_str] = total / (time() - start)
return out