from __future__ import annotations
import asyncio
import bisect
import builtins
import errno
import heapq
import logging
import os
import random
import sys
import threading
import warnings
import weakref
from collections import defaultdict, deque, namedtuple
from collections.abc import Callable, Collection, Iterable, Mapping, MutableMapping
from concurrent.futures import Executor
from contextlib import suppress
from datetime import timedelta
from inspect import isawaitable
from pickle import PicklingError
from typing import TYPE_CHECKING, Any, ClassVar, Container
if TYPE_CHECKING:
from typing_extensions import Literal
from .diagnostics.plugin import WorkerPlugin
from .actor import Actor
from .client import Client
from .nanny import Nanny
from tlz import first, keymap, merge, pluck # noqa: F401
from tornado.ioloop import IOLoop, PeriodicCallback
import dask
from dask.core import istask
from dask.system import CPU_COUNT
from dask.utils import (
apply,
format_bytes,
funcname,
parse_bytes,
parse_timedelta,
stringify,
typename,
)
from . import comm, preloading, profile, shuffle, system, utils
from .batched import BatchedSend
from .comm import Comm, connect, get_address_host
from .comm.addressing import address_from_user_args, parse_address
from .comm.utils import OFFLOAD_THRESHOLD
from .core import (
CommClosedError,
Status,
coerce_to_address,
error_message,
pingpong,
send_recv,
)
from .diagnostics import nvml
from .diagnostics.plugin import _get_plugin_name
from .diskutils import WorkDir, WorkSpace
from .http import get_handlers
from .metrics import time
from .node import ServerNode
from .proctitle import setproctitle
from .protocol import pickle, to_serialize
from .pubsub import PubSubWorkerExtension
from .security import Security
from .sizeof import safe_sizeof as sizeof
from .threadpoolexecutor import ThreadPoolExecutor
from .threadpoolexecutor import secede as tpe_secede
from .utils import (
LRU,
TimeoutError,
_maybe_complex,
get_ip,
has_arg,
import_file,
in_async_call,
iscoroutinefunction,
json_load_robust,
key_split,
log_errors,
offload,
parse_ports,
recursive_to_dict,
silence_logging,
thread_state,
warn_on_duration,
)
from .utils_comm import gather_from_workers, pack_data, retry_operation
from .utils_perf import ThrottledGC, disable_gc_diagnosis, enable_gc_diagnosis
from .versions import get_versions
logger = logging.getLogger(__name__)
LOG_PDB = dask.config.get("distributed.admin.pdb-on-err")
no_value = "--no-value-sentinel--"
# TaskState.state subsets
PROCESSING = {
"waiting",
"ready",
"constrained",
"executing",
"long-running",
"cancelled",
"resumed",
}
READY = {"ready", "constrained"}
FETCH_INTENDED = {"missing", "fetch", "flight", "cancelled", "resumed"}
# Worker.status subsets
RUNNING = {Status.running, Status.paused, Status.closing_gracefully}
DEFAULT_EXTENSIONS: list[type] = [PubSubWorkerExtension]
if shuffle.SHUFFLE_AVAILABLE:
DEFAULT_EXTENSIONS.append(shuffle.ShuffleWorkerExtension)
DEFAULT_METRICS: dict[str, Callable[[Worker], Any]] = {}
DEFAULT_STARTUP_INFORMATION: dict[str, Callable[[Worker], Any]] = {}
DEFAULT_DATA_SIZE = parse_bytes(
dask.config.get("distributed.scheduler.default-data-size")
)
SerializedTask = namedtuple("SerializedTask", ["function", "args", "kwargs", "task"])
_taskstate_to_dict_guard = False
class InvalidTransition(Exception):
pass
class TaskState:
"""Holds volatile state relating to an individual Dask task
* **dependencies**: ``set(TaskState instances)``
The data needed by this key to run
* **dependents**: ``set(TaskState instances)``
The keys that use this dependency.
* **duration**: ``float``
Expected duration the a task
* **priority**: ``tuple``
The priority this task given by the scheduler. Determines run order.
* **state**: ``str``
The current state of the task. One of ["waiting", "ready", "executing",
"fetch", "memory", "flight", "long-running", "rescheduled", "error"]
* **who_has**: ``set(worker)``
Workers that we believe have this data
* **coming_from**: ``str``
The worker that current task data is coming from if task is in flight
* **waiting_for_data**: ``set(keys of dependencies)``
A dynamic version of dependencies. All dependencies that we still don't
have for a particular key.
* **resource_restrictions**: ``{str: number}``
Abstract resources required to run a task
* **exception**: ``str``
The exception caused by running a task if it erred
* **traceback**: ``str``
The exception caused by running a task if it erred
* **type**: ``type``
The type of a particular piece of data
* **suspicious_count**: ``int``
The number of times a dependency has not been where we expected it
* **startstops**: ``[{startstop}]``
Log of transfer, load, and compute times for a task
* **start_time**: ``float``
Time at which task begins running
* **stop_time**: ``float``
Time at which task finishes running
* **metadata**: ``dict``
Metadata related to task. Stored metadata should be msgpack
serializable (e.g. int, string, list, dict).
* **nbytes**: ``int``
The size of a particular piece of data
* **annotations**: ``dict``
Task annotations
Parameters
----------
key: str
runspec: SerializedTask
A named tuple containing the ``function``, ``args``, ``kwargs`` and
``task`` associated with this `TaskState` instance. This defaults to
``None`` and can remain empty if it is a dependency that this worker
will receive from another worker.
"""
def __init__(self, key, runspec=None):
assert key is not None
self.key = key
self.runspec = runspec
self.dependencies = set()
self.dependents = set()
self.duration = None
self.priority = None
self.state = "released"
self.who_has = set()
self.coming_from = None
self.waiting_for_data = set()
self.waiters = set()
self.resource_restrictions = {}
self.exception = None
self.exception_text = ""
self.traceback = None
self.traceback_text = ""
self.type = None
self.suspicious_count = 0
self.startstops = []
self.start_time = None
self.stop_time = None
self.metadata = {}
self.nbytes = None
self.annotations = None
self.done = False
self._previous = None
self._next = None
def __repr__(self):
return f"<TaskState {self.key!r} {self.state}>"
def get_nbytes(self) -> int:
nbytes = self.nbytes
return nbytes if nbytes is not None else DEFAULT_DATA_SIZE
def _to_dict(self, *, exclude: Container[str] = ()) -> dict | str:
"""
A very verbose dictionary representation for debugging purposes.
Not type stable and not intended for roundtrips.
Parameters
----------
comm:
exclude:
A list of attributes which must not be present in the output.
See also
--------
Client.dump_cluster_state
"""
# When a task references another task, just print the task repr. All tasks
# should neatly appear under Worker.tasks. This also prevents a RecursionError
# during particularly heavy loads, which have been observed to happen whenever
# there's an acyclic dependency chain of ~200+ tasks.
global _taskstate_to_dict_guard
if _taskstate_to_dict_guard:
return repr(self)
_taskstate_to_dict_guard = True
try:
return recursive_to_dict(
{k: v for k, v in self.__dict__.items() if k not in exclude},
exclude=exclude,
)
finally:
_taskstate_to_dict_guard = False
def is_protected(self) -> bool:
return self.state in PROCESSING or any(
dep_ts.state in PROCESSING for dep_ts in self.dependents
)
[docs]class Worker(ServerNode):
"""Worker node in a Dask distributed cluster
Workers perform two functions:
1. **Serve data** from a local dictionary
2. **Perform computation** on that data and on data from peers
Workers keep the scheduler informed of their data and use that scheduler to
gather data from other workers when necessary to perform a computation.
You can start a worker with the ``dask-worker`` command line application::
$ dask-worker scheduler-ip:port
Use the ``--help`` flag to see more options::
$ dask-worker --help
The rest of this docstring is about the internal state the the worker uses
to manage and track internal computations.
**State**
**Informational State**
These attributes don't change significantly during execution.
* **nthreads:** ``int``:
Number of nthreads used by this worker process
* **executors:** ``dict[str, concurrent.futures.Executor]``:
Executors used to perform computation. Always contains the default
executor.
* **local_directory:** ``path``:
Path on local machine to store temporary files
* **scheduler:** ``rpc``:
Location of scheduler. See ``.ip/.port`` attributes.
* **name:** ``string``:
Alias
* **services:** ``{str: Server}``:
Auxiliary web servers running on this worker
* **service_ports:** ``{str: port}``:
* **total_out_connections**: ``int``
The maximum number of concurrent outgoing requests for data
* **total_in_connections**: ``int``
The maximum number of concurrent incoming requests for data
* **comm_threshold_bytes**: ``int``
As long as the total number of bytes in flight is below this threshold
we will not limit the number of outgoing connections for a single tasks
dependency fetch.
* **batched_stream**: ``BatchedSend``
A batched stream along which we communicate to the scheduler
* **log**: ``[(message)]``
A structured and queryable log. See ``Worker.story``
**Volatile State**
These attributes track the progress of tasks that this worker is trying to
complete. In the descriptions below a ``key`` is the name of a task that
we want to compute and ``dep`` is the name of a piece of dependent data
that we want to collect from others.
* **tasks**: ``{key: TaskState}``
The tasks currently executing on this worker (and any dependencies of those tasks)
* **data:** ``{key: object}``:
Prefer using the **host** attribute instead of this, unless
memory_limit and at least one of memory_target_fraction or
memory_spill_fraction values are defined, in that case, this attribute
is a zict.Buffer, from which information on LRU cache can be queried.
* **data.memory:** ``{key: object}``:
Dictionary mapping keys to actual values stored in memory. Only
available if condition for **data** being a zict.Buffer is met.
* **data.disk:** ``{key: object}``:
Dictionary mapping keys to actual values stored on disk. Only
available if condition for **data** being a zict.Buffer is met.
* **data_needed**: deque(keys)
The keys which still require data in order to execute, arranged in a deque
* **ready**: [keys]
Keys that are ready to run. Stored in a LIFO stack
* **constrained**: [keys]
Keys for which we have the data to run, but are waiting on abstract
resources like GPUs. Stored in a FIFO deque
* **executing_count**: ``int``
A count of tasks currently executing on this worker
* **executed_count**: int
A number of tasks that this worker has run in its lifetime
* **long_running**: {keys}
A set of keys of tasks that are running and have started their own
long-running clients.
* **has_what**: ``{worker: {deps}}``
The data that we care about that we think a worker has
* **pending_data_per_worker**: ``{worker: [dep]}``
The data on each worker that we still want, prioritized as a deque
* **in_flight_tasks**: ``int``
A count of the number of tasks that are coming to us in current
peer-to-peer connections
* **in_flight_workers**: ``{worker: {task}}``
The workers from which we are currently gathering data and the
dependencies we expect from those connections
* **comm_bytes**: ``int``
The total number of bytes in flight
* **threads**: ``{key: int}``
The ID of the thread on which the task ran
* **active_threads**: ``{int: key}``
The keys currently running on active threads
* **waiting_for_data_count**: ``int``
A count of how many tasks are currently waiting for data
* **generation**: ``int``
Counter that decreases every time the compute-task handler is invoked by the
Scheduler. It is appended to TaskState.priority and acts as a tie-breaker
between tasks that have the same priority on the Scheduler, determining a
last-in-first-out order between them.
Parameters
----------
scheduler_ip: str, optional
scheduler_port: int, optional
scheduler_file: str, optional
ip: str, optional
data: MutableMapping, type, None
The object to use for storage, builds a disk-backed LRU dict by default
nthreads: int, optional
loop: tornado.ioloop.IOLoop
local_directory: str, optional
Directory where we place local resources
name: str, optional
memory_limit: int, float, string
Number of bytes of memory that this worker should use.
Set to zero for no limit. Set to 'auto' to calculate
as system.MEMORY_LIMIT * min(1, nthreads / total_cores)
Use strings or numbers like 5GB or 5e9
memory_target_fraction: float or False
Fraction of memory to try to stay beneath
(default: read from config key 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)
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()
tasks: dict[str, TaskState]
waiting_for_data_count: int
has_what: defaultdict[str, set[str]] # {worker address: {ts.key, ...}
pending_data_per_worker: defaultdict[str, deque[str]]
nanny: Nanny | None
_lock: threading.Lock
data_needed: list[tuple[int, str]] # heap[(ts.priority, ts.key)]
in_flight_workers: dict[str, set[str]] # {worker address: {ts.key, ...}}
total_out_connections: int
total_in_connections: int
comm_threshold_bytes: int
comm_nbytes: int
_missing_dep_flight: set[TaskState]
threads: dict[str, int] # {ts.key: thread ID}
active_threads_lock: threading.Lock
active_threads: dict[int, str] # {thread ID: ts.key}
active_keys: set[str]
profile_keys: defaultdict[str, dict[str, Any]]
profile_keys_history: deque[tuple[float, dict[str, dict[str, Any]]]]
profile_recent: dict[str, Any]
profile_history: deque[tuple[float, dict[str, Any]]]
generation: int
ready: list[str]
constrained: deque[str]
_executing: set[TaskState]
_in_flight_tasks: set[TaskState]
executed_count: int
long_running: set[TaskState]
log: deque[tuple]
incoming_transfer_log: deque[dict[str, Any]]
outgoing_transfer_log: deque[dict[str, Any]]
target_message_size: int
validate: bool
_transitions_table: dict[tuple[str, str], Callable]
_transition_counter: int
incoming_count: int
outgoing_count: int
outgoing_current_count: int
repetitively_busy: int
bandwidth: float
latency: float
profile_cycle_interval: float
workspace: WorkSpace
_workdir: WorkDir
local_directory: str
_client: Client | None
bandwidth_workers: defaultdict[str, tuple[float, int]]
bandwidth_types: defaultdict[type, tuple[float, int]]
preloads: list[preloading.Preload]
contact_address: str | None
_start_port: int | None
_start_host: str | None
_interface: str | None
_protocol: str
_dashboard_address: str | None
_dashboard: bool
_http_prefix: str
nthreads: int
total_resources: dict[str, float]
available_resources: dict[str, float]
death_timeout: float | None
lifetime: float | None
lifetime_stagger: float | None
lifetime_restart: bool
extensions: dict
security: Security
connection_args: dict[str, Any]
memory_limit: int | None
memory_target_fraction: float | Literal[False]
memory_spill_fraction: float | Literal[False]
memory_pause_fraction: float | Literal[False]
data: MutableMapping[str, Any] # {task key: task payload}
actors: dict[str, Actor | None]
loop: IOLoop
reconnect: bool
executors: dict[str, Executor]
batched_stream: BatchedSend
name: Any
scheduler_delay: float
stream_comms: dict[str, BatchedSend]
heartbeat_active: bool
_ipython_kernel: Any | None = None
services: dict[str, Any] = {}
service_specs: dict[str, Any]
metrics: dict[str, Callable[[Worker], Any]]
startup_information: dict[str, Callable[[Worker], Any]]
low_level_profiler: bool
scheduler: Any
execution_state: dict[str, Any]
memory_monitor_interval: float | None
_memory_monitoring: bool
_throttled_gc: ThrottledGC
plugins: dict[str, WorkerPlugin]
_pending_plugins: tuple[WorkerPlugin, ...]
def __init__(
self,
scheduler_ip: str | None = None,
scheduler_port: int | None = None,
*,
scheduler_file: str | None = None,
ncores: None = None, # Deprecated, use nthreads instead
nthreads: int | None = None,
loop: IOLoop | None = None,
local_dir: None = None, # Deprecated, use local_directory instead
local_directory: str | None = None,
services: dict | None = None,
name: Any | None = None,
reconnect: bool = True,
memory_limit: str | float = "auto",
executor: Executor | dict[str, Executor] | Literal["offload"] | None = None,
resources: dict[str, float] | None = None,
silence_logs: int | None = None,
death_timeout: Any | None = None,
preload: list[str] | None = None,
preload_argv: list[str] | list[list[str]] | None = None,
security: Security | dict[str, Any] | None = None,
contact_address: str | None = None,
memory_monitor_interval: Any = "200ms",
memory_target_fraction: float | Literal[False] | None = None,
memory_spill_fraction: float | Literal[False] | None = None,
memory_pause_fraction: float | Literal[False] | None = None,
extensions: list[type] | None = None,
metrics: Mapping[str, Callable[[Worker], Any]] = DEFAULT_METRICS,
startup_information: Mapping[
str, Callable[[Worker], Any]
] = DEFAULT_STARTUP_INFORMATION,
data: (
MutableMapping[str, Any] # pre-initialised
| Callable[[], MutableMapping[str, Any]] # constructor
| tuple[
Callable[..., MutableMapping[str, Any]], dict[str, Any]
] # (constructor, kwargs to constructor)
| None # create internatlly
) = None,
interface: str | None = None,
host: str | None = None,
port: int | None = None,
protocol: str | None = None,
dashboard_address: str | None = None,
dashboard: bool = False,
http_prefix: str = "/",
nanny: Nanny | None = None,
plugins: tuple[WorkerPlugin, ...] = (),
low_level_profiler: bool | None = None,
validate: bool | None = None,
profile_cycle_interval=None,
lifetime: Any | None = None,
lifetime_stagger: Any | None = None,
lifetime_restart: bool | None = None,
**kwargs,
):
self.tasks = {}
self.waiting_for_data_count = 0
self.has_what = defaultdict(set)
self.pending_data_per_worker = defaultdict(deque)
self.nanny = nanny
self._lock = threading.Lock()
self.data_needed = []
self.in_flight_workers = {}
self.total_out_connections = dask.config.get(
"distributed.worker.connections.outgoing"
)
self.total_in_connections = dask.config.get(
"distributed.worker.connections.incoming"
)
self.comm_threshold_bytes = int(10e6)
self.comm_nbytes = 0
self._missing_dep_flight = set()
self.threads = {}
self.active_threads_lock = threading.Lock()
self.active_threads = {}
self.active_keys = set()
self.profile_keys = defaultdict(profile.create)
self.profile_keys_history = deque(maxlen=3600)
self.profile_recent = profile.create()
self.profile_history = deque(maxlen=3600)
self.generation = 0
self.ready = []
self.constrained = deque()
self._executing = set()
self._in_flight_tasks = set()
self.executed_count = 0
self.long_running = set()
self.target_message_size = int(50e6) # 50 MB
self.log = deque(maxlen=100000)
if validate is None:
validate = dask.config.get("distributed.scheduler.validate")
self.validate = validate
self._transitions_table = {
("cancelled", "resumed"): self.transition_cancelled_resumed,
("cancelled", "fetch"): self.transition_cancelled_fetch,
("cancelled", "released"): self.transition_cancelled_released,
("cancelled", "waiting"): self.transition_cancelled_waiting,
("cancelled", "forgotten"): self.transition_cancelled_forgotten,
("cancelled", "memory"): self.transition_cancelled_memory,
("cancelled", "error"): self.transition_cancelled_error,
("resumed", "memory"): self.transition_generic_memory,
("resumed", "error"): self.transition_generic_error,
("resumed", "released"): self.transition_generic_released,
("resumed", "waiting"): self.transition_resumed_waiting,
("resumed", "fetch"): self.transition_resumed_fetch,
("constrained", "executing"): self.transition_constrained_executing,
("constrained", "released"): self.transition_generic_released,
("error", "released"): self.transition_generic_released,
("executing", "error"): self.transition_executing_error,
("executing", "long-running"): self.transition_executing_long_running,
("executing", "memory"): self.transition_executing_memory,
("executing", "released"): self.transition_executing_released,
("executing", "rescheduled"): self.transition_executing_rescheduled,
("fetch", "flight"): self.transition_fetch_flight,
("fetch", "missing"): self.transition_fetch_missing,
("fetch", "released"): self.transition_generic_released,
("flight", "error"): self.transition_flight_error,
("flight", "fetch"): self.transition_flight_fetch,
("flight", "memory"): self.transition_flight_memory,
("flight", "released"): self.transition_flight_released,
("long-running", "error"): self.transition_generic_error,
("long-running", "memory"): self.transition_long_running_memory,
("long-running", "rescheduled"): self.transition_executing_rescheduled,
("long-running", "released"): self.transition_executing_released,
("memory", "released"): self.transition_memory_released,
("missing", "fetch"): self.transition_missing_fetch,
("missing", "released"): self.transition_missing_released,
("missing", "error"): self.transition_generic_error,
("ready", "error"): self.transition_generic_error,
("ready", "executing"): self.transition_ready_executing,
("ready", "released"): self.transition_generic_released,
("released", "error"): self.transition_generic_error,
("released", "fetch"): self.transition_released_fetch,
("released", "forgotten"): self.transition_released_forgotten,
("released", "memory"): self.transition_released_memory,
("released", "waiting"): self.transition_released_waiting,
("waiting", "constrained"): self.transition_waiting_constrained,
("waiting", "ready"): self.transition_waiting_ready,
("waiting", "released"): self.transition_generic_released,
}
self._transition_counter = 0
self.incoming_transfer_log = deque(maxlen=100000)
self.incoming_count = 0
self.outgoing_transfer_log = deque(maxlen=100000)
self.outgoing_count = 0
self.outgoing_current_count = 0
self.repetitively_busy = 0
self.bandwidth = parse_bytes(dask.config.get("distributed.scheduler.bandwidth"))
self.bandwidth_workers = defaultdict(
lambda: (0, 0)
) # bw/count recent transfers
self.bandwidth_types = defaultdict(lambda: (0, 0)) # bw/count recent transfers
self.latency = 0.001
self._client = None
if profile_cycle_interval is None:
profile_cycle_interval = dask.config.get("distributed.worker.profile.cycle")
profile_cycle_interval = parse_timedelta(profile_cycle_interval, default="ms")
assert profile_cycle_interval
self._setup_logging(logger)
if local_dir is not None:
warnings.warn("The local_dir keyword has moved to local_directory")
local_directory = local_dir
if not local_directory:
local_directory = dask.config.get("temporary-directory") or os.getcwd()
os.makedirs(local_directory, exist_ok=True)
local_directory = os.path.join(local_directory, "dask-worker-space")
with warn_on_duration(
"1s",
"Creating scratch directories is taking a surprisingly long time. "
"This is often due to running workers on a network file system. "
"Consider specifying a local-directory to point workers to write "
"scratch data to a local disk.",
):
self._workspace = WorkSpace(os.path.abspath(local_directory))
self._workdir = self._workspace.new_work_dir(prefix="worker-")
self.local_directory = self._workdir.dir_path
if not preload:
preload = dask.config.get("distributed.worker.preload")
if not preload_argv:
preload_argv = dask.config.get("distributed.worker.preload-argv")
assert preload is not None
assert preload_argv is not None
self.preloads = preloading.process_preloads(
self, preload, preload_argv, file_dir=self.local_directory
)
if scheduler_file:
cfg = json_load_robust(scheduler_file)
scheduler_addr = cfg["address"]
elif scheduler_ip is None and dask.config.get("scheduler-address", None):
scheduler_addr = dask.config.get("scheduler-address")
elif scheduler_port is None:
scheduler_addr = coerce_to_address(scheduler_ip)
else:
scheduler_addr = coerce_to_address((scheduler_ip, scheduler_port))
self.contact_address = contact_address
if protocol is None:
protocol_address = scheduler_addr.split("://")
if len(protocol_address) == 2:
protocol = protocol_address[0]
assert protocol
self._start_port = port
self._start_host = host
if host:
# Helpful error message if IPv6 specified incorrectly
_, host_address = parse_address(host)
if host_address.count(":") > 1 and not host_address.startswith("["):
raise ValueError(
"Host address with IPv6 must be bracketed like '[::1]'; "
f"got {host_address}"
)
self._interface = interface
self._protocol = protocol
if ncores is not None:
warnings.warn("the ncores= parameter has moved to nthreads=")
nthreads = ncores
self.nthreads = nthreads or CPU_COUNT
if resources is None:
resources = dask.config.get("distributed.worker.resources", None)
assert isinstance(resources, dict)
self.total_resources = resources or {}
self.available_resources = (resources or {}).copy()
self.death_timeout = parse_timedelta(death_timeout)
self.extensions = {}
if silence_logs:
silence_logging(level=silence_logs)
if isinstance(security, dict):
security = Security(**security)
self.security = security or Security()
assert isinstance(self.security, Security)
self.connection_args = self.security.get_connection_args("worker")
self.memory_limit = parse_memory_limit(memory_limit, self.nthreads)
self.memory_target_fraction = (
memory_target_fraction
if memory_target_fraction is not None
else dask.config.get("distributed.worker.memory.target")
)
self.memory_spill_fraction = (
memory_spill_fraction
if memory_spill_fraction is not None
else dask.config.get("distributed.worker.memory.spill")
)
self.memory_pause_fraction = (
memory_pause_fraction
if memory_pause_fraction is not None
else dask.config.get("distributed.worker.memory.pause")
)
if isinstance(data, MutableMapping):
self.data = data
elif callable(data):
self.data = data()
elif isinstance(data, tuple):
self.data = data[0](**data[1])
elif self.memory_limit and (
self.memory_target_fraction or self.memory_spill_fraction
):
from .spill import SpillBuffer
self.data = SpillBuffer(
os.path.join(self.local_directory, "storage"),
target=int(
self.memory_limit
* (self.memory_target_fraction or self.memory_spill_fraction)
)
or sys.maxsize,
)
else:
self.data = {}
self.actors = {}
self.loop = loop or IOLoop.current()
self.reconnect = reconnect
# Common executors always available
self.executors = {
"offload": utils._offload_executor,
"actor": ThreadPoolExecutor(1, thread_name_prefix="Dask-Actor-Threads"),
}
if nvml.device_get_count() > 0:
self.executors["gpu"] = ThreadPoolExecutor(
1, thread_name_prefix="Dask-GPU-Threads"
)
# Find the default executor
if executor == "offload":
self.executors["default"] = self.executors["offload"]
elif isinstance(executor, dict):
self.executors.update(executor)
elif executor is not None:
self.executors["default"] = executor
if "default" not in self.executors:
self.executors["default"] = ThreadPoolExecutor(
self.nthreads, thread_name_prefix="Dask-Default-Threads"
)
self.batched_stream = BatchedSend(interval="2ms", loop=self.loop)
self.name = name
self.scheduler_delay = 0
self.stream_comms = {}
self.heartbeat_active = False
self._ipython_kernel = None
if self.local_directory not in sys.path:
sys.path.insert(0, self.local_directory)
self.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_free_keys,
"terminate": self.close,
"ping": pingpong,
"upload_file": self.upload_file,
"start_ipython": self.start_ipython,
"call_stack": self.get_call_stack,
"profile": self.get_profile,
"profile_metadata": self.get_profile_metadata,
"get_logs": self.get_logs,
"keys": self.keys,
"versions": self.versions,
"actor_execute": self.actor_execute,
"actor_attribute": self.actor_attribute,
"plugin-add": self.plugin_add,
"plugin-remove": self.plugin_remove,
"get_monitor_info": self.get_monitor_info,
}
stream_handlers = {
"close": self.close,
"cancel-compute": self.handle_cancel_compute,
"acquire-replicas": self.handle_acquire_replicas,
"compute-task": self.handle_compute_task,
"free-keys": self.handle_free_keys,
"remove-replicas": self.handle_remove_replicas,
"steal-request": self.handle_steal_request,
}
super().__init__(
handlers=handlers,
stream_handlers=stream_handlers,
io_loop=self.loop,
connection_args=self.connection_args,
**kwargs,
)
self.scheduler = self.rpc(scheduler_addr)
self.execution_state = {
"scheduler": self.scheduler.address,
"ioloop": self.loop,
"worker": self,
}
pc = PeriodicCallback(self.heartbeat, 1000)
self.periodic_callbacks["heartbeat"] = pc
pc = PeriodicCallback(
lambda: self.batched_stream.send({"op": "keep-alive"}), 60000
)
self.periodic_callbacks["keep-alive"] = pc
pc = PeriodicCallback(self.find_missing, 1000)
self.periodic_callbacks["find-missing"] = pc
self._address = contact_address
self.memory_monitor_interval = parse_timedelta(
memory_monitor_interval, default="ms"
)
self._memory_monitoring = False
if self.memory_limit:
assert self.memory_monitor_interval is not None
pc = PeriodicCallback(
self.memory_monitor, self.memory_monitor_interval * 1000
)
self.periodic_callbacks["memory"] = pc
if extensions is None:
extensions = DEFAULT_EXTENSIONS
for ext in extensions:
ext(self)
self._throttled_gc = ThrottledGC(logger=logger)
setproctitle("dask-worker [not started]")
profile_trigger_interval = parse_timedelta(
dask.config.get("distributed.worker.profile.interval"), default="ms"
)
pc = PeriodicCallback(self.trigger_profile, profile_trigger_interval * 1000)
self.periodic_callbacks["profile"] = pc
pc = PeriodicCallback(self.cycle_profile, profile_cycle_interval * 1000)
self.periodic_callbacks["profile-cycle"] = pc
self.plugins = {}
self._pending_plugins = plugins
if lifetime is None:
lifetime = dask.config.get("distributed.worker.lifetime.duration")
self.lifetime = parse_timedelta(lifetime)
if lifetime_stagger is None:
lifetime_stagger = dask.config.get("distributed.worker.lifetime.stagger")
lifetime_stagger = parse_timedelta(lifetime_stagger)
if lifetime_restart is None:
lifetime_restart = dask.config.get("distributed.worker.lifetime.restart")
self.lifetime_restart = lifetime_restart
if self.lifetime:
self.lifetime += (random.random() * 2 - 1) * lifetime_stagger
self.io_loop.call_later(self.lifetime, self.close_gracefully)
Worker._instances.add(self)
##################
# Administrative #
##################
def __repr__(self):
return "<%s: %r, %s, %s, stored: %d, running: %d/%d, ready: %d, comm: %d, waiting: %d>" % (
self.__class__.__name__,
self.address,
self.name,
self.status,
len(self.data),
self.executing_count,
self.nthreads,
len(self.ready),
self.in_flight_tasks,
self.waiting_for_data_count,
)
@property
def logs(self):
return self._deque_handler.deque
def log_event(self, topic, msg):
self.batched_stream.send(
{
"op": "log-event",
"topic": topic,
"msg": msg,
}
)
@property
def executing_count(self) -> int:
return len(self._executing)
@property
def in_flight_tasks(self) -> int:
return len(self._in_flight_tasks)
@property
def worker_address(self):
"""For API compatibility with Nanny"""
return self.address
@property
def local_dir(self):
"""For API compatibility with Nanny"""
warnings.warn(
"The local_dir attribute has moved to local_directory", stacklevel=2
)
return self.local_directory
@property
def executor(self):
return self.executors["default"]
@ServerNode.status.setter # type: ignore
def status(self, value):
"""Override Server.status to notify the Scheduler of status changes"""
ServerNode.status.__set__(self, value)
self._send_worker_status_change()
def _send_worker_status_change(self) -> None:
if (
self.batched_stream
and self.batched_stream.comm
and not self.batched_stream.comm.closed()
):
self.batched_stream.send(
{"op": "worker-status-change", "status": self._status.name}
)
elif self._status != Status.closed:
self.loop.call_later(0.05, self._send_worker_status_change)
async def get_metrics(self):
out = dict(
executing=self.executing_count,
in_memory=len(self.data),
ready=len(self.ready),
in_flight=self.in_flight_tasks,
bandwidth={
"total": self.bandwidth,
"workers": dict(self.bandwidth_workers),
"types": keymap(typename, self.bandwidth_types),
},
spilled_nbytes=getattr(self.data, "spilled_total", 0),
)
out.update(self.monitor.recent())
for k, metric in self.metrics.items():
try:
result = metric(self)
if isawaitable(result):
result = await result
# In case of collision, prefer core metrics
out.setdefault(k, result)
except Exception: # TODO: log error once
pass
return out
async def get_startup_information(self):
result = {}
for k, f in self.startup_information.items():
try:
v = f(self)
if isawaitable(v):
v = await v
result[k] = v
except Exception: # TODO: log error once
pass
return result
def identity(self, comm=None):
return {
"type": type(self).__name__,
"id": self.id,
"scheduler": self.scheduler.address,
"nthreads": self.nthreads,
"ncores": self.nthreads, # backwards compatibility
"memory_limit": self.memory_limit,
}
def _to_dict(
self, comm: Comm | None = None, *, exclude: Container[str] = ()
) -> dict:
"""
A very verbose dictionary representation for debugging purposes.
Not type stable and not inteded for roundtrips.
Parameters
----------
comm:
exclude:
A list of attributes which must not be present in the output.
See also
--------
Worker.identity
Client.dump_cluster_state
"""
info = super()._to_dict(exclude=exclude)
extra = {
"status": self.status,
"ready": self.ready,
"constrained": self.constrained,
"long_running": self.long_running,
"executing_count": self.executing_count,
"in_flight_tasks": self.in_flight_tasks,
"in_flight_workers": self.in_flight_workers,
"log": self.log,
"tasks": self.tasks,
"memory_limit": self.memory_limit,
"memory_target_fraction": self.memory_target_fraction,
"memory_spill_fraction": self.memory_spill_fraction,
"memory_pause_fraction": self.memory_pause_fraction,
"logs": self.get_logs(),
"config": dask.config.config,
"incoming_transfer_log": self.incoming_transfer_log,
"outgoing_transfer_log": self.outgoing_transfer_log,
}
info.update(extra)
return recursive_to_dict(info, exclude=exclude)
#####################
# External Services #
#####################
async def _register_with_scheduler(self):
self.periodic_callbacks["keep-alive"].stop()
self.periodic_callbacks["heartbeat"].stop()
start = time()
if self.contact_address is None:
self.contact_address = self.address
logger.info("-" * 49)
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,
keys=list(self.data),
nthreads=self.nthreads,
name=self.name,
nbytes={
ts.key: ts.get_nbytes()
for ts in self.tasks.values()
# Only if the task is in memory this is a sensible
# result since otherwise it simply submits the
# default value
if ts.state == "memory"
},
types={k: typename(v) for k, v in self.data.items()},
now=time(),
resources=self.total_resources,
memory_limit=self.memory_limit,
local_directory=self.local_directory,
services=self.service_ports,
nanny=self.nanny,
pid=os.getpid(),
versions=get_versions(),
metrics=await self.get_metrics(),
extra=await self.get_startup_information(),
),
serializers=["msgpack"],
)
future = 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
self.status = Status.running
break
except OSError:
logger.info("Waiting to connect to: %26s", self.scheduler.address)
await asyncio.sleep(0.1)
except TimeoutError:
logger.info("Timed out when connecting to scheduler")
if response["status"] != "OK":
raise ValueError(f"Unexpected response from register: {response!r}")
else:
await asyncio.gather(
*(
self.plugin_add(name=name, plugin=plugin)
for name, plugin in response["worker-plugins"].items()
)
)
logger.info(" Registered to: %26s", self.scheduler.address)
logger.info("-" * 49)
self.batched_stream.start(comm)
self.periodic_callbacks["keep-alive"].start()
self.periodic_callbacks["heartbeat"].start()
self.loop.add_callback(self.handle_scheduler, comm)
def _update_latency(self, latency):
self.latency = latency * 0.05 + self.latency * 0.95
if self.digests is not None:
self.digests["latency"].add(latency)
async def heartbeat(self):
if self.heartbeat_active:
logger.debug("Heartbeat skipped: channel busy")
return
self.heartbeat_active = True
logger.debug("Heartbeat: %s", self.address)
try:
start = time()
response = await retry_operation(
self.scheduler.heartbeat_worker,
address=self.contact_address,
now=start,
metrics=await self.get_metrics(),
executing={
key: start - self.tasks[key].start_time
for key in self.active_keys
if key in self.tasks
},
)
end = time()
middle = (start + end) / 2
self._update_latency(end - start)
if response["status"] == "missing":
# If running, wait up to 0.5s and then re-register self.
# Otherwise just exit.
start = time()
while self.status in RUNNING and time() < start + 0.5:
await asyncio.sleep(0.01)
if self.status in RUNNING:
await self._register_with_scheduler()
return
self.scheduler_delay = response["time"] - middle
self.periodic_callbacks["heartbeat"].callback_time = (
response["heartbeat-interval"] * 1000
)
self.bandwidth_workers.clear()
self.bandwidth_types.clear()
except CommClosedError:
logger.warning("Heartbeat to scheduler failed", exc_info=True)
if not self.reconnect:
await self.close(report=False)
except OSError as e:
# Scheduler is gone. Respect distributed.comm.timeouts.connect
if "Timed out trying to connect" in str(e):
await self.close(report=False)
else:
raise e
finally:
self.heartbeat_active = False
async def handle_scheduler(self, comm):
try:
await self.handle_stream(
comm, every_cycle=[self.ensure_communicating, self.ensure_computing]
)
except Exception as e:
logger.exception(e)
raise
finally:
if self.reconnect and self.status in RUNNING:
logger.info("Connection to scheduler broken. Reconnecting...")
self.loop.add_callback(self.heartbeat)
else:
await self.close(report=False)
[docs] def start_ipython(self, comm):
"""Start an IPython kernel
Returns Jupyter connection info dictionary.
"""
from ._ipython_utils import start_ipython
if self._ipython_kernel is None:
self._ipython_kernel = start_ipython(
ip=self.ip, ns={"worker": self}, log=logger
)
return self._ipython_kernel.get_connection_info()
async def upload_file(self, comm, filename=None, data=None, load=True):
out_filename = os.path.join(self.local_directory, filename)
def func(data):
if isinstance(data, str):
data = data.encode()
with open(out_filename, "wb") as f:
f.write(data)
f.flush()
return data
if len(data) < 10000:
data = func(data)
else:
data = await offload(func, data)
if load:
try:
import_file(out_filename)
cache_loads.data.clear()
except Exception as e:
logger.exception(e)
raise e
return {"status": "OK", "nbytes": len(data)}
def keys(self, comm=None):
return list(self.data)
async def gather(self, comm=None, who_has=None):
who_has = {
k: [coerce_to_address(addr) for addr in v]
for k, v in who_has.items()
if k not in self.data
}
result, missing_keys, missing_workers = await gather_from_workers(
who_has, rpc=self.rpc, who=self.address
)
self.update_data(data=result, report=False)
if missing_keys:
logger.warning(
"Could not find data: %s on workers: %s (who_has: %s)",
missing_keys,
missing_workers,
who_has,
)
return {"status": "partial-fail", "keys": missing_keys}
else:
return {"status": "OK"}
def get_monitor_info(self, comm=None, recent=False, start=0):
result = dict(
range_query=(
self.monitor.recent()
if recent
else self.monitor.range_query(start=start)
),
count=self.monitor.count,
last_time=self.monitor.last_time,
)
if nvml.device_get_count() > 0:
result["gpu_name"] = self.monitor.gpu_name
result["gpu_memory_total"] = self.monitor.gpu_memory_total
return result
#############
# Lifecycle #
#############
async def start(self):
if self.status and self.status in (
Status.closed,
Status.closing,
Status.closing_gracefully,
):
return
assert self.status is Status.undefined, self.status
await super().start()
enable_gc_diagnosis()
ports = parse_ports(self._start_port)
for port in ports:
start_address = address_from_user_args(
host=self._start_host,
port=port,
interface=self._interface,
protocol=self._protocol,
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
for preload in self.preloads:
await preload.start()
# Services listen on all addresses
# Note Nanny is not a "real" service, just some metadata
# passed in service_ports...
self.start_services(self.ip)
try:
listening_address = "%s%s:%d" % (self.listener.prefix, self.ip, self.port)
except Exception:
listening_address = f"{self.listener.prefix}{self.ip}"
logger.info(" Start worker at: %26s", self.address)
logger.info(" Listening to: %26s", listening_address)
for k, v in self.service_ports.items():
logger.info(" {:>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.nthreads)
if self.memory_limit:
logger.info(" Memory: %26s", format_bytes(self.memory_limit))
logger.info(" Local Directory: %26s", self.local_directory)
setproctitle("dask-worker [%s]" % self.address)
plugins_msgs = await asyncio.gather(
*(
self.plugin_add(plugin=plugin, catch_errors=False)
for plugin in self._pending_plugins
),
return_exceptions=True,
)
plugins_exceptions = [msg for msg in plugins_msgs if isinstance(msg, Exception)]
if len(plugins_exceptions) >= 1:
if len(plugins_exceptions) > 1:
logger.error(
"Multiple plugin exceptions raised. All exceptions will be logged, the first is raised."
)
for exc in plugins_exceptions:
logger.error(repr(exc))
raise plugins_exceptions[0]
self._pending_plugins = ()
await self._register_with_scheduler()
self.start_periodic_callbacks()
return self
def _close(self, *args, **kwargs):
warnings.warn("Worker._close has moved to Worker.close", stacklevel=2)
return self.close(*args, **kwargs)
async def close(
self, report=True, timeout=30, nanny=True, executor_wait=True, safe=False
):
with log_errors():
if self.status in (Status.closed, Status.closing):
await self.finished()
return
self.reconnect = False
disable_gc_diagnosis()
try:
logger.info("Stopping worker at %s", self.address)
except ValueError: # address not available if already closed
logger.info("Stopping worker")
if self.status not in RUNNING:
logger.info("Closed worker has not yet started: %s", self.status)
self.status = Status.closing
for preload in self.preloads:
await preload.teardown()
if nanny and self.nanny:
with self.rpc(self.nanny) as r:
await r.close_gracefully()
setproctitle("dask-worker [closing]")
teardowns = [
plugin.teardown(self)
for plugin in self.plugins.values()
if hasattr(plugin, "teardown")
]
await asyncio.gather(*(td for td in teardowns if isawaitable(td)))
for pc in self.periodic_callbacks.values():
pc.stop()
if self._client:
# If this worker is the last one alive, clean up the worker
# initialized clients
if not any(
w for w in Worker._instances if w != self and w.status in RUNNING
):
for c in Worker._initialized_clients:
# Regardless of what the client was initialized with
# we'll require the result as a future. This is
# necessary since the heursitics of asynchronous are not
# reliable and we might deadlock here
c._asynchronous = True
if c.asynchronous:
await c.close()
else:
# There is still the chance that even with us
# telling the client to be async, itself will decide
# otherwise
c.close()
with suppress(EnvironmentError, TimeoutError):
if report and self.contact_address is not None:
await asyncio.wait_for(
self.scheduler.unregister(
address=self.contact_address, safe=safe
),
timeout,
)
await self.scheduler.close_rpc()
self._workdir.release()
self.stop_services()
# Give some time for a UCX scheduler to complete closing endpoints
# before closing self.batched_stream, otherwise the local endpoint
# may be closed too early and errors be raised on the scheduler when
# trying to send closing message.
if self._protocol == "ucx":
await asyncio.sleep(0.2)
if (
self.batched_stream
and self.batched_stream.comm
and not self.batched_stream.comm.closed()
):
self.batched_stream.send({"op": "close-stream"})
if self.batched_stream:
with suppress(TimeoutError):
await self.batched_stream.close(timedelta(seconds=timeout))
for executor in self.executors.values():
if executor is utils._offload_executor:
continue # Never shutdown the offload executor
if isinstance(executor, ThreadPoolExecutor):
executor._work_queue.queue.clear()
executor.shutdown(wait=executor_wait, timeout=timeout)
else:
executor.shutdown(wait=executor_wait)
self.stop()
await self.rpc.close()
self.status = Status.closed
await super().close()
setproctitle("dask-worker [closed]")
return "OK"
[docs] async def close_gracefully(self, restart=None):
"""Gracefully shut down a worker
This first informs the scheduler that we're shutting down, and asks it
to move our data elsewhere. Afterwards, we close as normal
"""
if self.status in (Status.closing, Status.closing_gracefully):
await self.finished()
if self.status == Status.closed:
return
if restart is None:
restart = self.lifetime_restart
logger.info("Closing worker gracefully: %s", self.address)
self.status = Status.closing_gracefully
await self.scheduler.retire_workers(workers=[self.address], remove=False)
await self.close(safe=True, nanny=not restart)
async def terminate(self, comm=None, report=True, **kwargs):
await self.close(report=report, **kwargs)
return "OK"
async def wait_until_closed(self):
warnings.warn("wait_until_closed has moved to finished()")
await self.finished()
assert self.status == Status.closed
################
# Worker Peers #
################
def send_to_worker(self, address, msg):
if address not in self.stream_comms:
bcomm = BatchedSend(interval="1ms", loop=self.loop)
self.stream_comms[address] = bcomm
async def batched_send_connect():
comm = await connect(
address, **self.connection_args # TODO, serialization
)
comm.name = "Worker->Worker"
await comm.write({"op": "connection_stream"})
bcomm.start(comm)
self.loop.add_callback(batched_send_connect)
self.stream_comms[address].send(msg)
async def get_data(
self, comm, keys=None, who=None, serializers=None, max_connections=None
):
start = time()
if max_connections is None:
max_connections = self.total_in_connections
# Allow same-host connections more liberally
if (
max_connections
and comm
and get_address_host(comm.peer_address) == get_address_host(self.address)
):
max_connections = max_connections * 2
if self.status == Status.paused:
max_connections = 1
throttle_msg = " Throttling outgoing connections because worker is paused."
else:
throttle_msg = ""
if (
max_connections is not False
and self.outgoing_current_count >= max_connections
):
logger.debug(
"Worker %s has too many open connections to respond to data request "
"from %s (%d/%d).%s",
self.address,
who,
self.outgoing_current_count,
max_connections,
throttle_msg,
)
return {"status": "busy"}
self.outgoing_current_count += 1
data = {k: self.data[k] for k in keys if k in self.data}
if len(data) < len(keys):
for k in set(keys) - set(data):
if k in self.actors:
from .actor import Actor
data[k] = Actor(type(self.actors[k]), self.address, k, worker=self)
msg = {"status": "OK", "data": {k: to_serialize(v) for k, v in data.items()}}
nbytes = {k: self.tasks[k].nbytes for k in data if k in self.tasks}
stop = time()
if self.digests is not None:
self.digests["get-data-load-duration"].add(stop - start)
start = time()
try:
compressed = await comm.write(msg, serializers=serializers)
response = await comm.read(deserializers=serializers)
assert response == "OK", response
except OSError:
logger.exception(
"failed during get data with %s -> %s", self.address, who, exc_info=True
)
comm.abort()
raise
finally:
self.outgoing_current_count -= 1
stop = time()
if self.digests is not None:
self.digests["get-data-send-duration"].add(stop - start)
total_bytes = sum(filter(None, nbytes.values()))
self.outgoing_count += 1
duration = (stop - start) or 0.5 # windows
self.outgoing_transfer_log.append(
{
"start": start + self.scheduler_delay,
"stop": stop + self.scheduler_delay,
"middle": (start + stop) / 2,
"duration": duration,
"who": who,
"keys": nbytes,
"total": total_bytes,
"compressed": compressed,
"bandwidth": total_bytes / duration,
}
)
return Status.dont_reply
###################
# Local Execution #
###################
def update_data(
self, comm=None, data=None, report=True, serializers=None, stimulus_id=None
):
if stimulus_id is None:
stimulus_id = f"update-data-{time()}"
recommendations = {}
scheduler_messages = []
for key, value in data.items():
try:
ts = self.tasks[key]
recommendations[ts] = ("memory", value)
except KeyError:
self.tasks[key] = ts = TaskState(key)
try:
recs = self._put_key_in_memory(ts, value, stimulus_id=stimulus_id)
except Exception as e:
msg = error_message(e)
recommendations = {ts: tuple(msg.values())}
else:
recommendations.update(recs)
self.log.append((key, "receive-from-scatter", stimulus_id, time()))
if report:
scheduler_messages.append(
{"op": "add-keys", "keys": list(data), "stimulus_id": stimulus_id}
)
self.transitions(recommendations, stimulus_id=stimulus_id)
for msg in scheduler_messages:
self.batched_stream.send(msg)
return {"nbytes": {k: sizeof(v) for k, v in data.items()}, "status": "OK"}
[docs] def handle_free_keys(self, comm=None, keys=None, stimulus_id=None):
"""
Handler to be called by the scheduler.
The given keys are no longer referred to and required by the scheduler.
The worker is now allowed to release the key, if applicable.
This does not guarantee that the memory is released since the worker may
still decide to hold on to the data and task since it is required by an
upstream dependency.
"""
self.log.append(("free-keys", keys, stimulus_id, time()))
recommendations = {}
for key in keys:
ts = self.tasks.get(key)
if ts:
recommendations[ts] = "released"
self.transitions(recommendations, stimulus_id=stimulus_id)
[docs] def handle_remove_replicas(self, keys, stimulus_id):
"""Stream handler notifying the worker that it might be holding unreferenced,
superfluous data.
This should not actually happen during ordinary operations and is only intended
to correct any erroneous state. An example where this is necessary is if a
worker fetches data for a downstream task but that task is released before the
data arrives. In this case, the scheduler will notify the worker that it may be
holding this unnecessary data, if the worker hasn't released the data itself,
already.
This handler does not guarantee the task nor the data to be actually
released but only asks the worker to release the data on a best effort
guarantee. This protects from race conditions where the given keys may
already have been rescheduled for compute in which case the compute
would win and this handler is ignored.
For stronger guarantees, see handler free_keys
"""
self.log.append(("remove-replicas", keys, stimulus_id, time()))
recommendations = {}
rejected = []
for key in keys:
ts = self.tasks.get(key)
if ts is None or ts.state != "memory":
continue
if not ts.is_protected():
self.log.append(
(ts.key, "remove-replica-confirmed", stimulus_id, time())
)
recommendations[ts] = "released"
else:
rejected.append(key)
if rejected:
self.log.append(("remove-replica-rejected", rejected, stimulus_id, time()))
self.batched_stream.send(
{"op": "add-keys", "keys": rejected, "stimulus_id": stimulus_id}
)
self.transitions(recommendations, stimulus_id=stimulus_id)
return "OK"
async def set_resources(self, **resources):
for r, quantity in resources.items():
if r in self.total_resources:
self.available_resources[r] += quantity - self.total_resources[r]
else:
self.available_resources[r] = quantity
self.total_resources[r] = quantity
await retry_operation(
self.scheduler.set_resources,
resources=self.total_resources,
worker=self.contact_address,
)
###################
# Task Management #
###################
[docs] def handle_cancel_compute(self, key, reason):
"""
Cancel a task on a best effort basis. This is only possible while a task
is in state `waiting` or `ready`.
Nothing will happen otherwise.
"""
ts = self.tasks.get(key)
if ts and ts.state in READY | {"waiting"}:
self.log.append((key, "cancel-compute", reason, time()))
ts.scheduler_holds_ref = False
# All possible dependents of TS should not be in state Processing on
# scheduler side and therefore should not be assigned to a worker,
# yet.
assert not ts.dependents
self.transition(ts, "released", stimulus_id=reason)
def handle_acquire_replicas(
self,
comm=None,
*,
keys: Collection[str],
who_has: dict[str, Collection[str]],
stimulus_id: str,
):
recommendations = {}
for key in keys:
ts = self.ensure_task_exists(
key=key,
# Transfer this data after all dependency tasks of computations with
# default or explicitly high (>0) user priority and before all
# computations with low priority (<0). Note that the priority= parameter
# of compute() is multiplied by -1 before it reaches TaskState.priority.
priority=(1,),
stimulus_id=stimulus_id,
)
if ts.state != "memory":
recommendations[ts] = "fetch"
self.update_who_has(who_has, stimulus_id=stimulus_id)
self.transitions(recommendations, stimulus_id=stimulus_id)
def ensure_task_exists(
self, key: str, *, priority: tuple[int, ...], stimulus_id: str
) -> TaskState:
try:
ts = self.tasks[key]
logger.debug("Data task %s already known (stimulus_id=%s)", ts, stimulus_id)
except KeyError:
self.tasks[key] = ts = TaskState(key)
if not ts.priority:
assert priority
ts.priority = priority
self.log.append((key, "ensure-task-exists", ts.state, stimulus_id, time()))
return ts
def handle_compute_task(
self,
*,
key: str,
who_has: dict[str, Collection[str]],
priority: tuple[int, ...],
duration: float,
function=None,
args=None,
kwargs=None,
task=no_value,
nbytes: dict[str, int] | None = None,
resource_restrictions=None,
actor: bool = False,
annotations=None,
stimulus_id: str,
):
self.log.append((key, "compute-task", stimulus_id, time()))
try:
ts = self.tasks[key]
logger.debug(
"Asked to compute an already known task %s",
{"task": ts, "stimulus_id": stimulus_id},
)
except KeyError:
self.tasks[key] = ts = TaskState(key)
ts.runspec = SerializedTask(function, args, kwargs, task)
assert isinstance(priority, tuple)
priority = priority + (self.generation,)
self.generation -= 1
if actor:
self.actors[ts.key] = None
ts.exception = None
ts.traceback = None
ts.exception_text = ""
ts.traceback_text = ""
ts.priority = priority
ts.duration = duration
if resource_restrictions:
ts.resource_restrictions = resource_restrictions
ts.annotations = annotations
recommendations = {}
scheduler_msgs = []
for dependency in who_has:
dep_ts = self.ensure_task_exists(
key=dependency,
priority=priority,
stimulus_id=stimulus_id,
)
# link up to child / parents
ts.dependencies.add(dep_ts)
dep_ts.dependents.add(ts)
if ts.state in READY | {"executing", "waiting", "resumed"}:
pass
elif ts.state == "memory":
recommendations[ts] = "memory"
scheduler_msgs.append(self._get_task_finished_msg(ts))
elif ts.state in {
"released",
"fetch",
"flight",
"missing",
"cancelled",
"error",
}:
recommendations[ts] = "waiting"
else:
raise RuntimeError(f"Unexpected task state encountered {ts} {stimulus_id}")
for msg in scheduler_msgs:
self.batched_stream.send(msg)
self.transitions(recommendations, stimulus_id=stimulus_id)
# We received new info, that's great but not related to the compute-task
# instruction
self.update_who_has(who_has, stimulus_id=stimulus_id)
if nbytes is not None:
for key, value in nbytes.items():
self.tasks[key].nbytes = value
def transition_missing_fetch(self, ts, *, stimulus_id):
if self.validate:
assert ts.state == "missing"
assert ts.priority is not None
self._missing_dep_flight.discard(ts)
ts.state = "fetch"
ts.done = False
heapq.heappush(self.data_needed, (ts.priority, ts.key))
return {}, []
def transition_missing_released(self, ts, *, stimulus_id):
self._missing_dep_flight.discard(ts)
recommendations, smsgs = self.transition_generic_released(
ts, stimulus_id=stimulus_id
)
assert ts.key in self.tasks
return recommendations, smsgs
def transition_fetch_missing(self, ts, *, stimulus_id):
# handle_missing will append to self.data_needed if new workers are found
ts.state = "missing"
self._missing_dep_flight.add(ts)
return {}, []
def transition_released_fetch(self, ts, *, stimulus_id):
if self.validate:
assert ts.state == "released"
assert ts.priority is not None
for w in ts.who_has:
self.pending_data_per_worker[w].append(ts.key)
ts.state = "fetch"
ts.done = False
heapq.heappush(self.data_needed, (ts.priority, ts.key))
return {}, []
def transition_generic_released(self, ts, *, stimulus_id):
self.release_key(ts.key, reason=stimulus_id)
recs = {}
for dependency in ts.dependencies:
if (
not dependency.waiters
and dependency.state not in READY | PROCESSING | {"memory"}
):
recs[dependency] = "released"
if not ts.dependents:
recs[ts] = "forgotten"
return recs, []
def transition_released_waiting(self, ts, *, stimulus_id):
if self.validate:
assert ts.state == "released"
assert all(d.key in self.tasks for d in ts.dependencies)
recommendations = {}
ts.waiting_for_data.clear()
for dep_ts in ts.dependencies:
if not dep_ts.state == "memory":
ts.waiting_for_data.add(dep_ts)
dep_ts.waiters.add(ts)
if dep_ts.state not in {"fetch", "flight"}:
recommendations[dep_ts] = "fetch"
if ts.waiting_for_data:
self.waiting_for_data_count += 1
elif ts.resource_restrictions:
recommendations[ts] = "constrained"
else:
recommendations[ts] = "ready"
ts.state = "waiting"
return recommendations, []
def transition_fetch_flight(self, ts, worker, *, stimulus_id):
if self.validate:
assert ts.state == "fetch"
assert ts.who_has
assert ts.key not in self.data_needed
ts.done = False
ts.state = "flight"
ts.coming_from = worker
self._in_flight_tasks.add(ts)
return {}, []
def transition_memory_released(self, ts, *, stimulus_id):
recs, smsgs = self.transition_generic_released(ts, stimulus_id=stimulus_id)
smsgs.append({"op": "release-worker-data", "key": ts.key})
return recs, smsgs
def transition_waiting_constrained(self, ts, *, stimulus_id):
if self.validate:
assert ts.state == "waiting"
assert not ts.waiting_for_data
assert all(
dep.key in self.data or dep.key in self.actors
for dep in ts.dependencies
)
assert all(dep.state == "memory" for dep in ts.dependencies)
assert ts.key not in self.ready
ts.state = "constrained"
self.constrained.append(ts.key)
return {}, []
def transition_long_running_rescheduled(self, ts, *, stimulus_id):
recs = {ts: "released"}
smsgs = [{"op": "reschedule", "key": ts.key, "worker": self.address}]
return recs, smsgs
def transition_executing_rescheduled(self, ts, *, stimulus_id):
for resource, quantity in ts.resource_restrictions.items():
self.available_resources[resource] += quantity
self._executing.discard(ts)
recs = {ts: "released"}
smsgs = [{"op": "reschedule", "key": ts.key, "worker": self.address}]
return recs, smsgs
def transition_waiting_ready(self, ts, *, stimulus_id):
if self.validate:
assert ts.state == "waiting"
assert ts.key not in self.ready
assert not ts.waiting_for_data
assert ts.priority is not None
for dep in ts.dependencies:
assert dep.key in self.data or dep.key in self.actors
assert dep.state == "memory"
ts.state = "ready"
heapq.heappush(self.ready, (ts.priority, ts.key))
return {}, []
def transition_cancelled_error(
self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id
):
recs, msgs = {}, []
if ts._previous == "executing":
recs, msgs = self.transition_executing_error(
ts,
exception,
traceback,
exception_text,
traceback_text,
stimulus_id=stimulus_id,
)
elif ts._previous == "flight":
recs, msgs = self.transition_flight_error(
ts,
exception,
traceback,
exception_text,
traceback_text,
stimulus_id=stimulus_id,
)
if ts._next:
recs[ts] = ts._next
return recs, msgs
def transition_generic_error(
self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id
):
ts.exception = exception
ts.traceback = traceback
ts.exception_text = exception_text
ts.traceback_text = traceback_text
ts.state = "error"
smsg = {
"op": "task-erred",
"status": "error",
"key": ts.key,
"thread": self.threads.get(ts.key),
"exception": ts.exception,
"traceback": ts.traceback,
"exception_text": ts.exception_text,
"traceback_text": ts.traceback_text,
}
if ts.startstops:
smsg["startstops"] = ts.startstops
return {}, [smsg]
def transition_executing_error(
self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id
):
for resource, quantity in ts.resource_restrictions.items():
self.available_resources[resource] += quantity
self._executing.discard(ts)
return self.transition_generic_error(
ts,
exception,
traceback,
exception_text,
traceback_text,
stimulus_id=stimulus_id,
)
[docs] def transition_resumed_fetch(self, ts, *, stimulus_id):
"""`resumed` is an intermediate degenerate state which splits further up
into two states depending on what the last signal / next state is
intended to be. There are only two viable choices depending on whether
the task is required to be fetched from another worker `resumed(fetch)`
or the task shall be computed on this worker `resumed(waiting)`.
The only viable state transitions ending up here are
flight -> cancelled -> resumed(waiting)
or
executing -> cancelled -> resumed(fetch)
depending on the origin. Equally, only `fetch`, `waiting` or `released`
are allowed output states.
See also `transition_resumed_waiting`
"""
# if the next state is already intended to be fetch or if the
# coro/thread is still running (ts.done==False), this is a noop
if ts._next == "fetch":
return {}, []
ts._next = "fetch"
if ts.done:
next_state = ts._next
recs, smsgs = self.transition_generic_released(ts, stimulus_id=stimulus_id)
recs[ts] = next_state
return recs, smsgs
return {}, []
[docs] def transition_resumed_waiting(self, ts, *, stimulus_id):
"""
See transition_resumed_fetch
"""
if ts._next == "waiting":
return {}, []
ts._next = "waiting"
if ts.done:
next_state = ts._next
recs, smsgs = self.transition_generic_released(ts, stimulus_id=stimulus_id)
recs[ts] = next_state
return recs, smsgs
return {}, []
def transition_cancelled_fetch(self, ts, *, stimulus_id):
if ts.done:
return {ts: "released"}, []
elif ts._previous == "flight":
ts.state = ts._previous
return {}, []
else:
assert ts._previous == "executing"
return {ts: ("resumed", "fetch")}, []
def transition_cancelled_resumed(self, ts, next, *, stimulus_id):
ts._next = next
ts.state = "resumed"
return {}, []
def transition_cancelled_waiting(self, ts, *, stimulus_id):
if ts.done:
return {ts: "released"}, []
elif ts._previous == "executing":
ts.state = ts._previous
return {}, []
else:
assert ts._previous == "flight"
return {ts: ("resumed", "waiting")}, []
def transition_cancelled_forgotten(self, ts, *, stimulus_id):
ts._next = "forgotten"
if not ts.done:
return {}, []
return {ts: "released"}, []
def transition_cancelled_released(self, ts, *, stimulus_id):
if not ts.done:
ts._next = "released"
return {}, []
next_state = ts._next
self._executing.discard(ts)
self._in_flight_tasks.discard(ts)
for resource, quantity in ts.resource_restrictions.items():
self.available_resources[resource] += quantity
recommendations, smsgs = self.transition_generic_released(
ts, stimulus_id=stimulus_id
)
if next_state != "released":
recommendations[ts] = next_state
return recommendations, smsgs
def transition_executing_released(self, ts, *, stimulus_id):
ts._previous = ts.state
ts._next = "released"
# See https://github.com/dask/distributed/pull/5046#discussion_r685093940
ts.state = "cancelled"
ts.done = False
return {}, []
def transition_long_running_memory(self, ts, value=no_value, *, stimulus_id):
self.executed_count += 1
return self.transition_generic_memory(ts, value=value, stimulus_id=stimulus_id)
def transition_generic_memory(self, ts, value=no_value, *, stimulus_id):
if value is no_value and ts.key not in self.data:
raise RuntimeError(
f"Tried to transition task {ts} to `memory` without data available"
)
if ts.resource_restrictions is not None:
for resource, quantity in ts.resource_restrictions.items():
self.available_resources[resource] += quantity
self._executing.discard(ts)
self._in_flight_tasks.discard(ts)
ts.coming_from = None
try:
recs = self._put_key_in_memory(ts, value, stimulus_id=stimulus_id)
except Exception as e:
msg = error_message(e)
recs = {ts: tuple(msg.values())}
return recs, []
assert ts.key in self.data or ts.key in self.actors
smsgs = [self._get_task_finished_msg(ts)]
return recs, smsgs
def transition_executing_memory(self, ts, value=no_value, *, stimulus_id):
if self.validate:
assert ts.state == "executing" or ts.key in self.long_running
assert not ts.waiting_for_data
assert ts.key not in self.ready
self._executing.discard(ts)
self.executed_count += 1
return self.transition_generic_memory(ts, value=value, stimulus_id=stimulus_id)
def transition_constrained_executing(self, ts, *, stimulus_id):
if self.validate:
assert not ts.waiting_for_data
assert ts.key not in self.data
assert ts.state in READY
assert ts.key not in self.ready
for dep in ts.dependencies:
assert dep.key in self.data or dep.key in self.actors
for resource, quantity in ts.resource_restrictions.items():
self.available_resources[resource] -= quantity
ts.state = "executing"
self._executing.add(ts)
self.loop.add_callback(self.execute, ts.key, stimulus_id=stimulus_id)
return {}, []
def transition_ready_executing(self, ts, *, stimulus_id):
if self.validate:
assert not ts.waiting_for_data
assert ts.key not in self.data
assert ts.state in READY
assert ts.key not in self.ready
assert all(
dep.key in self.data or dep.key in self.actors
for dep in ts.dependencies
)
ts.state = "executing"
self._executing.add(ts)
self.loop.add_callback(self.execute, ts.key, stimulus_id=stimulus_id)
return {}, []
def transition_flight_fetch(self, ts, *, stimulus_id):
# If this transition is called after the flight coroutine has finished,
# we can reset the task and transition to fetch again. If it is not yet
# finished, this should be a no-op
if ts.done:
recommendations, smsgs = self.transition_generic_released(
ts, stimulus_id=stimulus_id
)
recommendations[ts] = "fetch"
return recommendations, smsgs
else:
return {}, []
def transition_flight_error(
self, ts, exception, traceback, exception_text, traceback_text, *, stimulus_id
):
self._in_flight_tasks.discard(ts)
ts.coming_from = None
return self.transition_generic_error(
ts,
exception,
traceback,
exception_text,
traceback_text,
stimulus_id=stimulus_id,
)
def transition_flight_released(self, ts, *, stimulus_id):
if ts.done:
# FIXME: Is this even possible? Would an assert instead be more
# sensible?
return self.transition_generic_released(ts, stimulus_id=stimulus_id)
else:
ts._previous = "flight"
ts._next = "released"
# See https://github.com/dask/distributed/pull/5046#discussion_r685093940
ts.state = "cancelled"
return {}, []
def transition_cancelled_memory(self, ts, value, *, stimulus_id):
return {ts: ts._next}, []
def transition_executing_long_running(self, ts, compute_duration, *, stimulus_id):
ts.state = "long-running"
self._executing.discard(ts)
self.long_running.add(ts.key)
smsgs = [
{
"op": "long-running",
"key": ts.key,
"compute_duration": compute_duration,
}
]
self.io_loop.add_callback(self.ensure_computing)
return {}, smsgs
def transition_released_memory(self, ts, value, *, stimulus_id):
recommendations = {}
try:
recommendations = self._put_key_in_memory(
ts, value, stimulus_id=stimulus_id
)
except Exception as e:
msg = error_message(e)
recommendations[ts] = (
"error",
msg["exception"],
msg["traceback"],
msg["exception_text"],
msg["traceback_text"],
)
return recommendations, []
smsgs = [{"op": "add-keys", "keys": [ts.key], "stimulus_id": stimulus_id}]
return recommendations, smsgs
def transition_flight_memory(self, ts, value, *, stimulus_id):
self._in_flight_tasks.discard(ts)
ts.coming_from = None
recommendations = {}
try:
recommendations = self._put_key_in_memory(
ts, value, stimulus_id=stimulus_id
)
except Exception as e:
msg = error_message(e)
recommendations[ts] = (
"error",
msg["exception"],
msg["traceback"],
msg["exception_text"],
msg["traceback_text"],
)
return recommendations, []
smsgs = [{"op": "add-keys", "keys": [ts.key], "stimulus_id": stimulus_id}]
return recommendations, smsgs
def transition_released_forgotten(self, ts, *, stimulus_id):
recommendations = {}
# Dependents _should_ be released by the scheduler before this
if self.validate:
assert not any(d.state != "forgotten" for d in ts.dependents)
for dep in ts.dependencies:
dep.dependents.discard(ts)
if dep.state == "released" and not dep.dependents:
recommendations[dep] = "forgotten"
# Mark state as forgotten in case it is still referenced
ts.state = "forgotten"
self.tasks.pop(ts.key, None)
return recommendations, []
def _transition(self, ts, finish, *args, stimulus_id, **kwargs):
if isinstance(finish, tuple):
# the concatenated transition path might need to access the tuple
assert not args
finish, *args = finish
if ts is None or ts.state == finish:
return {}, []
start = ts.state
func = self._transitions_table.get((start, finish))
if func is not None:
self._transition_counter += 1
recs, smsgs = func(ts, *args, stimulus_id=stimulus_id, **kwargs)
self._notify_plugins("transition", ts.key, start, finish, **kwargs)
elif "released" not in (start, finish):
# start -> "released" -> finish
try:
recs, smsgs = self._transition(ts, "released", stimulus_id=stimulus_id)
v = recs.get(ts, (finish, *args))
if isinstance(v, tuple):
v_state, *v_args = v
else:
v_state, v_args = v, ()
b_recs, b_smsgs = self._transition(
ts, v_state, *v_args, stimulus_id=stimulus_id
)
recs.update(b_recs)
smsgs += b_smsgs
except InvalidTransition:
raise InvalidTransition(
f"Impossible transition from {start} to {finish} for {ts.key}"
) from None
else:
raise InvalidTransition(
f"Impossible transition from {start} to {finish} for {ts.key}"
)
self.log.append(
(
# key
ts.key,
# initial
start,
# recommended
finish,
# final
ts.state,
# new recommendations
{ts.key: new for ts, new in recs.items()},
stimulus_id,
time(),
)
)
return recs, smsgs
[docs] def transition(self, ts, finish: str, *, stimulus_id, **kwargs):
"""Transition a key from its current state to the finish state
Examples
--------
>>> self.transition('x', 'waiting')
{'x': 'processing'}
Returns
-------
Dictionary of recommendations for future transitions
See Also
--------
Scheduler.transitions: transitive version of this function
"""
recs, smsgs = self._transition(ts, finish, stimulus_id=stimulus_id, **kwargs)
for msg in smsgs:
self.batched_stream.send(msg)
self.transitions(recs, stimulus_id=stimulus_id)
[docs] def transitions(self, recommendations: dict, *, stimulus_id):
"""Process transitions until none are left
This includes feedback from previous transitions and continues until we
reach a steady state
"""
smsgs = []
remaining_recs = recommendations.copy()
tasks = set()
while remaining_recs:
ts, finish = remaining_recs.popitem()
tasks.add(ts)
a_recs, a_smsgs = self._transition(ts, finish, stimulus_id=stimulus_id)
remaining_recs.update(a_recs)
smsgs += a_smsgs
if self.validate:
# Full state validation is very expensive
for ts in tasks:
self.validate_task(ts)
if not self.batched_stream.closed():
for msg in smsgs:
self.batched_stream.send(msg)
else:
logger.debug(
"BatchedSend closed while transitioning tasks. %d tasks not sent.",
len(smsgs),
)
def maybe_transition_long_running(self, ts, *, stimulus_id, compute_duration=None):
if ts.state == "executing":
self.transition(
ts,
"long-running",
compute_duration=compute_duration,
stimulus_id=stimulus_id,
)
assert ts.state == "long-running"
def stateof(self, key):
ts = self.tasks[key]
return {
"executing": ts.state == "executing",
"waiting_for_data": bool(ts.waiting_for_data),
"heap": key in pluck(1, self.ready),
"data": key in self.data,
}
def story(self, *keys):
keys = [key.key if isinstance(key, TaskState) else key for key in keys]
return [
msg
for msg in self.log
if any(key in msg for key in keys)
or any(
key in c
for key in keys
for c in msg
if isinstance(c, (tuple, list, set))
)
]
def ensure_communicating(self):
stimulus_id = f"ensure-communicating-{time()}"
skipped_worker_in_flight = []
while self.data_needed and (
len(self.in_flight_workers) < self.total_out_connections
or self.comm_nbytes < self.comm_threshold_bytes
):
logger.debug(
"Ensure communicating. Pending: %d. Connections: %d/%d",
len(self.data_needed),
len(self.in_flight_workers),
self.total_out_connections,
)
_, key = heapq.heappop(self.data_needed)
try:
ts = self.tasks[key]
except KeyError:
continue
if ts.state != "fetch":
continue
if not ts.who_has:
self.transition(ts, "missing", stimulus_id=stimulus_id)
continue
workers = [w for w in ts.who_has if w not in self.in_flight_workers]
if not workers:
assert ts.priority is not None
skipped_worker_in_flight.append((ts.priority, ts.key))
continue
host = get_address_host(self.address)
local = [w for w in workers if get_address_host(w) == host]
if local:
worker = random.choice(local)
else:
worker = random.choice(list(workers))
assert worker != self.address
to_gather, total_nbytes = self.select_keys_for_gather(worker, ts.key)
self.log.append(
("gather-dependencies", worker, to_gather, stimulus_id, time())
)
self.comm_nbytes += total_nbytes
self.in_flight_workers[worker] = to_gather
recommendations = {self.tasks[d]: ("flight", worker) for d in to_gather}
self.transitions(recommendations, stimulus_id=stimulus_id)
self.loop.add_callback(
self.gather_dep,
worker=worker,
to_gather=to_gather,
total_nbytes=total_nbytes,
stimulus_id=stimulus_id,
)
for el in skipped_worker_in_flight:
heapq.heappush(self.data_needed, el)
def _get_task_finished_msg(self, ts):
if ts.key not in self.data and ts.key not in self.actors:
raise RuntimeError(f"Task {ts} not ready")
typ = ts.type
if ts.nbytes is None or typ is None:
try:
value = self.data[ts.key]
except KeyError:
value = self.actors[ts.key]
ts.nbytes = sizeof(value)
typ = ts.type = type(value)
del value
try:
typ_serialized = dumps_function(typ)
except PicklingError:
# Some types fail pickling (example: _thread.lock objects),
# send their name as a best effort.
typ_serialized = pickle.dumps(typ.__name__, protocol=4)
d = {
"op": "task-finished",
"status": "OK",
"key": ts.key,
"nbytes": ts.nbytes,
"thread": self.threads.get(ts.key),
"type": typ_serialized,
"typename": typename(typ),
"metadata": ts.metadata,
}
if ts.startstops:
d["startstops"] = ts.startstops
return d
def _put_key_in_memory(self, ts, value, *, stimulus_id):
"""
Put a key into memory and set data related task state attributes.
On success, generate recommendations for dependents.
This method does not generate any scheduler messages since this method
cannot distinguish whether it has to be an `add-task` or a
`task-finished` signal. The caller is required to generate this message
on success.
Raises
------
TypeError:
In case the data is put into the in memory buffer and an exception
occurs during spilling, this raises an exception. This has to be
handled by the caller since most callers generate scheduler messages
on success (see comment above) but we need to signal that this was
not successful.
Can only trigger if spill to disk is enabled and the task is not an
actor.
"""
if ts.key in self.data:
ts.state = "memory"
return {}
recommendations = {}
if ts.key in self.actors:
self.actors[ts.key] = value
else:
start = time()
self.data[ts.key] = value
stop = time()
if stop - start > 0.020:
ts.startstops.append(
{"action": "disk-write", "start": start, "stop": stop}
)
ts.state = "memory"
if ts.nbytes is None:
ts.nbytes = sizeof(value)
ts.type = type(value)
for dep in ts.dependents:
dep.waiting_for_data.discard(ts)
if not dep.waiting_for_data and dep.state == "waiting":
self.waiting_for_data_count -= 1
recommendations[dep] = "ready"
self.log.append((ts.key, "put-in-memory", stimulus_id, time()))
return recommendations
def select_keys_for_gather(self, worker, dep):
assert isinstance(dep, str)
deps = {dep}
total_bytes = self.tasks[dep].get_nbytes()
L = self.pending_data_per_worker[worker]
while L:
d = L.popleft()
ts = self.tasks.get(d)
if ts is None or ts.state != "fetch":
continue
if total_bytes + ts.get_nbytes() > self.target_message_size:
break
deps.add(d)
total_bytes += ts.get_nbytes()
return deps, total_bytes
@property
def total_comm_bytes(self):
warnings.warn(
"The attribute `Worker.total_comm_bytes` has been renamed to `comm_threshold_bytes`. "
"Future versions will only support the new name.",
DeprecationWarning,
)
return self.comm_threshold_bytes
def _filter_deps_for_fetch(
self, to_gather_keys: Iterable[str]
) -> tuple[set[str], set[str], TaskState | None]:
"""Filter a list of keys before scheduling coroutines to fetch data from workers.
Returns
-------
in_flight_keys:
The subset of keys in to_gather_keys in state `flight` or `resumed`
cancelled_keys:
The subset of tasks in to_gather_keys in state `cancelled` or `memory`
cause:
The task to attach startstops of this transfer to
"""
in_flight_tasks: set[TaskState] = set()
cancelled_keys: set[str] = set()
for key in to_gather_keys:
ts = self.tasks.get(key)
if ts is None:
continue
# At this point, a task has been transitioned fetch->flight
# flight is only allowed to be transitioned into
# {memory, resumed, cancelled}
# resumed and cancelled will block any further transition until this
# coro has been finished
if ts.state in ("flight", "resumed"):
in_flight_tasks.add(ts)
# If the key is already in memory, the fetch should not happen which
# is signalled by the cancelled_keys
elif ts.state in {"cancelled", "memory"}:
cancelled_keys.add(key)
else:
raise RuntimeError(
f"Task {ts.key} found in illegal state {ts.state}. "
"Only states `flight`, `resumed` and `cancelled` possible."
)
# For diagnostics we want to attach the transfer to a single task. this
# task is typically the next to be executed but since we're fetching
# tasks for potentially many dependents, an exact match is not possible.
# If there are no dependents, this is a pure replica fetch
cause = None
for ts in in_flight_tasks:
if ts.dependents:
cause = next(iter(ts.dependents))
break
else:
cause = ts
in_flight_keys = {ts.key for ts in in_flight_tasks}
return in_flight_keys, cancelled_keys, cause
def _update_metrics_received_data(
self, start: float, stop: float, data: dict, cause: TaskState, worker: str
) -> None:
total_bytes = sum(self.tasks[key].get_nbytes() for key in data)
cause.startstops.append(
{
"action": "transfer",
"start": start + self.scheduler_delay,
"stop": stop + self.scheduler_delay,
"source": worker,
}
)
duration = (stop - start) or 0.010
bandwidth = total_bytes / duration
self.incoming_transfer_log.append(
{
"start": start + self.scheduler_delay,
"stop": stop + self.scheduler_delay,
"middle": (start + stop) / 2.0 + self.scheduler_delay,
"duration": duration,
"keys": {key: self.tasks[key].nbytes for key in data},
"total": total_bytes,
"bandwidth": bandwidth,
"who": worker,
}
)
if total_bytes > 1_000_000:
self.bandwidth = self.bandwidth * 0.95 + bandwidth * 0.05
bw, cnt = self.bandwidth_workers[worker]
self.bandwidth_workers[worker] = (bw + bandwidth, cnt + 1)
types = set(map(type, data.values()))
if len(types) == 1:
[typ] = types
bw, cnt = self.bandwidth_types[typ]
self.bandwidth_types[typ] = (bw + bandwidth, cnt + 1)
if self.digests is not None:
self.digests["transfer-bandwidth"].add(total_bytes / duration)
self.digests["transfer-duration"].add(duration)
self.counters["transfer-count"].add(len(data))
self.incoming_count += 1
[docs] async def gather_dep(
self,
worker: str,
to_gather: Iterable[str],
total_nbytes: int,
*,
stimulus_id,
):
"""Gather dependencies for a task from a worker who has them
Parameters
----------
worker : str
Address of worker to gather dependencies from
to_gather : list
Keys of dependencies to gather from worker -- this is not
necessarily equivalent to the full list of dependencies of ``dep``
as some dependencies may already be present on this worker.
total_nbytes : int
Total number of bytes for all the dependencies in to_gather combined
"""
if self.status not in RUNNING:
return
recommendations: dict[TaskState, str | tuple] = {}
with log_errors():
response = {}
to_gather_keys: set[str] = set()
cancelled_keys: set[str] = set()
try:
to_gather_keys, cancelled_keys, cause = self._filter_deps_for_fetch(
to_gather
)
if not to_gather_keys:
self.log.append(
("nothing-to-gather", worker, to_gather, stimulus_id, time())
)
return
assert cause
# Keep namespace clean since this func is long and has many
# dep*, *ts* variables
del to_gather
self.log.append(
("request-dep", worker, to_gather_keys, stimulus_id, time())
)
logger.debug(
"Request %d keys for task %s from %s",
len(to_gather_keys),
cause,
worker,
)
start = time()
response = await get_data_from_worker(
self.rpc, to_gather_keys, worker, who=self.address
)
stop = time()
if response["status"] == "busy":
return
self._update_metrics_received_data(
start=start,
stop=stop,
data=response["data"],
cause=cause,
worker=worker,
)
self.log.append(
("receive-dep", worker, set(response["data"]), stimulus_id, time())
)
except OSError:
logger.exception("Worker stream died during communication: %s", worker)
has_what = self.has_what.pop(worker)
self.pending_data_per_worker.pop(worker)
self.log.append(
("receive-dep-failed", worker, has_what, stimulus_id, time())
)
for d in has_what:
ts = self.tasks[d]
ts.who_has.remove(worker)
except Exception as e:
logger.exception(e)
if self.batched_stream and LOG_PDB:
import pdb
pdb.set_trace()
msg = error_message(e)
for k in self.in_flight_workers[worker]:
ts = self.tasks[k]
recommendations[ts] = tuple(msg.values())
raise
finally:
self.comm_nbytes -= total_nbytes
busy = response.get("status", "") == "busy"
data = response.get("data", {})
if busy:
self.log.append(
("busy-gather", worker, to_gather_keys, stimulus_id, time())
)
for d in self.in_flight_workers.pop(worker):
ts = self.tasks[d]
ts.done = True
if d in cancelled_keys:
if ts.state == "cancelled":
recommendations[ts] = "released"
else:
recommendations[ts] = "fetch"
elif d in data:
recommendations[ts] = ("memory", data[d])
elif busy:
recommendations[ts] = "fetch"
elif ts not in recommendations:
ts.who_has.discard(worker)
self.has_what[worker].discard(ts.key)
self.log.append((d, "missing-dep", stimulus_id, time()))
self.batched_stream.send(
{"op": "missing-data", "errant_worker": worker, "key": d}
)
recommendations[ts] = "fetch"
del data, response
self.transitions(recommendations, stimulus_id=stimulus_id)
self.ensure_computing()
if not busy:
self.repetitively_busy = 0
else:
# Exponential backoff to avoid hammering scheduler/worker
self.repetitively_busy += 1
await asyncio.sleep(0.100 * 1.5 ** self.repetitively_busy)
await self.query_who_has(*to_gather_keys, stimulus_id=stimulus_id)
self.ensure_communicating()
async def find_missing(self):
with log_errors():
if not self._missing_dep_flight:
return
try:
if self.validate:
for ts in self._missing_dep_flight:
assert not ts.who_has
stimulus_id = f"find-missing-{time()}"
who_has = await retry_operation(
self.scheduler.who_has,
keys=[ts.key for ts in self._missing_dep_flight],
)
who_has = {k: v for k, v in who_has.items() if v}
self.update_who_has(who_has, stimulus_id=stimulus_id)
finally:
# This is quite arbitrary but the heartbeat has scaling implemented
self.periodic_callbacks[
"find-missing"
].callback_time = self.periodic_callbacks["heartbeat"].callback_time
self.ensure_communicating()
self.ensure_computing()
async def query_who_has(
self, *deps: str, stimulus_id: str
) -> dict[str, Collection[str]]:
with log_errors():
who_has = await retry_operation(self.scheduler.who_has, keys=deps)
self.update_who_has(who_has, stimulus_id=stimulus_id)
return who_has
def update_who_has(
self, who_has: dict[str, Collection[str]], *, stimulus_id: str
) -> None:
try:
recommendations = {}
for dep, workers in who_has.items():
if not workers:
continue
if dep in self.tasks:
if self.address in workers and self.tasks[dep].state != "memory":
logger.debug(
"Scheduler claims worker %s holds data for task %s which is not true.",
self.name,
dep,
)
# Do not mutate the input dict. That's rude
workers = set(workers) - {self.address}
dep_ts = self.tasks[dep]
if dep_ts.state in FETCH_INTENDED:
dep_ts.who_has.update(workers)
if dep_ts.state == "missing":
recommendations[dep_ts] = "fetch"
for worker in workers:
self.has_what[worker].add(dep)
self.pending_data_per_worker[worker].append(dep_ts.key)
self.transitions(recommendations, stimulus_id=stimulus_id)
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
def handle_steal_request(self, key, stimulus_id):
# There may be a race condition between stealing and releasing a task.
# In this case the self.tasks is already cleared. The `None` will be
# registered as `already-computing` on the other end
ts = self.tasks.get(key)
state = ts.state if ts is not None else None
response = {
"op": "steal-response",
"key": key,
"state": state,
"stimulus_id": stimulus_id,
}
self.batched_stream.send(response)
if state in READY | {"waiting"}:
# If task is marked as "constrained" we haven't yet assigned it an
# `available_resources` to run on, that happens in
# `transition_constrained_executing`
self.transition(ts, "released", stimulus_id=stimulus_id)
def release_key(
self,
key: str,
cause: TaskState | None = None,
reason: str | None = None,
report: bool = True,
) -> None:
try:
if self.validate:
assert not isinstance(key, TaskState)
ts = self.tasks[key]
# needed for legacy notification support
state_before = ts.state
ts.state = "released"
logger.debug(
"Release key %s", {"key": key, "cause": cause, "reason": reason}
)
if cause:
self.log.append((key, "release-key", {"cause": cause}, reason, time()))
else:
self.log.append((key, "release-key", reason, time()))
if key in self.data:
try:
del self.data[key]
except FileNotFoundError:
logger.error("Tried to delete %s but no file found", exc_info=True)
if key in self.actors:
del self.actors[key]
for worker in ts.who_has:
self.has_what[worker].discard(ts.key)
ts.who_has.clear()
if key in self.threads:
del self.threads[key]
if ts.resource_restrictions is not None:
if ts.state == "executing":
for resource, quantity in ts.resource_restrictions.items():
self.available_resources[resource] += quantity
for d in ts.dependencies:
ts.waiting_for_data.discard(d)
d.waiters.discard(ts)
ts.waiting_for_data.clear()
ts.nbytes = None
ts._previous = None
ts._next = None
ts.done = False
self._executing.discard(ts)
self._in_flight_tasks.discard(ts)
self._notify_plugins(
"release_key", key, state_before, cause, reason, report
)
except CommClosedError:
# Batched stream send might raise if it was already closed
pass
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
################
# Execute Task #
################
def run(self, comm, function, args=(), wait=True, kwargs=None):
return run(self, comm, function=function, args=args, kwargs=kwargs, wait=wait)
def run_coroutine(self, comm, function, args=(), kwargs=None, wait=True):
return run(self, comm, function=function, args=args, kwargs=kwargs, wait=wait)
async def plugin_add(self, comm=None, plugin=None, name=None, catch_errors=True):
with log_errors(pdb=False):
if isinstance(plugin, bytes):
plugin = pickle.loads(plugin)
if name is None:
name = _get_plugin_name(plugin)
assert name
if name in self.plugins:
await self.plugin_remove(comm=comm, name=name)
self.plugins[name] = plugin
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
msg = error_message(e)
return msg
return {"status": "OK"}
async def plugin_remove(self, comm=None, name=None):
with log_errors(pdb=False):
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:
msg = error_message(e)
return msg
return {"status": "OK"}
async def actor_execute(
self,
comm=None,
actor=None,
function=None,
args=(),
kwargs: dict | None = None,
):
kwargs = kwargs or {}
separate_thread = kwargs.pop("separate_thread", True)
key = actor
actor = self.actors[key]
func = getattr(actor, function)
name = key_split(key) + "." + function
try:
if iscoroutinefunction(func):
result = await func(*args, **kwargs)
elif separate_thread:
result = await self.loop.run_in_executor(
self.executors["actor"],
apply_function_actor,
func,
args,
kwargs,
self.execution_state,
name,
self.active_threads,
self.active_threads_lock,
)
else:
result = func(*args, **kwargs)
return {"status": "OK", "result": to_serialize(result)}
except Exception as ex:
return {"status": "error", "exception": to_serialize(ex)}
def actor_attribute(self, comm=None, actor=None, attribute=None):
try:
value = getattr(self.actors[actor], attribute)
return {"status": "OK", "result": to_serialize(value)}
except Exception as ex:
return {"status": "error", "exception": to_serialize(ex)}
def meets_resource_constraints(self, key: str) -> bool:
ts = self.tasks[key]
if not ts.resource_restrictions:
return True
for resource, needed in ts.resource_restrictions.items():
if self.available_resources[resource] < needed:
return False
return True
async def _maybe_deserialize_task(self, ts, *, stimulus_id):
if not isinstance(ts.runspec, SerializedTask):
return ts.runspec
try:
start = time()
# Offload deserializing large tasks
if sizeof(ts.runspec) > OFFLOAD_THRESHOLD:
function, args, kwargs = await offload(_deserialize, *ts.runspec)
else:
function, args, kwargs = _deserialize(*ts.runspec)
stop = time()
if stop - start > 0.010:
ts.startstops.append(
{"action": "deserialize", "start": start, "stop": stop}
)
return function, args, kwargs
except Exception as e:
logger.error("Could not deserialize task", exc_info=True)
self.log.append((ts.key, "deserialize-error", stimulus_id, time()))
emsg = error_message(e)
emsg.pop("status")
self.transition(
ts,
"error",
**emsg,
stimulus_id=stimulus_id,
)
raise
def ensure_computing(self):
if self.status == Status.paused:
return
try:
stimulus_id = f"ensure-computing-{time()}"
while self.constrained and self.executing_count < self.nthreads:
key = self.constrained[0]
ts = self.tasks.get(key, None)
if ts is None or ts.state != "constrained":
self.constrained.popleft()
continue
if self.meets_resource_constraints(key):
self.constrained.popleft()
self.transition(ts, "executing", stimulus_id=stimulus_id)
else:
break
while self.ready and self.executing_count < self.nthreads:
priority, key = heapq.heappop(self.ready)
ts = self.tasks.get(key)
if ts is None:
# It is possible for tasks to be released while still remaining on
# `ready` The scheduler might have re-routed to a new worker and
# told this worker to release. If the task has "disappeared" just
# continue through the heap
continue
elif ts.key in self.data:
self.transition(ts, "memory", stimulus_id=stimulus_id)
elif ts.state in READY:
self.transition(ts, "executing", stimulus_id=stimulus_id)
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
async def execute(self, key, *, stimulus_id):
if self.status in (Status.closing, Status.closed, Status.closing_gracefully):
return
if key not in self.tasks:
return
ts = self.tasks[key]
try:
if ts.state == "cancelled":
# This might happen if keys are canceled
logger.debug(
"Trying to execute task %s which is not in executing state anymore",
ts,
)
ts.done = True
self.transition(ts, "released", stimulus_id=stimulus_id)
return
if self.validate:
assert not ts.waiting_for_data
assert ts.state == "executing"
assert ts.runspec is not None
function, args, kwargs = await self._maybe_deserialize_task(
ts, stimulus_id=stimulus_id
)
args2, kwargs2 = self._prepare_args_for_execution(ts, args, kwargs)
if ts.annotations is not None and "executor" in ts.annotations:
executor = ts.annotations["executor"]
else:
executor = "default"
assert executor in self.executors
assert key == ts.key
self.active_keys.add(ts.key)
result: dict
try:
e = self.executors[executor]
ts.start_time = time()
if iscoroutinefunction(function):
result = await apply_function_async(
function,
args2,
kwargs2,
self.scheduler_delay,
)
elif "ThreadPoolExecutor" in str(type(e)):
result = await self.loop.run_in_executor(
e,
apply_function,
function,
args2,
kwargs2,
self.execution_state,
ts.key,
self.active_threads,
self.active_threads_lock,
self.scheduler_delay,
)
else:
result = await self.loop.run_in_executor(
e,
apply_function_simple,
function,
args2,
kwargs2,
self.scheduler_delay,
)
finally:
self.active_keys.discard(ts.key)
key = ts.key
# key *must* be still in tasks. Releasing it direclty is forbidden
# without going through cancelled
ts = self.tasks.get(key)
assert ts, self.story(key)
ts.done = True
result["key"] = ts.key
value = result.pop("result", None)
ts.startstops.append(
{"action": "compute", "start": result["start"], "stop": result["stop"]}
)
self.threads[ts.key] = result["thread"]
recommendations = {}
if result["op"] == "task-finished":
ts.nbytes = result["nbytes"]
ts.type = result["type"]
recommendations[ts] = ("memory", value)
if self.digests is not None:
self.digests["task-duration"].add(result["stop"] - result["start"])
elif isinstance(result.pop("actual-exception"), Reschedule):
recommendations[ts] = "rescheduled"
else:
logger.warning(
"Compute Failed\n"
"Function: %s\n"
"args: %s\n"
"kwargs: %s\n"
"Exception: %r\n",
str(funcname(function))[:1000],
convert_args_to_str(args2, max_len=1000),
convert_kwargs_to_str(kwargs2, max_len=1000),
result["exception_text"],
)
recommendations[ts] = (
"error",
result["exception"],
result["traceback"],
result["exception_text"],
result["traceback_text"],
)
self.transitions(recommendations, stimulus_id=stimulus_id)
logger.debug("Send compute response to scheduler: %s, %s", ts.key, result)
if self.validate:
assert ts.state != "executing"
assert not ts.waiting_for_data
except Exception as exc:
assert ts
logger.error(
"Exception during execution of task %s.", ts.key, exc_info=True
)
emsg = error_message(exc)
emsg.pop("status")
self.transition(
ts,
"error",
**emsg,
stimulus_id=stimulus_id,
)
finally:
self.ensure_computing()
self.ensure_communicating()
def _prepare_args_for_execution(self, ts, args, kwargs):
start = time()
data = {}
for dep in ts.dependencies:
k = dep.key
try:
data[k] = self.data[k]
except KeyError:
from .actor import Actor # TODO: create local actor
data[k] = Actor(type(self.actors[k]), self.address, k, self)
args2 = pack_data(args, data, key_types=(bytes, str))
kwargs2 = pack_data(kwargs, data, key_types=(bytes, str))
stop = time()
if stop - start > 0.005:
ts.startstops.append({"action": "disk-read", "start": start, "stop": stop})
if self.digests is not None:
self.digests["disk-load-duration"].add(stop - start)
return args2, kwargs2
##################
# Administrative #
##################
[docs] async def memory_monitor(self):
"""Track this process's memory usage and act accordingly
If we rise above 70% memory use, start dumping data to disk.
If we rise above 80% memory use, stop execution of new tasks
"""
if self._memory_monitoring:
return
self._memory_monitoring = True
total = 0
proc = self.monitor.proc
memory = proc.memory_info().rss
frac = memory / self.memory_limit
def check_pause(memory):
frac = memory / self.memory_limit
# Pause worker threads if above 80% memory use
if self.memory_pause_fraction and frac > self.memory_pause_fraction:
# Try to free some memory while in paused state
self._throttled_gc.collect()
if self.status == Status.running:
logger.warning(
"Worker is at %d%% memory usage. Pausing worker. "
"Process memory: %s -- Worker memory limit: %s",
int(frac * 100),
format_bytes(memory),
format_bytes(self.memory_limit)
if self.memory_limit is not None
else "None",
)
self.status = Status.paused
elif self.status == Status.paused:
logger.warning(
"Worker is at %d%% memory usage. Resuming worker. "
"Process memory: %s -- Worker memory limit: %s",
int(frac * 100),
format_bytes(memory),
format_bytes(self.memory_limit)
if self.memory_limit is not None
else "None",
)
self.status = Status.running
self.ensure_computing()
check_pause(memory)
# Dump data to disk if above 70%
if self.memory_spill_fraction and frac > self.memory_spill_fraction:
logger.debug(
"Worker is at %.0f%% memory usage. Start spilling data to disk.",
frac * 100,
)
start = time()
target = self.memory_limit * self.memory_target_fraction
count = 0
need = memory - target
while memory > target:
if not self.data.fast:
logger.warning(
"Unmanaged memory use is high. This may indicate a memory leak "
"or the memory may not be released to the OS; see "
"https://distributed.dask.org/en/latest/worker.html#memtrim "
"for more information. "
"-- Unmanaged memory: %s -- Worker memory limit: %s",
format_bytes(memory),
format_bytes(self.memory_limit),
)
break
k, v, weight = self.data.fast.evict()
del k, v
total += weight
count += 1
# If the current buffer is filled with a lot of small values,
# evicting one at a time is very slow and the worker might
# generate new data faster than it is able to evict. Therefore,
# only pass on control if we spent at least 0.5s evicting
if time() - start > 0.5:
await asyncio.sleep(0)
start = time()
memory = proc.memory_info().rss
if total > need and memory > target:
# Issue a GC to ensure that the evicted data is actually
# freed from memory and taken into account by the monitor
# before trying to evict even more data.
self._throttled_gc.collect()
memory = proc.memory_info().rss
check_pause(memory)
if count:
logger.debug(
"Moved %d tasks worth %s to disk",
count,
format_bytes(total),
)
self._memory_monitoring = False
return total
def cycle_profile(self):
now = time() + self.scheduler_delay
prof, self.profile_recent = self.profile_recent, profile.create()
self.profile_history.append((now, prof))
self.profile_keys_history.append((now, dict(self.profile_keys)))
self.profile_keys.clear()
[docs] def trigger_profile(self):
"""
Get a frame from all actively computing threads
Merge these frames into existing profile counts
"""
if not self.active_threads: # hope that this is thread-atomic?
return
start = time()
with self.active_threads_lock:
active_threads = self.active_threads.copy()
frames = sys._current_frames()
frames = {ident: frames[ident] for ident in active_threads}
llframes = {}
if self.low_level_profiler:
llframes = {ident: profile.ll_get_stack(ident) for ident in active_threads}
for ident, frame in frames.items():
if frame is not None:
key = key_split(active_threads[ident])
llframe = llframes.get(ident)
state = profile.process(
frame, True, self.profile_recent, stop="distributed/worker.py"
)
profile.llprocess(llframe, None, state)
profile.process(
frame, True, self.profile_keys[key], stop="distributed/worker.py"
)
stop = time()
if self.digests is not None:
self.digests["profile-duration"].add(stop - start)
async def get_profile(
self, comm=None, start=None, stop=None, key=None, server=False
):
now = time() + self.scheduler_delay
if server:
history = self.io_loop.profile
elif key is None:
history = self.profile_history
else:
history = [(t, d[key]) for t, d in self.profile_keys_history if key in d]
if start is None:
istart = 0
else:
istart = bisect.bisect_left(history, (start,))
if stop is None:
istop = None
else:
istop = bisect.bisect_right(history, (stop,)) + 1
if istop >= len(history):
istop = None # include end
if istart == 0 and istop is None:
history = list(history)
else:
iistop = len(history) if istop is None else istop
history = [history[i] for i in range(istart, iistop)]
prof = profile.merge(*pluck(1, history))
if not history:
return profile.create()
if istop is None and (start is None or start < now):
if key is None:
recent = self.profile_recent
else:
recent = self.profile_keys[key]
prof = profile.merge(prof, recent)
return prof
async def get_profile_metadata(self, comm=None, start=0, stop=None):
add_recent = stop is None
now = time() + self.scheduler_delay
stop = stop or now
start = start or 0
result = {
"counts": [
(t, d["count"]) for t, d in self.profile_history if start < t < stop
],
"keys": [
(t, {k: d["count"] for k, d in v.items()})
for t, v in self.profile_keys_history
if start < t < stop
],
}
if add_recent:
result["counts"].append((now, self.profile_recent["count"]))
result["keys"].append(
(now, {k: v["count"] for k, v in self.profile_keys.items()})
)
return result
def get_call_stack(self, comm=None, keys=None):
with self.active_threads_lock:
frames = sys._current_frames()
active_threads = self.active_threads.copy()
frames = {k: frames[ident] for ident, k in active_threads.items()}
if keys is not None:
frames = {k: frame for k, frame in frames.items() if k in keys}
result = {k: profile.call_stack(frame) for k, frame in frames.items()}
return result
def _notify_plugins(self, method_name, *args, **kwargs):
for name, plugin in self.plugins.items():
if hasattr(plugin, method_name):
if method_name == "release_key":
warnings.warn(
"The `WorkerPlugin.release_key` hook is depreacted and will be "
"removed in a future version. A similar event can now be "
"caught by filtering for a `finish=='released'` event in the "
"`WorkerPlugin.transition` hook.",
DeprecationWarning,
)
try:
getattr(plugin, method_name)(*args, **kwargs)
except Exception:
logger.info(
"Plugin '%s' failed with exception", name, exc_info=True
)
##############
# Validation #
##############
def validate_task_memory(self, ts):
assert ts.key in self.data or ts.key in self.actors
assert isinstance(ts.nbytes, int)
assert not ts.waiting_for_data
assert ts.key not in self.ready
assert ts.state == "memory"
def validate_task_executing(self, ts):
assert ts.state == "executing"
assert ts.runspec is not None
assert ts.key not in self.data
assert not ts.waiting_for_data
for dep in ts.dependencies:
assert dep.state == "memory", self.story(dep)
assert dep.key in self.data or dep.key in self.actors
def validate_task_ready(self, ts):
assert ts.key in pluck(1, self.ready)
assert ts.key not in self.data
assert ts.state != "executing"
assert not ts.done
assert not ts.waiting_for_data
assert all(
dep.key in self.data or dep.key in self.actors for dep in ts.dependencies
)
def validate_task_waiting(self, ts):
assert ts.key not in self.data
assert ts.state == "waiting"
assert not ts.done
if ts.dependencies and ts.runspec:
assert not all(dep.key in self.data for dep in ts.dependencies)
def validate_task_flight(self, ts):
assert ts.key not in self.data
assert ts in self._in_flight_tasks
assert not any(dep.key in self.ready for dep in ts.dependents)
assert ts.coming_from
assert ts.coming_from in self.in_flight_workers
assert ts.key in self.in_flight_workers[ts.coming_from]
def validate_task_fetch(self, ts):
assert ts.key not in self.data
assert self.address not in ts.who_has
assert not ts.done
for w in ts.who_has:
assert ts.key in self.has_what[w]
def validate_task_missing(self, ts):
assert ts.key not in self.data
assert not ts.who_has
assert not ts.done
assert not any(ts.key in has_what for has_what in self.has_what.values())
assert ts.key in self._missing_dep_flight
def validate_task_cancelled(self, ts):
assert ts.key not in self.data
assert ts._previous
assert ts._next
def validate_task_resumed(self, ts):
assert ts.key not in self.data
assert ts._next
assert ts._previous
def validate_task_released(self, ts):
assert ts.key not in self.data
assert not ts._next
assert not ts._previous
assert ts not in self._executing
assert ts not in self._in_flight_tasks
assert ts not in self._missing_dep_flight
assert ts not in self._missing_dep_flight
assert not ts.who_has
assert not any(ts.key in has_what for has_what in self.has_what.values())
assert not ts.waiting_for_data
assert not ts.done
assert not ts.exception
assert not ts.traceback
def validate_task(self, ts):
try:
if ts.key in self.tasks:
assert self.tasks[ts.key] == ts
if ts.state == "memory":
self.validate_task_memory(ts)
elif ts.state == "waiting":
self.validate_task_waiting(ts)
elif ts.state == "missing":
self.validate_task_missing(ts)
elif ts.state == "cancelled":
self.validate_task_cancelled(ts)
elif ts.state == "resumed":
self.validate_task_resumed(ts)
elif ts.state == "ready":
self.validate_task_ready(ts)
elif ts.state == "executing":
self.validate_task_executing(ts)
elif ts.state == "flight":
self.validate_task_flight(ts)
elif ts.state == "fetch":
self.validate_task_fetch(ts)
elif ts.state == "released":
self.validate_task_released(ts)
except Exception as e:
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise AssertionError(
f"Invalid TaskState encountered for {ts!r}.\nStory:\n{self.story(ts)}\n"
) from e
def validate_state(self):
if self.status not in RUNNING:
return
try:
assert self.executing_count >= 0
waiting_for_data_count = 0
for ts in self.tasks.values():
assert ts.state is not None
# check that worker has task
for worker in ts.who_has:
assert ts.key in self.has_what[worker]
# check that deps have a set state and that dependency<->dependent links
# are there
for dep in ts.dependencies:
# self.tasks was just a dict of tasks
# and this check was originally that the key was in `task_state`
# so we may have popped the key out of `self.tasks` but the
# dependency can still be in `memory` before GC grabs it...?
# Might need better bookkeeping
assert dep.state is not None
assert ts in dep.dependents, ts
if ts.waiting_for_data:
waiting_for_data_count += 1
for ts_wait in ts.waiting_for_data:
assert ts_wait.key in self.tasks
assert (
ts_wait.state
in READY | {"executing", "flight", "fetch", "missing"}
or ts_wait.key in self._missing_dep_flight
or ts_wait.who_has.issubset(self.in_flight_workers)
), (ts, ts_wait, self.story(ts), self.story(ts_wait))
if ts.state == "memory":
assert isinstance(ts.nbytes, int)
assert not ts.waiting_for_data
assert ts.key in self.data or ts.key in self.actors
assert self.waiting_for_data_count == waiting_for_data_count
for worker, keys in self.has_what.items():
for k in keys:
assert worker in self.tasks[k].who_has
for ts in self.tasks.values():
self.validate_task(ts)
except Exception as e:
self.loop.add_callback(self.close)
logger.exception(e)
if LOG_PDB:
import pdb
pdb.set_trace()
raise
#######################################
# Worker Clients (advanced workloads) #
#######################################
@property
def client(self) -> Client:
with self._lock:
if self._client:
return self._client
else:
return self._get_client()
def _get_client(self, timeout: float | None = None) -> Client:
"""Get local client attached to this worker
If no such client exists, create one
See Also
--------
get_client
"""
if timeout is None:
timeout = dask.config.get("distributed.comm.timeouts.connect")
timeout = parse_timedelta(timeout, "s")
try:
from .client import default_client
client = default_client()
except ValueError: # no clients found, need to make a new one
pass
else:
# must be lazy import otherwise cyclic import
from distributed.deploy.cluster import Cluster
if (
client.scheduler
and client.scheduler.address == self.scheduler.address
# The below conditions should only happen in case a second
# cluster is alive, e.g. if a submitted task spawned its onwn
# LocalCluster, see gh4565
or (
isinstance(client._start_arg, str)
and client._start_arg == self.scheduler.address
or isinstance(client._start_arg, Cluster)
and client._start_arg.scheduler_address == self.scheduler.address
)
):
self._client = client
if not self._client:
from .client import Client
asynchronous = in_async_call(self.loop)
self._client = Client(
self.scheduler,
loop=self.loop,
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 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 thread_state.execution_state["worker"]
except AttributeError:
try:
return first(w for w in Worker._instances if w.status in RUNNING)
except StopIteration:
raise ValueError("No workers found")
[docs]def get_client(address=None, timeout=None, resolve_address=True) -> Client:
"""Get a client while within a task.
This client connects to the same scheduler to which the worker is connected
Parameters
----------
address : str, optional
The address of the scheduler to connect to. Defaults to the scheduler
the worker is connected to.
timeout : int or str
Timeout (in seconds) for getting the Client. Defaults to the
``distributed.comm.timeouts.connect`` configuration value.
resolve_address : bool, default True
Whether to resolve `address` to its canonical form.
Returns
-------
Client
Examples
--------
>>> def f():
... client = get_client(timeout="10s")
... futures = 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 .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.maybe_transition_long_running,
worker.tasks[thread_state.key],
compute_duration=duration,
stimulus_id=f"secede-{thread_state.key}-{time()}",
)
class Reschedule(Exception):
"""Reschedule this task
Raising this exception will stop the current execution of the task and ask
the scheduler to reschedule this task, possibly on a different machine.
This does not guarantee that the task will move onto a different machine.
The scheduler will proceed through its normal heuristics to determine the
optimal machine to accept this task. The machine will likely change if the
load across the cluster has significantly changed since first scheduling
the task.
"""
def parse_memory_limit(memory_limit, nthreads, total_cores=CPU_COUNT) -> int | None:
if memory_limit is None:
return None
if memory_limit == "auto":
memory_limit = int(system.MEMORY_LIMIT * min(1, nthreads / total_cores))
with suppress(ValueError, TypeError):
memory_limit = float(memory_limit)
if isinstance(memory_limit, float) and memory_limit <= 1:
memory_limit = int(memory_limit * system.MEMORY_LIMIT)
if isinstance(memory_limit, str):
memory_limit = parse_bytes(memory_limit)
else:
memory_limit = int(memory_limit)
return min(memory_limit, system.MEMORY_LIMIT)
async def get_data_from_worker(
rpc,
keys,
worker,
who=None,
max_connections=None,
serializers=None,
deserializers=None,
):
"""Get keys from worker
The worker has a two step handshake to acknowledge when data has been fully
delivered. This function implements that handshake.
See Also
--------
Worker.get_data
Worker.gather_dep
utils_comm.gather_data_from_workers
"""
if serializers is None:
serializers = rpc.serializers
if deserializers is None:
deserializers = rpc.deserializers
async def _get_data():
comm = await rpc.connect(worker)
comm.name = "Ephemeral Worker->Worker for gather"
try:
response = await send_recv(
comm,
serializers=serializers,
deserializers=deserializers,
op="get_data",
keys=keys,
who=who,
max_connections=max_connections,
)
try:
status = response["status"]
except KeyError:
raise ValueError("Unexpected response", response)
else:
if status == "OK":
await comm.write("OK")
return response
finally:
rpc.reuse(worker, comm)
return await retry_operation(_get_data, operation="get_data_from_worker")
job_counter = [0]
cache_loads = LRU(maxsize=100)
def loads_function(bytes_object):
"""Load a function from bytes, cache bytes"""
if len(bytes_object) < 100000:
try:
result = cache_loads[bytes_object]
except KeyError:
result = pickle.loads(bytes_object)
cache_loads[bytes_object] = result
return result
return pickle.loads(bytes_object)
def _deserialize(function=None, args=None, kwargs=None, task=no_value):
"""Deserialize task inputs and regularize to func, args, kwargs"""
if function is not None:
function = loads_function(function)
if args and isinstance(args, bytes):
args = pickle.loads(args)
if kwargs and isinstance(kwargs, bytes):
kwargs = pickle.loads(kwargs)
if task is not no_value:
assert not function and not args and not kwargs
function = execute_task
args = (task,)
return function, args or (), kwargs or {}
def execute_task(task):
"""Evaluate a nested task
>>> inc = lambda x: x + 1
>>> execute_task((inc, 1))
2
>>> execute_task((sum, [1, 2, (inc, 3)]))
7
"""
if istask(task):
func, args = task[0], task[1:]
return func(*map(execute_task, args))
elif isinstance(task, list):
return list(map(execute_task, task))
else:
return task
cache_dumps = LRU(maxsize=100)
_cache_lock = threading.Lock()
def dumps_function(func) -> bytes:
"""Dump a function to bytes, cache functions"""
try:
with _cache_lock:
result = cache_dumps[func]
except KeyError:
result = pickle.dumps(func, protocol=4)
if len(result) < 100000:
with _cache_lock:
cache_dumps[func] = result
except TypeError: # Unhashable function
result = pickle.dumps(func, protocol=4)
return result
def dumps_task(task):
"""Serialize a dask task
Returns a dict of bytestrings that can each be loaded with ``loads``
Examples
--------
Either returns a task as a function, args, kwargs dict
>>> from operator import add
>>> dumps_task((add, 1)) # doctest: +SKIP
{'function': b'\x80\x04\x95\x00\x8c\t_operator\x94\x8c\x03add\x94\x93\x94.'
'args': b'\x80\x04\x95\x07\x00\x00\x00K\x01K\x02\x86\x94.'}
Or as a single task blob if it can't easily decompose the result. This
happens either if the task is highly nested, or if it isn't a task at all
>>> dumps_task(1) # doctest: +SKIP
{'task': b'\x80\x04\x95\x03\x00\x00\x00\x00\x00\x00\x00K\x01.'}
"""
if istask(task):
if task[0] is apply and not any(map(_maybe_complex, task[2:])):
d = {"function": dumps_function(task[1]), "args": warn_dumps(task[2])}
if len(task) == 4:
d["kwargs"] = warn_dumps(task[3])
return d
elif not any(map(_maybe_complex, task[1:])):
return {"function": dumps_function(task[0]), "args": warn_dumps(task[1:])}
return to_serialize(task)
_warn_dumps_warned = [False]
def warn_dumps(obj, dumps=pickle.dumps, limit=1e6):
"""Dump an object to bytes, warn if those bytes are large"""
b = dumps(obj, protocol=4)
if not _warn_dumps_warned[0] and len(b) > limit:
_warn_dumps_warned[0] = True
s = str(obj)
if len(s) > 70:
s = s[:50] + " ... " + s[-15:]
warnings.warn(
"Large object of size %s detected in task graph: \n"
" %s\n"
"Consider scattering large objects ahead of time\n"
"with client.scatter to reduce scheduler burden and \n"
"keep data on workers\n\n"
" future = client.submit(func, big_data) # bad\n\n"
" big_future = client.scatter(big_data) # good\n"
" future = client.submit(func, big_future) # good"
% (format_bytes(len(b)), s)
)
return b
def apply_function(
function,
args,
kwargs,
execution_state,
key,
active_threads,
active_threads_lock,
time_delay,
):
"""Run a function, collect information
Returns
-------
msg: dictionary with status, result/error, timings, etc..
"""
ident = threading.get_ident()
with active_threads_lock:
active_threads[ident] = key
thread_state.start_time = time()
thread_state.execution_state = execution_state
thread_state.key = key
msg = apply_function_simple(function, args, kwargs, time_delay)
with active_threads_lock:
del active_threads[ident]
return msg
def apply_function_simple(
function,
args,
kwargs,
time_delay,
):
"""Run a function, collect information
Returns
-------
msg: dictionary with status, result/error, timings, etc..
"""
ident = threading.get_ident()
start = time()
try:
result = function(*args, **kwargs)
except Exception as e:
msg = error_message(e)
msg["op"] = "task-erred"
msg["actual-exception"] = e
else:
msg = {
"op": "task-finished",
"status": "OK",
"result": result,
"nbytes": sizeof(result),
"type": type(result) if result is not None else None,
}
finally:
end = time()
msg["start"] = start + time_delay
msg["stop"] = end + time_delay
msg["thread"] = ident
return msg
async def apply_function_async(
function,
args,
kwargs,
time_delay,
):
"""Run a function, collect information
Returns
-------
msg: dictionary with status, result/error, timings, etc..
"""
ident = threading.get_ident()
start = time()
try:
result = await function(*args, **kwargs)
except Exception as e:
msg = error_message(e)
msg["op"] = "task-erred"
msg["actual-exception"] = e
else:
msg = {
"op": "task-finished",
"status": "OK",
"result": result,
"nbytes": sizeof(result),
"type": type(result) if result is not None else None,
}
finally:
end = time()
msg["start"] = start + time_delay
msg["stop"] = end + time_delay
msg["thread"] = ident
return msg
def apply_function_actor(
function, args, kwargs, execution_state, key, active_threads, active_threads_lock
):
"""Run a function, collect information
Returns
-------
msg: dictionary with status, result/error, timings, etc..
"""
ident = threading.get_ident()
with active_threads_lock:
active_threads[ident] = key
thread_state.execution_state = execution_state
thread_state.key = key
thread_state.actor = True
result = function(*args, **kwargs)
with active_threads_lock:
del active_threads[ident]
return result
def get_msg_safe_str(msg):
"""Make a worker msg, which contains args and kwargs, safe to cast to str:
allowing for some arguments to raise exceptions during conversion and
ignoring them.
"""
class Repr:
def __init__(self, f, val):
self._f = f
self._val = val
def __repr__(self):
return self._f(self._val)
msg = msg.copy()
if "args" in msg:
msg["args"] = Repr(convert_args_to_str, msg["args"])
if "kwargs" in msg:
msg["kwargs"] = Repr(convert_kwargs_to_str, msg["kwargs"])
return msg
def convert_args_to_str(args, max_len: int | None = None) -> str:
"""Convert args to a string, allowing for some arguments to raise
exceptions during conversion and ignoring them.
"""
length = 0
strs = ["" for i in range(len(args))]
for i, arg in enumerate(args):
try:
sarg = repr(arg)
except Exception:
sarg = "< could not convert arg to str >"
strs[i] = sarg
length += len(sarg) + 2
if max_len is not None and length > max_len:
return "({}".format(", ".join(strs[: i + 1]))[:max_len]
else:
return "({})".format(", ".join(strs))
def convert_kwargs_to_str(kwargs: dict, max_len: int | None = None) -> str:
"""Convert kwargs to a string, allowing for some arguments to raise
exceptions during conversion and ignoring them.
"""
length = 0
strs = ["" for i in range(len(kwargs))]
for i, (argname, arg) in enumerate(kwargs.items()):
try:
sarg = repr(arg)
except Exception:
sarg = "< could not convert arg to str >"
skwarg = repr(argname) + ": " + sarg
strs[i] = skwarg
length += len(skwarg) + 2
if max_len is not None and length > max_len:
return "{{{}".format(", ".join(strs[: i + 1]))[:max_len]
else:
return "{{{}}}".format(", ".join(strs))
async def run(server, comm, function, args=(), kwargs=None, is_coro=None, wait=True):
kwargs = kwargs or {}
function = pickle.loads(function)
if is_coro is None:
is_coro = iscoroutinefunction(function)
else:
warnings.warn(
"The is_coro= parameter is deprecated. "
"We now automatically detect coroutines/async functions"
)
assert wait or is_coro, "Combination not supported"
if args:
args = pickle.loads(args)
if kwargs:
kwargs = pickle.loads(kwargs)
if has_arg(function, "dask_worker"):
kwargs["dask_worker"] = server
if has_arg(function, "dask_scheduler"):
kwargs["dask_scheduler"] = server
logger.info("Run out-of-band function %r", funcname(function))
try:
if not is_coro:
result = function(*args, **kwargs)
else:
if wait:
result = await function(*args, **kwargs)
else:
server.loop.add_callback(function, *args, **kwargs)
result = None
except Exception as e:
logger.warning(
"Run Failed\nFunction: %s\nargs: %s\nkwargs: %s\n",
str(funcname(function))[:1000],
convert_args_to_str(args, max_len=1000),
convert_kwargs_to_str(kwargs, max_len=1000),
exc_info=True,
)
response = error_message(e)
else:
response = {"status": "OK", "result": to_serialize(result)}
return response
_global_workers = Worker._instances
try:
if nvml.device_get_count() < 1:
raise RuntimeError
except (Exception, RuntimeError):
pass
else:
async def gpu_metric(worker):
result = await offload(nvml.real_time)
return result
DEFAULT_METRICS["gpu"] = gpu_metric
def gpu_startup(worker):
return nvml.one_time()
DEFAULT_STARTUP_INFORMATION["gpu"] = gpu_startup
def print(*args, **kwargs):
"""Dask print function
This prints both wherever this function is run, and also in the user's
client session
"""
try:
worker = get_worker()
except ValueError:
pass
else:
msg = {
"args": tuple(stringify(arg) for arg in args),
"kwargs": {k: stringify(v) for k, v in kwargs.items()},
}
worker.log_event("print", msg)
builtins.print(*args, **kwargs)
def warn(*args, **kwargs):
"""Dask warn function
This raises a warning both wherever this function is run, and also
in the user's client session
"""
try:
worker = get_worker()
except ValueError:
pass
else:
worker.log_event("warn", {"args": args, "kwargs": kwargs})
warnings.warn(*args, **kwargs)