from __future__ import annotations import asyncio import atexit import copy import inspect import itertools import json import logging import os import pickle import re import sys import threading import traceback import uuid import warnings import weakref from collections import defaultdict from collections.abc import Collection, Coroutine, Iterable, Iterator, Sequence from concurrent.futures import ThreadPoolExecutor from concurrent.futures._base import DoneAndNotDoneFutures from contextlib import asynccontextmanager, contextmanager, suppress from contextvars import ContextVar from functools import partial, singledispatchmethod from importlib.metadata import PackageNotFoundError, version from numbers import Number from queue import Queue as pyQueue from typing import ( TYPE_CHECKING, Any, Callable, ClassVar, Literal, NamedTuple, TypedDict, cast, ) from packaging.version import parse as parse_version from tlz import first, groupby, merge, partition_all, valmap import dask from dask.base import collections_to_dsk from dask.core import flatten, validate_key from dask.highlevelgraph import HighLevelGraph from dask.layers import Layer from dask.optimization import SubgraphCallable from dask.tokenize import tokenize from dask.typing import Key, NestedKeys, NoDefault, no_default from dask.utils import ( ensure_dict, format_bytes, funcname, parse_bytes, parse_timedelta, shorten_traceback, typename, ) from dask.widgets import get_template from distributed.core import OKMessage from distributed.protocol.serialize import _is_dumpable from distributed.utils import Deadline, wait_for try: from dask.delayed import single_key except ImportError: single_key = first from tornado import gen from tornado.ioloop import IOLoop from dask._task_spec import DataNode, GraphNode, List, Task, TaskRef, parse_input import distributed.utils from distributed import cluster_dump, preloading from distributed import versions as version_module from distributed.batched import BatchedSend from distributed.cfexecutor import ClientExecutor from distributed.compatibility import PeriodicCallback from distributed.core import ( CommClosedError, ConnectionPool, PooledRPCCall, Status, clean_exception, connect, rpc, ) from distributed.diagnostics.plugin import ( ForwardLoggingPlugin, NannyPlugin, SchedulerPlugin, SchedulerUploadFile, UploadFile, WorkerPlugin, _get_plugin_name, ) from distributed.metrics import time from distributed.objects import HasWhat, SchedulerInfo, WhoHas from distributed.protocol import to_serialize from distributed.protocol.pickle import dumps, loads from distributed.publish import Datasets from distributed.pubsub import PubSubClientExtension from distributed.security import Security from distributed.sizeof import sizeof from distributed.spans import SpanMetadata from distributed.threadpoolexecutor import rejoin from distributed.utils import ( CancelledError, LoopRunner, NoOpAwaitable, SyncMethodMixin, TimeoutError, format_dashboard_link, has_keyword, import_term, log_errors, nbytes, sync, thread_state, ) from distributed.utils_comm import ( gather_from_workers, pack_data, retry_operation, scatter_to_workers, unpack_remotedata, ) from distributed.worker import get_client, get_worker, secede if TYPE_CHECKING: from typing_extensions import TypeAlias logger = logging.getLogger(__name__) _global_clients: weakref.WeakValueDictionary[int, Client] = ( weakref.WeakValueDictionary() ) _global_client_index = [0] _current_client: ContextVar[Client | None] = ContextVar("_current_client", default=None) DEFAULT_EXTENSIONS = { "pubsub": PubSubClientExtension, } TOPIC_PREFIX_FORWARDED_LOG_RECORD = "forwarded-log-record" class FutureCancelledError(CancelledError): key: str reason: str msg: str | None def __init__(self, key: str, reason: str | None, msg: str | None = None): self.key = key self.reason = reason if reason else "unknown" self.msg = msg def __str__(self) -> str: result = f"{self.key} cancelled for reason: {self.reason}." if self.msg: result = "\n".join([result, self.msg]) return result def __reduce__(self): return self.__class__, (self.key, self.reason, self.msg) class FuturesCancelledError(CancelledError): error_groups: list[CancelledFuturesGroup] def __init__(self, error_groups: list[CancelledFuturesGroup]): self.error_groups = sorted( error_groups, key=lambda group: len(group.errors), reverse=True ) def __str__(self): count = sum(map(lambda group: len(group.errors), self.error_groups)) result = f"{count} Future{'s' if count > 1 else ''} cancelled:" return "\n".join( [result, "Reasons:"] + [str(group) for group in self.error_groups] ) class CancelledFuturesGroup: #: Errors of the cancelled futures errors: list[FutureCancelledError] #: Reason for cancelling the futures reason: str __slots__ = tuple(__annotations__) def __init__(self, errors: list[FutureCancelledError], reason: str): self.errors = errors self.reason = reason def __str__(self): keys = [error.key for error in self.errors] example_message = None for error in self.errors: if error.msg: example_message = error.msg break return ( f"{len(keys)} Future{'s' if len(keys) > 1 else ''} cancelled for reason: " f"{self.reason}.\nMessage: {example_message}\n" f"Future{'s' if len(keys) > 1 else ''}: {keys}" ) class SourceCode(NamedTuple): code: str lineno_frame: int lineno_relative: int filename: str def _get_global_client() -> Client | None: c = _current_client.get() if c: return c L = sorted(list(_global_clients), reverse=True) for k in L: c = _global_clients[k] if c.status != "closed": return c else: del _global_clients[k] return None def _set_global_client(c: Client | None) -> None: if c is not None: c._set_as_default = True _global_clients[_global_client_index[0]] = c _global_client_index[0] += 1 def _del_global_client(c: Client) -> None: for k in list(_global_clients): try: if _global_clients[k] is c: del _global_clients[k] except KeyError: # pragma: no cover pass class Future(TaskRef): """A remotely running computation A Future is a local proxy to a result running on a remote worker. A user manages future objects in the local Python process to determine what happens in the larger cluster. .. note:: Users should not instantiate futures manually. This can lead to state corruption and deadlocking clusters. Parameters ---------- key: str, or tuple Key of remote data to which this future refers client: Client Client that should own this future. Defaults to _get_global_client() inform: bool Do we inform the scheduler that we need an update on this future state: FutureState The state of the future Examples -------- Futures typically emerge from Client computations >>> my_future = client.submit(add, 1, 2) # doctest: +SKIP We can track the progress and results of a future >>> my_future # doctest: +SKIP We can get the result or the exception and traceback from the future >>> my_future.result() # doctest: +SKIP See Also -------- Client: Creates futures """ _is_finalizing: staticmethod[[], bool] = staticmethod(sys.is_finalizing) _cb_executor = None _cb_executor_pid = None _counter = itertools.count() # Make sure this stays unique even across multiple processes or hosts _uid = uuid.uuid4().hex def __init__(self, key, client=None, state=None, _id=None): self.key = key self._cleared = False self._client = client self._id = _id or (Future._uid, next(Future._counter)) self._input_state = state self._state = None self._bind_late() @property def client(self): self._bind_late() return self._client def bind_client(self, client): self._client = client self._bind_late() def _bind_late(self): if self._client and not self._state: self._client._inc_ref(self.key) self._generation = self._client.generation if self.key in self._client.futures: self._state = self._client.futures[self.key] else: self._state = self._client.futures[self.key] = FutureState(self.key) if self._input_state is not None: try: handler = self._client._state_handlers[self._input_state] except KeyError: pass else: handler(key=self.key) def _verify_initialized(self): if not self.client or not self._state: raise RuntimeError( f"{type(self)} object not properly initialized. This can happen" " if the object is being deserialized outside of the context of" " a Client or Worker." ) @property def executor(self): """Returns the executor, which is the client. Returns ------- Client The executor """ return self.client @property def status(self): """Returns the status Returns ------- str The status """ return self._state.status def done(self): """Returns whether or not the computation completed. Returns ------- bool True if the computation is complete, otherwise False """ return self._state.done() def result(self, timeout=None): """Wait until computation completes, gather result to local process. Parameters ---------- timeout : number, optional Time in seconds after which to raise a ``dask.distributed.TimeoutError`` Raises ------ dask.distributed.TimeoutError If *timeout* seconds are elapsed before returning, a ``dask.distributed.TimeoutError`` is raised. Returns ------- result The result of the computation. Or a coroutine if the client is asynchronous. """ self._verify_initialized() with shorten_traceback(): return self.client.sync(self._result, callback_timeout=timeout) async def _result(self, raiseit=True): await self._state.wait() if self.status == "error": exc = clean_exception(self._state.exception, self._state.traceback) if raiseit: typ, exc, tb = exc raise exc.with_traceback(tb) else: return exc elif self.cancelled(): assert self._state exception = self._state.exception assert isinstance(exception, CancelledError) if raiseit: raise exception else: return exception else: result = await self.client._gather([self]) return result[0] async def _exception(self): await self._state.wait() if self.status == "error": return self._state.exception else: return None def exception(self, timeout=None, **kwargs): """Return the exception of a failed task Parameters ---------- timeout : number, optional Time in seconds after which to raise a ``dask.distributed.TimeoutError`` **kwargs : dict Optional keyword arguments for the function Returns ------- Exception The exception that was raised If *timeout* seconds are elapsed before returning, a ``dask.distributed.TimeoutError`` is raised. See Also -------- Future.traceback """ self._verify_initialized() return self.client.sync(self._exception, callback_timeout=timeout, **kwargs) def add_done_callback(self, fn): """Call callback on future when future has finished The callback ``fn`` should take the future as its only argument. This will be called regardless of if the future completes successfully, errs, or is cancelled The callback is executed in a separate thread. Parameters ---------- fn : callable The method or function to be called """ self._verify_initialized() cls = Future if cls._cb_executor is None or cls._cb_executor_pid != os.getpid(): try: cls._cb_executor = ThreadPoolExecutor( 1, thread_name_prefix="Dask-Callback-Thread" ) except TypeError: cls._cb_executor = ThreadPoolExecutor(1) cls._cb_executor_pid = os.getpid() def execute_callback(fut): try: fn(fut) except BaseException: logger.exception("Error in callback %s of %s:", fn, fut) raise self.client.loop.add_callback( done_callback, self, partial(cls._cb_executor.submit, execute_callback) ) def cancel(self, reason=None, msg=None, **kwargs): """Cancel the request to run this future See Also -------- Client.cancel """ self._verify_initialized() return self.client.cancel([self], reason=reason, msg=msg, **kwargs) def retry(self, **kwargs): """Retry this future if it has failed See Also -------- Client.retry """ self._verify_initialized() return self.client.retry([self], **kwargs) def cancelled(self): """Returns True if the future has been cancelled Returns ------- bool True if the future was 'cancelled', otherwise False """ return self._state.status == "cancelled" async def _traceback(self): await self._state.wait() if self.status == "error": return self._state.traceback else: return None def traceback(self, timeout=None, **kwargs): """Return the traceback of a failed task This returns a traceback object. You can inspect this object using the ``traceback`` module. Alternatively if you call ``future.result()`` this traceback will accompany the raised exception. Parameters ---------- timeout : number, optional Time in seconds after which to raise a ``dask.distributed.TimeoutError`` If *timeout* seconds are elapsed before returning, a ``dask.distributed.TimeoutError`` is raised. Examples -------- >>> import traceback # doctest: +SKIP >>> tb = future.traceback() # doctest: +SKIP >>> traceback.format_tb(tb) # doctest: +SKIP [...] Returns ------- traceback The traceback object. Or a coroutine if the client is asynchronous. See Also -------- Future.exception """ self._verify_initialized() return self.client.sync(self._traceback, callback_timeout=timeout, **kwargs) @property def type(self): """Returns the type""" return self._state.type def release(self): """ Notes ----- This method can be called from different threads (see e.g. Client.get() or Future.__del__()) """ self._verify_initialized() if not self._cleared and self.client.generation == self._generation: self._cleared = True try: self.client.loop.add_callback(self.client._dec_ref, self.key) except TypeError: # pragma: no cover pass # Shutting down, add_callback may be None def __reduce__(self) -> str | tuple[Any, ...]: return Future, (self.key,) def __dask_tokenize__(self): return (type(self).__name__, self.key, self._id) def __del__(self): try: self.release() except AttributeError: # Occasionally we see this error when shutting down the client # https://github.com/dask/distributed/issues/4305 if not self._is_finalizing(): raise except RuntimeError: # closed event loop pass def __str__(self): return repr(self) def __repr__(self): if self.type: return ( f"" ) else: return f"" def _repr_html_(self): return get_template("future.html.j2").render( key=str(self.key), type=typename(self.type), status=self.status, ) def __await__(self): return self.result().__await__() def __hash__(self): return hash(self._id) def __eq__(self, other): return self is other class FutureState: """A Future's internal state. This is shared between all Futures with the same key and client. """ __slots__ = ("_event", "key", "status", "type", "exception", "traceback") def __init__(self, key: str): self._event = None self.key = key self.exception = None self.status = "pending" self.traceback = None self.type = None def _get_event(self): # Can't create Event eagerly in constructor as it can fetch # its IOLoop from the wrong thread # (https://github.com/tornadoweb/tornado/issues/2189) event = self._event if event is None: event = self._event = asyncio.Event() return event def cancel(self, reason=None, msg=None): """Cancels the operation""" self.status = "cancelled" self.exception = FutureCancelledError(key=self.key, reason=reason, msg=msg) self._get_event().set() def finish(self, type=None): """Sets the status to 'finished' and sets the event Parameters ---------- type : any The type """ self.status = "finished" self._get_event().set() if type is not None: self.type = type def lose(self): """Sets the status to 'lost' and clears the event""" self.status = "lost" self._get_event().clear() def retry(self): """Sets the status to 'pending' and clears the event""" self.status = "pending" self._get_event().clear() def set_error(self, exception, traceback): """Sets the error data Sets the status to 'error'. Sets the exception, the traceback, and the event Parameters ---------- exception: Exception The exception traceback: Exception The traceback """ _, exception, traceback = clean_exception(exception, traceback) self.status = "error" self.exception = exception self.traceback = traceback self._get_event().set() def done(self): """Returns 'True' if the event is not None and the event is set""" return self._event is not None and self._event.is_set() def reset(self): """Sets the status to 'pending' and clears the event""" self.status = "pending" if self._event is not None: self._event.clear() async def wait(self, timeout=None): """Wait for the awaitable to complete with a timeout. Parameters ---------- timeout : number, optional Time in seconds after which to raise a ``dask.distributed.TimeoutError`` """ await wait_for(self._get_event().wait(), timeout) def __repr__(self): return f"<{self.__class__.__name__}: {self.status}>" async def done_callback(future, callback): """Coroutine that waits on the future, then calls the callback Parameters ---------- future : asyncio.Future The future callback : callable The callback """ while future.status == "pending": await future._state.wait() callback(future) class AllExit(Exception): """Custom exception class to exit All(...) early.""" def _handle_print(event): _, msg = event if not isinstance(msg, dict): # someone must have manually logged a print event with a hand-crafted # payload, rather than by calling worker.print(). In that case simply # print the payload and hope it works. print(msg) return args = msg.get("args") if not isinstance(args, tuple): # worker.print() will always send us a tuple of args, even if it's an # empty tuple. raise TypeError( f"_handle_print: client received non-tuple print args: {args!r}" ) file = msg.get("file") if file == 1: file = sys.stdout elif file == 2: file = sys.stderr elif file is not None: raise TypeError( f"_handle_print: client received unsupported file kwarg: {file!r}" ) print( *args, sep=msg.get("sep"), end=msg.get("end"), file=file, flush=msg.get("flush") ) def _handle_warn(event): _, msg = event if not isinstance(msg, dict): # someone must have manually logged a warn event with a hand-crafted # payload, rather than by calling worker.warn(). In that case simply # warn the payload and hope it works. warnings.warn(msg) else: if "message" not in msg: # TypeError makes sense here because it's analogous to calling a # function without a required positional argument raise TypeError( "_handle_warn: client received a warn event missing the required " '"message" argument.' ) if "category" in msg: category = pickle.loads(msg["category"]) else: category = None warnings.warn( pickle.loads(msg["message"]), category=category, ) def _maybe_call_security_loader(address): security_loader_term = dask.config.get("distributed.client.security-loader") if security_loader_term: try: security_loader = import_term(security_loader_term) except Exception as exc: raise ImportError( f"Failed to import `{security_loader_term}` configured at " f"`distributed.client.security-loader` - is this module " f"installed?" ) from exc return security_loader({"address": address}) return None class VersionsDict(TypedDict): scheduler: dict[str, dict[str, Any]] workers: dict[str, dict[str, dict[str, Any]]] client: dict[str, dict[str, Any]] _T_LowLevelGraph: TypeAlias = dict[Key, GraphNode] def _is_nested(iterable): for item in iterable: if ( isinstance(item, Iterable) and not isinstance(item, str) and not isinstance(item, bytes) ): return True return False class _MapLayer(Layer): func: Callable iterables: Iterable[Any] key: str | Iterable[str] | None pure: bool annotations: dict[str, Any] | None def __init__( self, func: Callable, iterables: Iterable[Any], key: str | Iterable[str] | None = None, pure: bool = True, annotations: dict[str, Any] | None = None, **kwargs, ): self.func: Callable = func self.iterables = [tuple(map(parse_input, iterable)) for iterable in iterables] self.key: str | Iterable[str] | None = key self.pure: bool = pure self.kwargs = {k: parse_input(v) for k, v in kwargs.items()} super().__init__(annotations=annotations) def __repr__(self) -> str: return f"{type(self).__name__} " @property def _dict(self) -> _T_LowLevelGraph: self._cached_dict: _T_LowLevelGraph dsk: _T_LowLevelGraph if hasattr(self, "_cached_dict"): return self._cached_dict else: dsk = self._construct_graph() self._cached_dict = dsk return self._cached_dict @property def _keys(self) -> Iterable[Key]: if hasattr(self, "_cached_keys"): return self._cached_keys else: if isinstance(self.key, Iterable) and not isinstance(self.key, str): self._cached_keys: Iterable[Key] = self.key return self.key else: if self.pure: tok = tokenize(self.func, self.kwargs) keys = [ self.key + "-" + tokenize(tok, args) # type: ignore for args in zip(*self.iterables) ] else: uid = str(uuid.uuid4()) keys = ( [ f"{self.key}-{uid}-{i}" for i in range(min(map(len, self.iterables))) ] if self.iterables else [] ) self._cached_keys = keys return keys def get_output_keys(self) -> set[Key]: return set(self._keys) def get_ordered_keys(self): return list(self._keys) def is_materialized(self) -> bool: return hasattr(self, "_cached_dict") def __getitem__(self, key: Key) -> GraphNode: return self._dict[key] def __iter__(self) -> Iterator[Key]: return iter(self._dict) def __len__(self) -> int: return len(self._dict) def _construct_graph(self) -> _T_LowLevelGraph: dsk: _T_LowLevelGraph = {} if not self.kwargs: dsk = { key: Task(key, self.func, *args) for key, args in zip(self._keys, zip(*self.iterables)) } else: kwargs2 = {} dsk = {} for k, v in self.kwargs.items(): if sizeof(v) > 1e5: vv = DataNode(k, v) kwargs2[k] = vv.ref() dsk[vv.key] = vv else: kwargs2[k] = v dsk.update( { key: Task(key, self.func, *args, **kwargs2) for key, args in zip(self._keys, zip(*self.iterables)) } ) return dsk class Client(SyncMethodMixin): """Connect to and submit computation to a Dask cluster The Client connects users to a Dask cluster. It provides an asynchronous user interface around functions and futures. This class resembles executors in ``concurrent.futures`` but also allows ``Future`` objects within ``submit/map`` calls. When a Client is instantiated it takes over all ``dask.compute`` and ``dask.persist`` calls by default. It is also common to create a Client without specifying the scheduler address , like ``Client()``. In this case the Client creates a :class:`LocalCluster` in the background and connects to that. Any extra keywords are passed from Client to LocalCluster in this case. See the LocalCluster documentation for more information. Parameters ---------- address: string, or Cluster This can be the address of a ``Scheduler`` server like a string ``'127.0.0.1:8786'`` or a cluster object like ``LocalCluster()`` loop The event loop timeout: int (defaults to configuration ``distributed.comm.timeouts.connect``) Timeout duration for initial connection to the scheduler set_as_default: bool (True) Use this Client as the global dask scheduler scheduler_file: string (optional) Path to a file with scheduler information if available security: Security or bool, optional Optional security information. If creating a local cluster can also pass in ``True``, in which case temporary self-signed credentials will be created automatically. asynchronous: bool (False by default) Set to True if using this client within async/await functions or within Tornado gen.coroutines. Otherwise this should remain False for normal use. name: string (optional) Gives the client a name that will be included in logs generated on the scheduler for matters relating to this client heartbeat_interval: int (optional) Time in milliseconds between heartbeats to scheduler serializers Iterable of approaches to use when serializing the object. See :ref:`serialization` for more. deserializers Iterable of approaches to use when deserializing the object. See :ref:`serialization` for more. extensions : list The extensions direct_to_workers: bool (optional) Whether or not to connect directly to the workers, or to ask the scheduler to serve as intermediary. connection_limit : int The number of open comms to maintain at once in the connection pool **kwargs: If you do not pass a scheduler address, Client will create a ``LocalCluster`` object, passing any extra keyword arguments. Examples -------- Provide cluster's scheduler node address on initialization: >>> client = Client('127.0.0.1:8786') # doctest: +SKIP Use ``submit`` method to send individual computations to the cluster >>> a = client.submit(add, 1, 2) # doctest: +SKIP >>> b = client.submit(add, 10, 20) # doctest: +SKIP Continue using submit or map on results to build up larger computations >>> c = client.submit(add, a, b) # doctest: +SKIP Gather results with the ``gather`` method. >>> client.gather(c) # doctest: +SKIP 33 You can also call Client with no arguments in order to create your own local cluster. >>> client = Client() # makes your own local "cluster" # doctest: +SKIP Extra keywords will be passed directly to LocalCluster >>> client = Client(n_workers=2, threads_per_worker=4) # doctest: +SKIP See Also -------- distributed.scheduler.Scheduler: Internal scheduler distributed.LocalCluster: """ _is_finalizing: staticmethod[[], bool] = staticmethod(sys.is_finalizing) _instances: ClassVar[weakref.WeakSet[Client]] = weakref.WeakSet() _default_event_handlers = {"print": _handle_print, "warn": _handle_warn} preloads: preloading.PreloadManager __loop: IOLoop | None = None def __init__( self, address=None, loop=None, timeout=no_default, set_as_default=True, scheduler_file=None, security=None, asynchronous=False, name=None, heartbeat_interval=None, serializers=None, deserializers=None, extensions=DEFAULT_EXTENSIONS, direct_to_workers=None, connection_limit=512, **kwargs, ): if timeout is no_default: timeout = dask.config.get("distributed.comm.timeouts.connect") timeout = parse_timedelta(timeout, "s") if timeout is None: raise ValueError("None is an invalid value for global client timeout") self._timeout = timeout self.futures = dict() self.refcount = defaultdict(int) self._handle_report_task = None if name is None: name = dask.config.get("client-name", None) self.id = ( type(self).__name__ + ("-" + name + "-" if name else "-") + str(uuid.uuid1(clock_seq=os.getpid())) ) self.generation = 0 self.status = "newly-created" self._pending_msg_buffer = [] self.extensions = {} self.scheduler_file = scheduler_file self._startup_kwargs = kwargs self.cluster = None self.scheduler = None self._scheduler_identity = {} # A reentrant-lock on the refcounts for futures associated with this # client. Should be held by individual operations modifying refcounts, # or any bulk operation that needs to ensure the set of futures doesn't # change during operation. self._refcount_lock = threading.RLock() self.datasets = Datasets(self) self._serializers = serializers if deserializers is None: deserializers = serializers self._deserializers = deserializers self.direct_to_workers = direct_to_workers self._previous_as_current = None # Communication self.scheduler_comm = None if address is None: address = dask.config.get("scheduler-address", None) if address: logger.info("Config value `scheduler-address` found: %s", address) if address is not None and kwargs: raise ValueError(f"Unexpected keyword arguments: {sorted(kwargs)}") if isinstance(address, (rpc, PooledRPCCall)): self.scheduler = address elif isinstance(getattr(address, "scheduler_address", None), str): # It's a LocalCluster or LocalCluster-compatible object self.cluster = address status = self.cluster.status if status in (Status.closed, Status.closing): raise RuntimeError( f"Trying to connect to an already closed or closing Cluster {self.cluster}." ) with suppress(AttributeError): loop = address.loop if security is None: security = getattr(self.cluster, "security", None) elif address is not None and not isinstance(address, str): raise TypeError( "Scheduler address must be a string or a Cluster instance, got {}".format( type(address) ) ) # If connecting to an address and no explicit security is configured, attempt # to load security credentials with a security loader (if configured). if security is None and isinstance(address, str): security = _maybe_call_security_loader(address) if security is None: security = Security() elif isinstance(security, dict): security = Security(**security) elif security is True: security = Security.temporary() self._startup_kwargs["security"] = security elif not isinstance(security, Security): # pragma: no cover raise TypeError("security must be a Security object") self.security = security if name == "worker": self.connection_args = self.security.get_connection_args("worker") else: self.connection_args = self.security.get_connection_args("client") self._asynchronous = asynchronous self._loop_runner = LoopRunner(loop=loop, asynchronous=asynchronous) self._connecting_to_scheduler = False self._gather_keys = None self._gather_future = None if heartbeat_interval is None: heartbeat_interval = dask.config.get("distributed.client.heartbeat") heartbeat_interval = parse_timedelta(heartbeat_interval, default="ms") scheduler_info_interval = parse_timedelta( dask.config.get("distributed.client.scheduler-info-interval", default="ms") ) self._periodic_callbacks = dict() self._periodic_callbacks["scheduler-info"] = PeriodicCallback( self._update_scheduler_info, scheduler_info_interval * 1000 ) self._periodic_callbacks["heartbeat"] = PeriodicCallback( self._heartbeat, heartbeat_interval * 1000 ) self._start_arg = address self._set_as_default = set_as_default if set_as_default: self._set_config = dask.config.set(scheduler="dask.distributed") self._event_handlers = {} self._stream_handlers = { "key-in-memory": self._handle_key_in_memory, "lost-data": self._handle_lost_data, "cancelled-keys": self._handle_cancelled_keys, "task-retried": self._handle_retried_key, "task-erred": self._handle_task_erred, "restart": self._handle_restart, "error": self._handle_error, "event": self._handle_event, } self._state_handlers = { "memory": self._handle_key_in_memory, "lost": self._handle_lost_data, "erred": self._handle_task_erred, } self.rpc = ConnectionPool( limit=connection_limit, serializers=serializers, deserializers=deserializers, deserialize=True, connection_args=self.connection_args, timeout=timeout, server=self, ) self.extensions = { name: extension(self) for name, extension in extensions.items() } preload = dask.config.get("distributed.client.preload") preload_argv = dask.config.get("distributed.client.preload-argv") self.preloads = preloading.process_preloads(self, preload, preload_argv) self.start(timeout=timeout) Client._instances.add(self) from distributed.recreate_tasks import ReplayTaskClient ReplayTaskClient(self) @property def io_loop(self) -> IOLoop | None: warnings.warn( "The io_loop property is deprecated", DeprecationWarning, stacklevel=2 ) return self.loop @io_loop.setter def io_loop(self, value: IOLoop) -> None: warnings.warn( "The io_loop property is deprecated", DeprecationWarning, stacklevel=2 ) self.loop = value @property def loop(self) -> IOLoop | None: loop = self.__loop if loop is None: # If the loop is not running when this is called, the LoopRunner.loop # property will raise a DeprecationWarning # However subsequent calls might occur - eg atexit, where a stopped # loop is still acceptable - so we cache access to the loop. self.__loop = loop = self._loop_runner.loop return loop @loop.setter def loop(self, value: IOLoop) -> None: warnings.warn( "setting the loop property is deprecated", DeprecationWarning, stacklevel=2 ) self.__loop = value @contextmanager def as_current(self): """Thread-local, Task-local context manager that causes the Client.current class method to return self. Any Future objects deserialized inside this context manager will be automatically attached to this Client. """ tok = _current_client.set(self) with dask.config.set(scheduler="dask.distributed"): try: yield finally: _current_client.reset(tok) @classmethod def current(cls, allow_global=True): """When running within the context of `as_client`, return the context-local current client. Otherwise, return the latest initialised Client. If no Client instances exist, raise ValueError. If allow_global is set to False, raise ValueError if running outside of the `as_client` context manager. Parameters ---------- allow_global : bool If True returns the default client Returns ------- Client The current client Raises ------ ValueError If there is no client set, a ValueError is raised See also -------- default_client """ out = _current_client.get() if out: return out if allow_global: return default_client() raise ValueError("Not running inside the `as_current` context manager") @property def dashboard_link(self): """Link to the scheduler's dashboard. Returns ------- str Dashboard URL. Examples -------- Opening the dashboard in your default web browser: >>> import webbrowser >>> from distributed import Client >>> client = Client() >>> webbrowser.open(client.dashboard_link) """ try: return self.cluster.dashboard_link except AttributeError: scheduler, info = self._get_scheduler_info() if scheduler is None: return None else: protocol, rest = scheduler.address.split("://") port = info["services"]["dashboard"] if protocol == "inproc": host = "localhost" else: host = rest.split(":")[0] return format_dashboard_link(host, port) def _get_scheduler_info(self): from distributed.scheduler import Scheduler if ( self.cluster and hasattr(self.cluster, "scheduler") and isinstance(self.cluster.scheduler, Scheduler) ): info = self.cluster.scheduler.identity() scheduler = self.cluster.scheduler elif ( self._loop_runner.is_started() and self.scheduler and not self.asynchronous ): info = sync(self.loop, self.scheduler.identity) scheduler = self.scheduler else: info = self._scheduler_identity scheduler = self.scheduler return scheduler, SchedulerInfo(info) def __repr__(self): # Note: avoid doing I/O here... info = self._scheduler_identity addr = info.get("address") if addr: workers = info.get("workers", {}) nworkers = len(workers) nthreads = sum(w["nthreads"] for w in workers.values()) text = "<%s: %r processes=%d threads=%d" % ( self.__class__.__name__, addr, nworkers, nthreads, ) memory = [w["memory_limit"] for w in workers.values()] if all(memory): text += ", memory=" + format_bytes(sum(memory)) text += ">" return text elif self.scheduler is not None: return "<{}: scheduler={!r}>".format( self.__class__.__name__, self.scheduler.address, ) else: return f"<{self.__class__.__name__}: No scheduler connected>" def _repr_html_(self): try: dle_version = parse_version(version("dask-labextension")) JUPYTERLAB = False if dle_version < parse_version("6.0.0") else True except PackageNotFoundError: JUPYTERLAB = False scheduler, info = self._get_scheduler_info() return get_template("client.html.j2").render( id=self.id, scheduler=scheduler, info=info, cluster=self.cluster, scheduler_file=self.scheduler_file, dashboard_link=self.dashboard_link, jupyterlab=JUPYTERLAB, ) def start(self, **kwargs): """Start scheduler running in separate thread""" if self.status != "newly-created": return self._loop_runner.start() if self._set_as_default: _set_global_client(self) if self.asynchronous: self._started = asyncio.ensure_future(self._start(**kwargs)) else: sync(self.loop, self._start, **kwargs) def __await__(self): if hasattr(self, "_started"): return self._started.__await__() else: async def _(): return self return _().__await__() def _send_to_scheduler_safe(self, msg): if self.status in ("running", "closing"): try: self.scheduler_comm.send(msg) except (CommClosedError, AttributeError): if self.status == "running": raise elif self.status in ("connecting", "newly-created"): self._pending_msg_buffer.append(msg) def _send_to_scheduler(self, msg): if self.status in ("running", "closing", "connecting", "newly-created"): self.loop.add_callback(self._send_to_scheduler_safe, msg) else: raise Exception( "Tried sending message after closing. Status: %s\n" "Message: %s" % (self.status, msg) ) async def _start(self, timeout=no_default, **kwargs): self.status = "connecting" await self.rpc.start() if timeout is no_default: timeout = self._timeout timeout = parse_timedelta(timeout, "s") address = self._start_arg if self.cluster is not None: # Ensure the cluster is started (no-op if already running) try: await self.cluster except Exception: logger.info( "Tried to start cluster and received an error. Proceeding.", exc_info=True, ) address = self.cluster.scheduler_address elif self.scheduler_file is not None: while not os.path.exists(self.scheduler_file): await asyncio.sleep(0.01) for _ in range(10): try: with open(self.scheduler_file) as f: cfg = json.load(f) address = cfg["address"] break except (ValueError, KeyError): # JSON file not yet flushed await asyncio.sleep(0.01) elif self._start_arg is None: from distributed.deploy import LocalCluster self.cluster = await LocalCluster( loop=self.loop, asynchronous=self._asynchronous, **self._startup_kwargs, ) address = self.cluster.scheduler_address self._gather_semaphore = asyncio.Semaphore(5) if self.scheduler is None: self.scheduler = self.rpc(address) self.scheduler_comm = None try: await self._ensure_connected(timeout=timeout) except (OSError, ImportError): await self._close() raise for pc in self._periodic_callbacks.values(): pc.start() for topic, handler in Client._default_event_handlers.items(): self.subscribe_topic(topic, handler) await self.preloads.start() self._handle_report_task = asyncio.create_task(self._handle_report()) return self @log_errors async def _reconnect(self): assert self.scheduler_comm.comm.closed() self.status = "connecting" self.scheduler_comm = None for st in self.futures.values(): st.cancel( reason="scheduler-connection-lost", msg=( "Client lost the connection to the scheduler. " "Please check your connection and re-run your work." ), ) self.futures.clear() timeout = self._timeout deadline = time() + timeout while timeout > 0 and self.status == "connecting": try: await self._ensure_connected(timeout=timeout) break except OSError: # Wait a bit before retrying await asyncio.sleep(0.1) timeout = deadline - time() except ImportError: await self._close() break else: logger.error( "Failed to reconnect to scheduler after %.2f " "seconds, closing client", self._timeout, ) await self._close() async def _ensure_connected(self, timeout=None): if ( self.scheduler_comm and not self.scheduler_comm.closed() or self._connecting_to_scheduler or self.scheduler is None ): return self._connecting_to_scheduler = True try: comm = await connect( self.scheduler.address, timeout=timeout, **self.connection_args ) comm.name = "Client->Scheduler" if timeout is not None: await wait_for(self._update_scheduler_info(), timeout) else: await self._update_scheduler_info() await comm.write( { "op": "register-client", "client": self.id, "reply": False, "versions": version_module.get_versions(), } ) except Exception: if self.status == "closed": return else: raise finally: self._connecting_to_scheduler = False if timeout is not None: msg = await wait_for(comm.read(), timeout) else: msg = await comm.read() assert len(msg) == 1 assert msg[0]["op"] == "stream-start" if msg[0].get("error"): raise ImportError(msg[0]["error"]) if msg[0].get("warning"): warnings.warn(version_module.VersionMismatchWarning(msg[0]["warning"])) bcomm = BatchedSend(interval="10ms", loop=self.loop) bcomm.start(comm) self.scheduler_comm = bcomm self.status = "running" for msg in self._pending_msg_buffer: self._send_to_scheduler(msg) del self._pending_msg_buffer[:] logger.debug("Started scheduling coroutines. Synchronized") async def _update_scheduler_info(self): if self.status not in ("running", "connecting") or self.scheduler is None: return try: self._scheduler_identity = SchedulerInfo(await self.scheduler.identity()) except OSError: logger.debug("Not able to query scheduler for identity") async def _wait_for_workers( self, n_workers: int, timeout: float | None = None ) -> None: info = await self.scheduler.identity() self._scheduler_identity = SchedulerInfo(info) if timeout: deadline = time() + parse_timedelta(timeout) else: deadline = None def running_workers(info): return len( [ ws for ws in info["workers"].values() if ws["status"] == Status.running.name ] ) while running_workers(info) < n_workers: if deadline and time() > deadline: raise TimeoutError( "Only %d/%d workers arrived after %s" % (running_workers(info), n_workers, timeout) ) await asyncio.sleep(0.1) info = await self.scheduler.identity() self._scheduler_identity = SchedulerInfo(info) def wait_for_workers(self, n_workers: int, timeout: float | None = None) -> None: """Blocking call to wait for n workers before continuing Parameters ---------- n_workers : int The number of workers timeout : number, optional Time in seconds after which to raise a ``dask.distributed.TimeoutError`` """ if not isinstance(n_workers, int) or n_workers < 1: raise ValueError( f"`n_workers` must be a positive integer. Instead got {n_workers}." ) if self.cluster and hasattr(self.cluster, "wait_for_workers"): return self.cluster.wait_for_workers(n_workers, timeout) return self.sync(self._wait_for_workers, n_workers, timeout=timeout) def _heartbeat(self): # Don't send heartbeat if scheduler comm or cluster are already closed if self.scheduler_comm is not None and not ( self.scheduler_comm.comm.closed() or (self.cluster and self.cluster.status in (Status.closed, Status.closing)) ): self.scheduler_comm.send({"op": "heartbeat-client"}) def __enter__(self): if not self._loop_runner.is_started(): self.start() if self._set_as_default: self._previous_as_current = _current_client.set(self) return self async def __aenter__(self): await self if self._set_as_default: self._previous_as_current = _current_client.set(self) return self async def __aexit__(self, exc_type, exc_value, traceback): if self._previous_as_current: try: _current_client.reset(self._previous_as_current) except ValueError as e: if not e.args[0].endswith(" was created in a different Context"): raise # pragma: nocover warnings.warn( "It is deprecated to enter and exit the Client context " "manager from different tasks", DeprecationWarning, stacklevel=2, ) await self._close( # if we're handling an exception, we assume that it's more # important to deliver that exception than shutdown gracefully. fast=(exc_type is not None) ) def __exit__(self, exc_type, exc_value, traceback): if self._previous_as_current: try: _current_client.reset(self._previous_as_current) except ValueError as e: if not e.args[0].endswith(" was created in a different Context"): raise # pragma: nocover warnings.warn( "It is deprecated to enter and exit the Client context " "manager from different threads", DeprecationWarning, stacklevel=2, ) self.close() def __del__(self): # If the loop never got assigned, we failed early in the constructor, # nothing to do if self.__loop is not None: self.close() def _inc_ref(self, key): with self._refcount_lock: self.refcount[key] += 1 def _dec_ref(self, key): with self._refcount_lock: self.refcount[key] -= 1 if self.refcount[key] == 0: del self.refcount[key] self._release_key(key) def _release_key(self, key): """Release key from distributed memory""" logger.debug("Release key %s", key) st = self.futures.pop(key, None) if st is not None: st.cancel() if self.status != "closed": self._send_to_scheduler( {"op": "client-releases-keys", "keys": [key], "client": self.id} ) @log_errors async def _handle_report(self): """Listen to scheduler""" try: while True: if self.scheduler_comm is None: break try: msgs = await self.scheduler_comm.comm.read() except CommClosedError: if self._is_finalizing(): return if self.status == "running": if self.cluster and self.cluster.status in ( Status.closed, Status.closing, ): # Don't attempt to reconnect if cluster are already closed. # Instead close down the client. await self._close() return logger.info("Client report stream closed to scheduler") logger.info("Reconnecting...") self.status = "connecting" await self._reconnect() continue else: break if not isinstance(msgs, (list, tuple)): msgs = (msgs,) breakout = False for msg in msgs: logger.debug("Client %s receives message %s", self.id, msg) if "status" in msg and "error" in msg["status"]: typ, exc, tb = clean_exception(**msg) raise exc.with_traceback(tb) op = msg.pop("op") if op == "close" or op == "stream-closed": breakout = True break try: handler = self._stream_handlers[op] result = handler(**msg) if inspect.isawaitable(result): await result except Exception as e: logger.exception(e) if breakout: break except (CancelledError, asyncio.CancelledError): pass def _handle_key_in_memory(self, key=None, type=None, workers=None): state = self.futures.get(key) if state is not None: if type and not state.type: # Type exists and not yet set try: type = loads(type) except Exception: type = None # Here, `type` may be a str if actual type failed # serializing in Worker else: type = None state.finish(type) def _handle_lost_data(self, key=None): state = self.futures.get(key) if state is not None: state.lose() def _handle_cancelled_keys(self, keys): for key in keys: state = self.futures.get(key) if state is not None: state.cancel() def _handle_retried_key(self, key=None): state = self.futures.get(key) if state is not None: state.retry() def _handle_task_erred(self, key=None, exception=None, traceback=None): state = self.futures.get(key) if state is not None: state.set_error(exception, traceback) def _handle_restart(self): logger.info("Receive restart signal from scheduler") for state in self.futures.values(): state.cancel( reason="scheduler-restart", msg="Scheduler has restarted. Please re-run your work.", ) self.futures.clear() self.generation += 1 with self._refcount_lock: self.refcount.clear() def _handle_error(self, exception=None): logger.warning("Scheduler exception:") logger.exception(exception) @asynccontextmanager async def _wait_for_handle_report_task(self, fast=False): current_task = asyncio.current_task() handle_report_task = self._handle_report_task # Give the scheduler 'stream-closed' message 100ms to come through # This makes the shutdown slightly smoother and quieter should_wait = ( handle_report_task is not None and handle_report_task is not current_task ) if should_wait: with suppress(asyncio.CancelledError, TimeoutError): await wait_for(asyncio.shield(handle_report_task), 0.1) yield if should_wait: with suppress(TimeoutError, asyncio.CancelledError): await wait_for(handle_report_task, 0 if fast else 2) @log_errors async def _close(self, fast: bool = False) -> None: """Send close signal and wait until scheduler completes If fast is True, the client will close forcefully, by cancelling tasks the background _handle_report_task. """ # TODO: close more forcefully by aborting the RPC and cancelling all # background tasks. # See https://trio.readthedocs.io/en/stable/reference-io.html#trio.aclose_forcefully if self.status == "closed": return self.status = "closing" await self.preloads.teardown() with suppress(AttributeError): for pc in self._periodic_callbacks.values(): pc.stop() _del_global_client(self) self._scheduler_identity = {} if self._set_as_default and not _get_global_client(): with suppress(AttributeError): # clear the dask.config set keys with self._set_config: pass if self.get == dask.config.get("get", None): del dask.config.config["get"] if ( self.scheduler_comm and self.scheduler_comm.comm and not self.scheduler_comm.comm.closed() ): self._send_to_scheduler({"op": "close-client"}) self._send_to_scheduler({"op": "close-stream"}) async with self._wait_for_handle_report_task(fast=fast): if ( self.scheduler_comm and self.scheduler_comm.comm and not self.scheduler_comm.comm.closed() ): await self.scheduler_comm.close() for key in list(self.futures): self._release_key(key=key) if self._start_arg is None: with suppress(AttributeError): await self.cluster.close() await self.rpc.close() self.status = "closed" if _get_global_client() is self: _set_global_client(None) with suppress(AttributeError): await self.scheduler.close_rpc() self.scheduler = None self.status = "closed" def close(self, timeout=no_default): """Close this client Clients will also close automatically when your Python session ends If you started a client without arguments like ``Client()`` then this will also close the local cluster that was started at the same time. Parameters ---------- timeout : number Time in seconds after which to raise a ``dask.distributed.TimeoutError`` See Also -------- Client.restart """ if timeout is no_default: timeout = self._timeout * 2 # XXX handling of self.status here is not thread-safe if self.status in ["closed", "newly-created"]: if self.asynchronous: return NoOpAwaitable() return self.status = "closing" with suppress(AttributeError): for pc in self._periodic_callbacks.values(): pc.stop() if self.asynchronous: coro = self._close() if timeout: coro = wait_for(coro, timeout) return coro sync(self.loop, self._close, fast=True, callback_timeout=timeout) assert self.status == "closed" if not self._is_finalizing(): self._loop_runner.stop() async def _shutdown(self): logger.info("Shutting down scheduler from Client") self.status = "closing" for pc in self._periodic_callbacks.values(): pc.stop() async with self._wait_for_handle_report_task(): if self.cluster: await self.cluster.close() else: with suppress(CommClosedError): await self.scheduler.terminate() await self._close() def shutdown(self): """Shut down the connected scheduler and workers Note, this may disrupt other clients that may be using the same scheduler and workers. See Also -------- Client.close : close only this client """ return self.sync(self._shutdown) def get_executor(self, **kwargs): """ Return a concurrent.futures Executor for submitting tasks on this Client Parameters ---------- **kwargs Any submit()- or map()- compatible arguments, such as `workers` or `resources`. Returns ------- ClientExecutor An Executor object that's fully compatible with the concurrent.futures API. """ return ClientExecutor(self, **kwargs) def submit( self, func, *args, key=None, workers=None, resources=None, retries=None, priority=0, fifo_timeout="100 ms", allow_other_workers=False, actor=False, actors=False, pure=True, **kwargs, ): """Submit a function application to the scheduler Parameters ---------- func : callable Callable to be scheduled as ``func(*args **kwargs)``. If ``func`` returns a coroutine, it will be run on the main event loop of a worker. Otherwise ``func`` will be run in a worker's task executor pool (see ``Worker.executors`` for more information.) *args : tuple Optional positional arguments key : str Unique identifier for the task. Defaults to function-name and hash workers : string or iterable of strings A set of worker addresses or hostnames on which computations may be performed. Leave empty to default to all workers (common case) resources : dict (defaults to {}) Defines the ``resources`` each instance of this mapped task requires on the worker; e.g. ``{'GPU': 2}``. See :doc:`worker resources ` for details on defining resources. retries : int (default to 0) Number of allowed automatic retries if the task fails priority : Number Optional prioritization of task. Zero is default. Higher priorities take precedence fifo_timeout : str timedelta (default '100ms') Allowed amount of time between calls to consider the same priority allow_other_workers : bool (defaults to False) Used with ``workers``. Indicates whether or not the computations may be performed on workers that are not in the `workers` set(s). actor : bool (default False) Whether this task should exist on the worker as a stateful actor. See :doc:`actors` for additional details. actors : bool (default False) Alias for `actor` pure : bool (defaults to True) Whether or not the function is pure. Set ``pure=False`` for impure functions like ``np.random.random``. Note that if both ``actor`` and ``pure`` kwargs are set to True, then the value of ``pure`` will be reverted to False, since an actor is stateful. See :ref:`pure functions` for more details. **kwargs Examples -------- >>> c = client.submit(add, a, b) # doctest: +SKIP Notes ----- The current implementation of a task graph resolution searches for occurrences of ``key`` and replaces it with a corresponding ``Future`` result. That can lead to unwanted substitution of strings passed as arguments to a task if these strings match some ``key`` that already exists on a cluster. To avoid these situations it is required to use unique values if a ``key`` is set manually. See https://github.com/dask/dask/issues/9969 to track progress on resolving this issue. Returns ------- Future If running in asynchronous mode, returns the future. Otherwise returns the concrete value Raises ------ TypeError If 'func' is not callable, a TypeError is raised ValueError If 'allow_other_workers'is True and 'workers' is None, a ValueError is raised See Also -------- Client.map : Submit on many arguments at once """ if not callable(func): raise TypeError("First input to submit must be a callable function") actor = actor or actors if actor: pure = not actor if allow_other_workers not in (True, False, None): raise TypeError("allow_other_workers= must be True or False") if key is None: if pure: key = funcname(func) + "-" + tokenize(func, kwargs, *args) else: key = funcname(func) + "-" + str(uuid.uuid4()) with self._refcount_lock: if key in self.futures: return Future(key, self) if allow_other_workers and workers is None: raise ValueError("Only use allow_other_workers= if using workers=") if isinstance(workers, (str, Number)): workers = [workers] dsk = { key: Task( key, func, *(parse_input(a) for a in args), **{k: parse_input(v) for k, v in kwargs.items()}, ) } futures = self._graph_to_futures( dsk, [key], workers=workers, allow_other_workers=allow_other_workers, internal_priority={key: 0}, user_priority=priority, resources=resources, retries=retries, fifo_timeout=fifo_timeout, actors=actor, span_metadata=SpanMetadata(collections=[{"type": "Future"}]), ) logger.debug("Submit %s(...), %s", funcname(func), key) return futures[key] def map( self, func: Callable, *iterables: Collection, key: str | list | None = None, workers: str | Iterable[str] | None = None, retries: int | None = None, resources: dict[str, Any] | None = None, priority: int = 0, allow_other_workers: bool = False, fifo_timeout: str = "100 ms", actor: bool = False, actors: bool = False, pure: bool = True, batch_size=None, **kwargs, ): """Map a function on a sequence of arguments Arguments can be normal objects or Futures Parameters ---------- func : callable Callable to be scheduled for execution. If ``func`` returns a coroutine, it will be run on the main event loop of a worker. Otherwise ``func`` will be run in a worker's task executor pool (see ``Worker.executors`` for more information.) iterables : Iterables List-like objects to map over. They should have the same length. key : str, list Prefix for task names if string. Explicit names if list. workers : string or iterable of strings A set of worker hostnames on which computations may be performed. Leave empty to default to all workers (common case) retries : int (default to 0) Number of allowed automatic retries if a task fails resources : dict (defaults to {}) Defines the `resources` each instance of this mapped task requires on the worker; e.g. ``{'GPU': 2}``. See :doc:`worker resources ` for details on defining resources. priority : Number Optional prioritization of task. Zero is default. Higher priorities take precedence allow_other_workers : bool (defaults to False) Used with `workers`. Indicates whether or not the computations may be performed on workers that are not in the `workers` set(s). fifo_timeout : str timedelta (default '100ms') Allowed amount of time between calls to consider the same priority actor : bool (default False) Whether these tasks should exist on the worker as stateful actors. See :doc:`actors` for additional details. actors : bool (default False) Alias for `actor` pure : bool (defaults to True) Whether or not the function is pure. Set ``pure=False`` for impure functions like ``np.random.random``. Note that if both ``actor`` and ``pure`` kwargs are set to True, then the value of ``pure`` will be reverted to False, since an actor is stateful. See :ref:`pure functions` for more details. batch_size : int, optional (default: just one batch whose size is the entire iterable) Submit tasks to the scheduler in batches of (at most) ``batch_size``. The tradeoff in batch size is that large batches avoid more per-batch overhead, but batches that are too big can take a long time to submit and unreasonably delay the cluster from starting its processing. **kwargs : dict Extra keyword arguments to send to the function. Large values will be included explicitly in the task graph. Examples -------- >>> L = client.map(func, sequence) # doctest: +SKIP Notes ----- The current implementation of a task graph resolution searches for occurrences of ``key`` and replaces it with a corresponding ``Future`` result. That can lead to unwanted substitution of strings passed as arguments to a task if these strings match some ``key`` that already exists on a cluster. To avoid these situations it is required to use unique values if a ``key`` is set manually. See https://github.com/dask/dask/issues/9969 to track progress on resolving this issue. Returns ------- List, iterator, or Queue of futures, depending on the type of the inputs. See Also -------- Client.submit : Submit a single function """ if not callable(func): raise TypeError("First input to map must be a callable function") if all(isinstance(it, pyQueue) for it in iterables) or all( isinstance(i, Iterator) for i in iterables ): raise TypeError( "Dask no longer supports mapping over Iterators or Queues." "Consider using a normal for loop and Client.submit" ) total_length = sum(len(x) for x in iterables) if batch_size and batch_size > 1 and total_length > batch_size: batches = list( zip(*(partition_all(batch_size, iterable) for iterable in iterables)) ) keys: list[list[Any]] | list[Any] if isinstance(key, list): keys = [list(element) for element in partition_all(batch_size, key)] else: keys = [key for _ in range(len(batches))] return sum( ( self.map( func, *batch, key=key, workers=workers, retries=retries, priority=priority, allow_other_workers=allow_other_workers, fifo_timeout=fifo_timeout, resources=resources, actor=actor, actors=actors, pure=pure, **kwargs, ) for key, batch in zip(keys, batches) ), [], ) key = key or funcname(func) actor = actor or actors if actor: pure = not actor if allow_other_workers and workers is None: raise ValueError("Only use allow_other_workers= if using workers=") dsk = _MapLayer( func, iterables, key=key, pure=pure, **kwargs, ) keys = dsk.get_ordered_keys() if isinstance(workers, (str, Number)): workers = [workers] if workers is not None and not isinstance(workers, (list, set)): raise TypeError("Workers must be a list or set of workers or None") internal_priority = dict(zip(keys, range(len(keys)))) futures = self._graph_to_futures( dsk, keys, workers=workers, allow_other_workers=allow_other_workers, internal_priority=internal_priority, resources=resources, retries=retries, user_priority=priority, fifo_timeout=fifo_timeout, actors=actor, span_metadata=SpanMetadata(collections=[{"type": "Future"}]), ) # make sure the graph is not materialized assert not dsk.is_materialized(), "Graph must be non-materialized" logger.debug("map(%s, ...)", funcname(func)) return [futures[k] for k in keys] async def _gather(self, futures, errors="raise", direct=None, local_worker=None): unpacked, future_set = unpack_remotedata(futures, byte_keys=True) mismatched_futures = [f for f in future_set if f.client is not self] if mismatched_futures: raise ValueError( "Cannot gather Futures created by another client. " f"These are the {len(mismatched_futures)} (out of {len(futures)}) " f"mismatched Futures and their client IDs (this client is {self.id}): " f"{ {f: f.client.id for f in mismatched_futures} }" # noqa: E201, E202 ) keys = [future.key for future in future_set] bad_data = dict() data = {} if direct is None: direct = self.direct_to_workers if direct is None: try: w = get_worker() except Exception: direct = False else: if w.scheduler.address == self.scheduler.address: direct = True async def wait(k): """Want to stop the All(...) early if we find an error""" try: st = self.futures[k] except KeyError: raise AllExit() else: await st.wait() if st.status != "finished" and errors == "raise": raise AllExit() while True: logger.debug("Waiting on futures to clear before gather") with suppress(AllExit): await distributed.utils.All( [wait(key) for key in keys if key in self.futures], quiet_exceptions=AllExit, ) failed = ("error", "cancelled") exceptions = set() bad_keys = set() for future in future_set: key = future.key if key not in self.futures or future.status in failed: exceptions.add(key) if errors == "raise": st = future._state exception = st.exception traceback = st.traceback raise exception.with_traceback(traceback) if errors == "skip": bad_keys.add(key) bad_data[key] = None else: # pragma: no cover raise ValueError("Bad value, `errors=%s`" % errors) keys = [k for k in keys if k not in bad_keys and k not in data] if local_worker: # look inside local worker data.update( {k: local_worker.data[k] for k in keys if k in local_worker.data} ) keys = [k for k in keys if k not in data] # We now do an actual remote communication with workers or scheduler if self._gather_future: # attach onto another pending gather request self._gather_keys |= set(keys) response = await self._gather_future else: # no one waiting, go ahead self._gather_keys = set(keys) future = asyncio.ensure_future( self._gather_remote(direct, local_worker) ) if self._gather_keys is None: self._gather_future = None else: self._gather_future = future response = await future if response["status"] == "error": log = logger.warning if errors == "raise" else logger.debug log( "Couldn't gather %s keys, rescheduling %s", len(response["keys"]), response["keys"], ) for key in response["keys"]: self._send_to_scheduler({"op": "report-key", "key": key}) for key in response["keys"]: try: self.futures[key].reset() except KeyError: # TODO: verify that this is safe pass else: # pragma: no cover break if bad_data and errors == "skip" and isinstance(unpacked, list): unpacked = [f for f in unpacked if f not in bad_data] data.update(response["data"]) result = pack_data(unpacked, merge(data, bad_data)) return result async def _gather_remote(self, direct: bool, local_worker: bool) -> dict[str, Any]: """Perform gather with workers or scheduler This method exists to limit and batch many concurrent gathers into a few. In controls access using a Tornado semaphore, and picks up keys from other requests made recently. """ async with self._gather_semaphore: keys = list(self._gather_keys) self._gather_keys = None # clear state, these keys are being sent off self._gather_future = None if direct or local_worker: # gather directly from workers who_has = await retry_operation(self.scheduler.who_has, keys=keys) data, missing_keys, failed_keys, _ = await gather_from_workers( who_has, rpc=self.rpc ) response: dict[str, Any] = {"status": "OK", "data": data} if missing_keys or failed_keys: response = await retry_operation( self.scheduler.gather, keys=missing_keys + failed_keys ) if response["status"] == "OK": response["data"].update(data) else: # ask scheduler to gather data for us response = await retry_operation(self.scheduler.gather, keys=keys) return response def gather(self, futures, errors="raise", direct=None, asynchronous=None): """Gather futures from distributed memory Accepts a future, nested container of futures, iterator, or queue. The return type will match the input type. Parameters ---------- futures : Collection of futures This can be a possibly nested collection of Future objects. Collections can be lists, sets, or dictionaries errors : string Either 'raise' or 'skip' if we should raise if a future has erred or skip its inclusion in the output collection direct : boolean Whether or not to connect directly to the workers, or to ask the scheduler to serve as intermediary. This can also be set when creating the Client. asynchronous: bool If True the client is in asynchronous mode Returns ------- results: a collection of the same type as the input, but now with gathered results rather than futures Examples -------- >>> from operator import add # doctest: +SKIP >>> c = Client('127.0.0.1:8787') # doctest: +SKIP >>> x = c.submit(add, 1, 2) # doctest: +SKIP >>> c.gather(x) # doctest: +SKIP 3 >>> c.gather([x, [x], x]) # support lists and dicts # doctest: +SKIP [3, [3], 3] See Also -------- Client.scatter : Send data out to cluster """ if isinstance(futures, pyQueue): raise TypeError( "Dask no longer supports gathering over Iterators and Queues. " "Consider using a normal for loop and Client.submit/gather" ) if isinstance(futures, Iterator): return (self.gather(f, errors=errors, direct=direct) for f in futures) try: local_worker = get_worker() except ValueError: local_worker = None with shorten_traceback(): return self.sync( self._gather, futures, errors=errors, direct=direct, local_worker=local_worker, asynchronous=asynchronous, ) async def _scatter( self, data, workers=None, broadcast=False, direct=None, local_worker=None, timeout=no_default, hash=True, ): if timeout is no_default: timeout = self._timeout if isinstance(workers, (str, Number)): workers = [workers] if isinstance(data, type(range(0))): data = list(data) input_type = type(data) names = False unpack = False if isinstance(data, Iterator): data = list(data) if isinstance(data, (set, frozenset)): data = list(data) if not isinstance(data, (dict, list, tuple, set, frozenset)): unpack = True data = [data] if isinstance(data, (list, tuple)): if hash: names = [type(x).__name__ + "-" + tokenize(x) for x in data] else: names = [type(x).__name__ + "-" + uuid.uuid4().hex for x in data] data = dict(zip(names, data)) assert isinstance(data, dict) types = valmap(type, data) if direct is None: direct = self.direct_to_workers if direct is None: try: w = get_worker() except Exception: direct = False else: if w.scheduler.address == self.scheduler.address: direct = True if local_worker: # running within task local_worker.update_data(data=data) await self.scheduler.update_data( who_has={key: [local_worker.address] for key in data}, nbytes=valmap(sizeof, data), client=self.id, ) else: data2 = valmap(to_serialize, data) if direct: nthreads = None start = time() while not nthreads: if nthreads is not None: await asyncio.sleep(0.1) if time() > start + timeout: raise TimeoutError("No valid workers found") # Exclude paused and closing_gracefully workers nthreads = await self.scheduler.ncores_running(workers=workers) if not nthreads: # pragma: no cover raise ValueError("No valid workers found") workers = list(nthreads.keys()) _, who_has, nbytes = await scatter_to_workers(workers, data2, self.rpc) await self.scheduler.update_data( who_has=who_has, nbytes=nbytes, client=self.id ) else: await self.scheduler.scatter( data=data2, workers=workers, client=self.id, broadcast=broadcast, timeout=timeout, ) out = {k: Future(k, self) for k in data} for key, typ in types.items(): self.futures[key].finish(type=typ) if direct and broadcast: n = None if broadcast is True else broadcast await self._replicate(list(out.values()), workers=workers, n=n) if issubclass(input_type, (list, tuple, set, frozenset)): out = input_type(out[k] for k in names) if unpack: assert len(out) == 1 out = list(out.values())[0] return out def scatter( self, data, workers=None, broadcast=False, direct=None, hash=True, timeout=no_default, asynchronous=None, ): """Scatter data into distributed memory This moves data from the local client process into the workers of the distributed scheduler. Note that it is often better to submit jobs to your workers to have them load the data rather than loading data locally and then scattering it out to them. Parameters ---------- data : list, dict, or object Data to scatter out to workers. Output type matches input type. workers : list of tuples (optional) Optionally constrain locations of data. Specify workers as hostname/port pairs, e.g. ``('127.0.0.1', 8787)``. broadcast : bool (defaults to False) Whether to send each data element to all workers. By default we round-robin based on number of cores. .. note:: Setting this flag to True is incompatible with the Active Memory Manager's :ref:`ReduceReplicas` policy. If you wish to use it, you must first disable the policy or disable the AMM entirely. direct : bool (defaults to automatically check) Whether or not to connect directly to the workers, or to ask the scheduler to serve as intermediary. This can also be set when creating the Client. hash : bool (optional) Whether or not to hash data to determine key. If False then this uses a random key timeout : number, optional Time in seconds after which to raise a ``dask.distributed.TimeoutError`` asynchronous: bool If True the client is in asynchronous mode Returns ------- List, dict, iterator, or queue of futures matching the type of input. Examples -------- >>> c = Client('127.0.0.1:8787') # doctest: +SKIP >>> c.scatter(1) # doctest: +SKIP >>> c.scatter([1, 2, 3]) # doctest: +SKIP [, , ] >>> c.scatter({'x': 1, 'y': 2, 'z': 3}) # doctest: +SKIP {'x': , 'y': , 'z': } Constrain location of data to subset of workers >>> c.scatter([1, 2, 3], workers=[('hostname', 8788)]) # doctest: +SKIP Broadcast data to all workers >>> [future] = c.scatter([element], broadcast=True) # doctest: +SKIP Send scattered data to parallelized function using client futures interface >>> data = c.scatter(data, broadcast=True) # doctest: +SKIP >>> res = [c.submit(func, data, i) for i in range(100)] Notes ----- Scattering a dictionary uses ``dict`` keys to create ``Future`` keys. The current implementation of a task graph resolution searches for occurrences of ``key`` and replaces it with a corresponding ``Future`` result. That can lead to unwanted substitution of strings passed as arguments to a task if these strings match some ``key`` that already exists on a cluster. To avoid these situations it is required to use unique values if a ``key`` is set manually. See https://github.com/dask/dask/issues/9969 to track progress on resolving this issue. See Also -------- Client.gather : Gather data back to local process """ if timeout is no_default: timeout = self._timeout if isinstance(data, pyQueue) or isinstance(data, Iterator): raise TypeError( "Dask no longer supports mapping over Iterators or Queues." "Consider using a normal for loop and Client.submit" ) try: local_worker = get_worker() except ValueError: local_worker = None return self.sync( self._scatter, data, workers=workers, broadcast=broadcast, direct=direct, local_worker=local_worker, timeout=timeout, asynchronous=asynchronous, hash=hash, ) async def _cancel(self, futures, reason=None, msg=None, force=False): # FIXME: This method is asynchronous since interacting with the FutureState below requires an event loop. keys = list({f.key for f in futures_of(futures)}) self._send_to_scheduler({"op": "cancel-keys", "keys": keys, "force": force}) for k in keys: st = self.futures.pop(k, None) if st is not None: st.cancel(reason=reason, msg=msg) def cancel(self, futures, asynchronous=None, force=False, reason=None, msg=None): """ Cancel running futures This stops future tasks from being scheduled if they have not yet run and deletes them if they have already run. After calling, this result and all dependent results will no longer be accessible Parameters ---------- futures : List[Future] The list of Futures asynchronous: bool If True the client is in asynchronous mode force : boolean (False) Cancel this future even if other clients desire it reason: str Reason for cancelling the futures msg : str Message that will be attached to the cancelled future """ return self.sync( self._cancel, futures, asynchronous=asynchronous, force=force, msg=msg ) async def _retry(self, futures): keys = list({f.key for f in futures_of(futures)}) response = await self.scheduler.retry(keys=keys, client=self.id) for key in response: st = self.futures[key] st.retry() def retry(self, futures, asynchronous=None): """ Retry failed futures Parameters ---------- futures : list of Futures The list of Futures asynchronous: bool If True the client is in asynchronous mode """ return self.sync(self._retry, futures, asynchronous=asynchronous) @log_errors async def _publish_dataset(self, *args, name=None, override=False, **kwargs): coroutines = [] uid = uuid.uuid4().hex self._send_to_scheduler({"op": "publish_flush_batched_send", "uid": uid}) def add_coro(name, data): keys = [f.key for f in futures_of(data)] async def _(): await self.scheduler.publish_wait_flush(uid=uid) await self.scheduler.publish_put( keys=keys, name=name, data=to_serialize(data), override=override, client=self.id, ) coroutines.append(_()) if name: if len(args) == 0: raise ValueError( "If name is provided, expecting call signature like" " publish_dataset(df, name='ds')" ) # in case this is a singleton, collapse it elif len(args) == 1: args = args[0] add_coro(name, args) for name, data in kwargs.items(): add_coro(name, data) await asyncio.gather(*coroutines) def publish_dataset(self, *args, **kwargs): """ Publish named datasets to scheduler This stores a named reference to a dask collection or list of futures on the scheduler. These references are available to other Clients which can download the collection or futures with ``get_dataset``. Datasets are not immediately computed. You may wish to call ``Client.persist`` prior to publishing a dataset. Parameters ---------- args : list of objects to publish as name kwargs : dict named collections to publish on the scheduler Examples -------- Publishing client: >>> df = dd.read_csv('s3://...') # doctest: +SKIP >>> df = c.persist(df) # doctest: +SKIP >>> c.publish_dataset(my_dataset=df) # doctest: +SKIP Alternative invocation >>> c.publish_dataset(df, name='my_dataset') Receiving client: >>> c.list_datasets() # doctest: +SKIP ['my_dataset'] >>> df2 = c.get_dataset('my_dataset') # doctest: +SKIP Returns ------- None See Also -------- Client.list_datasets Client.get_dataset Client.unpublish_dataset Client.persist """ return self.sync(self._publish_dataset, *args, **kwargs) def unpublish_dataset(self, name, **kwargs): """ Remove named datasets from scheduler Parameters ---------- name : str The name of the dataset to unpublish Examples -------- >>> c.list_datasets() # doctest: +SKIP ['my_dataset'] >>> c.unpublish_dataset('my_dataset') # doctest: +SKIP >>> c.list_datasets() # doctest: +SKIP [] See Also -------- Client.publish_dataset """ return self.sync(self.scheduler.publish_delete, name=name, **kwargs) def list_datasets(self, **kwargs): """ List named datasets available on the scheduler See Also -------- Client.publish_dataset Client.get_dataset """ return self.sync(self.scheduler.publish_list, **kwargs) async def _get_dataset(self, name, default=no_default): with self.as_current(): out = await self.scheduler.publish_get(name=name, client=self.id) if out is None: if default is no_default: raise KeyError(f"Dataset '{name}' not found") else: return default for fut in futures_of(out["data"]): fut.bind_client(self) self._inform_scheduler_of_futures() return out["data"] def get_dataset(self, name, default=no_default, **kwargs): """ Get named dataset from the scheduler if present. Return the default or raise a KeyError if not present. Parameters ---------- name : str name of the dataset to retrieve default : str optional, not set by default If set, do not raise a KeyError if the name is not present but return this default kwargs : dict additional keyword arguments to _get_dataset Returns ------- The dataset from the scheduler, if present See Also -------- Client.publish_dataset Client.list_datasets """ return self.sync(self._get_dataset, name, default=default, **kwargs) async def _run_on_scheduler(self, function, *args, wait=True, **kwargs): response = await self.scheduler.run_function( function=dumps(function), args=dumps(args), kwargs=dumps(kwargs), wait=wait, ) if response["status"] == "error": typ, exc, tb = clean_exception(**response) raise exc.with_traceback(tb) else: return response["result"] def run_on_scheduler(self, function, *args, **kwargs): """Run a function on the scheduler process This is typically used for live debugging. The function should take a keyword argument ``dask_scheduler=``, which will be given the scheduler object itself. Parameters ---------- function : callable The function to run on the scheduler process *args : tuple Optional arguments for the function **kwargs : dict Optional keyword arguments for the function Examples -------- >>> def get_number_of_tasks(dask_scheduler=None): ... return len(dask_scheduler.tasks) >>> client.run_on_scheduler(get_number_of_tasks) # doctest: +SKIP 100 Run asynchronous functions in the background: >>> async def print_state(dask_scheduler): # doctest: +SKIP ... while True: ... print(dask_scheduler.status) ... await asyncio.sleep(1) >>> c.run(print_state, wait=False) # doctest: +SKIP See Also -------- Client.run : Run a function on all workers """ return self.sync(self._run_on_scheduler, function, *args, **kwargs) async def _run( self, function, *args, nanny: bool = False, workers: list[str] | None = None, wait: bool = True, on_error: Literal["raise", "return", "ignore"] = "raise", **kwargs, ): responses = await self.scheduler.broadcast( msg=dict( op="run", function=dumps(function), args=dumps(args), wait=wait, kwargs=dumps(kwargs), ), workers=workers, nanny=nanny, on_error="return_pickle", ) results = {} for key, resp in responses.items(): if isinstance(resp, bytes): # Pickled RPC exception exc = loads(resp) assert isinstance(exc, Exception) elif resp["status"] == "error": # Exception raised by the remote function _, exc, tb = clean_exception(**resp) exc = exc.with_traceback(tb) else: assert resp["status"] == "OK" results[key] = resp["result"] continue if on_error == "raise": raise exc elif on_error == "return": results[key] = exc elif on_error != "ignore": raise ValueError( "on_error must be 'raise', 'return', or 'ignore'; " f"got {on_error!r}" ) if wait: return results def run( self, function, *args, workers: list[str] | None = None, wait: bool = True, nanny: bool = False, on_error: Literal["raise", "return", "ignore"] = "raise", **kwargs, ): """ Run a function on all workers outside of task scheduling system This calls a function on all currently known workers immediately, blocks until those results come back, and returns the results asynchronously as a dictionary keyed by worker address. This method is generally used for side effects such as collecting diagnostic information or installing libraries. If your function takes an input argument named ``dask_worker`` then that variable will be populated with the worker itself. Parameters ---------- function : callable The function to run *args : tuple Optional arguments for the remote function **kwargs : dict Optional keyword arguments for the remote function workers : list Workers on which to run the function. Defaults to all known workers. wait : boolean (optional) If the function is asynchronous whether or not to wait until that function finishes. nanny : bool, default False Whether to run ``function`` on the nanny. By default, the function is run on the worker process. If specified, the addresses in ``workers`` should still be the worker addresses, not the nanny addresses. on_error: "raise" | "return" | "ignore" If the function raises an error on a worker: raise (default) Re-raise the exception on the client. The output from other workers will be lost. return Return the Exception object instead of the function output for the worker ignore Ignore the exception and remove the worker from the result dict Examples -------- >>> c.run(os.getpid) # doctest: +SKIP {'192.168.0.100:9000': 1234, '192.168.0.101:9000': 4321, '192.168.0.102:9000': 5555} Restrict computation to particular workers with the ``workers=`` keyword argument. >>> c.run(os.getpid, workers=['192.168.0.100:9000', ... '192.168.0.101:9000']) # doctest: +SKIP {'192.168.0.100:9000': 1234, '192.168.0.101:9000': 4321} >>> def get_status(dask_worker): ... return dask_worker.status >>> c.run(get_status) # doctest: +SKIP {'192.168.0.100:9000': 'running', '192.168.0.101:9000': 'running} Run asynchronous functions in the background: >>> async def print_state(dask_worker): # doctest: +SKIP ... while True: ... print(dask_worker.status) ... await asyncio.sleep(1) >>> c.run(print_state, wait=False) # doctest: +SKIP """ return self.sync( self._run, function, *args, workers=workers, wait=wait, nanny=nanny, on_error=on_error, **kwargs, ) @staticmethod def _get_computation_code( stacklevel: int | None = None, nframes: int = 1 ) -> tuple[SourceCode, ...]: """Walk up the stack to the user code and extract the code surrounding the compute/submit/persist call. All modules encountered which are ignored through the option `distributed.diagnostics.computations.ignore-modules` will be ignored. This can be used to exclude commonly used libraries which wrap dask/distributed compute calls. ``stacklevel`` may be used to explicitly indicate from which frame on the stack to get the source code. """ if nframes <= 0: return () ignore_modules = dask.config.get( "distributed.diagnostics.computations.ignore-modules" ) if not isinstance(ignore_modules, list): raise TypeError( "Ignored modules must be a list. Instead got " f"({type(ignore_modules)}, {ignore_modules})" ) ignore_files = dask.config.get( "distributed.diagnostics.computations.ignore-files" ) if not isinstance(ignore_files, list): raise TypeError( "Ignored files must be a list. Instead got " f"({type(ignore_files)}, {ignore_files})" ) mod_pattern: re.Pattern | None = None fname_pattern: re.Pattern | None = None if stacklevel is None: if ignore_modules: mod_pattern = re.compile( "|".join([f"(?:{mod})" for mod in ignore_modules]) ) if ignore_files: fname_pattern = re.compile( r".*[\\/](" + "|".join(mod for mod in ignore_files) + r")([\\/]|$)" ) else: # stacklevel 0 or less - shows dask internals which likely isn't helpful stacklevel = stacklevel if stacklevel > 0 else 1 code: list[SourceCode] = [] for i, (fr, lineno_frame) in enumerate( traceback.walk_stack(sys._getframe().f_back), 1 ): if len(code) >= nframes: break if stacklevel is not None and i != stacklevel: continue if fr.f_code.co_name in ("", ""): continue if mod_pattern and mod_pattern.match(fr.f_globals.get("__name__", "")): continue if fname_pattern and fname_pattern.match(fr.f_code.co_filename): continue kwargs = dict( lineno_frame=lineno_frame, lineno_relative=lineno_frame - fr.f_code.co_firstlineno, filename=fr.f_code.co_filename, ) try: code.append(SourceCode(code=inspect.getsource(fr), **kwargs)) # type: ignore except OSError: try: from IPython import get_ipython ip = get_ipython() if ip is not None: # The current cell code.append(SourceCode(code=ip.history_manager._i00, **kwargs)) # type: ignore except ImportError: pass # No IPython break if hasattr(fr.f_back, "f_globals"): module_name = fr.f_back.f_globals["__name__"] # type: ignore if module_name == "__channelexec__": break # execnet; pytest-xdist # pragma: nocover try: module_name = sys.modules[module_name].__name__ except KeyError: # Ignore pathological cases where the module name isn't in `sys.modules` break # Ignore IPython related wrapping functions to user code if module_name.endswith("interactiveshell"): break return tuple(reversed(code)) def _inform_scheduler_of_futures(self): self._send_to_scheduler( { "op": "client-desires-keys", "keys": list(self.refcount), } ) def _graph_to_futures( self, dsk, keys, span_metadata, workers=None, allow_other_workers=None, internal_priority=None, user_priority=0, resources=None, retries=None, fifo_timeout=0, actors=None, ): with self._refcount_lock: if actors is not None and actors is not True and actors is not False: actors = list(self._expand_key(actors)) # Make sure `dsk` is a high level graph if not isinstance(dsk, HighLevelGraph): dsk = HighLevelGraph.from_collections(id(dsk), dsk, dependencies=()) annotations = {} if user_priority: annotations["priority"] = user_priority if workers: if not isinstance(workers, (list, tuple, set)): workers = [workers] annotations["workers"] = workers if retries: annotations["retries"] = retries if allow_other_workers not in (True, False, None): raise TypeError("allow_other_workers= must be True, False, or None") if allow_other_workers: annotations["allow_other_workers"] = allow_other_workers if resources: annotations["resources"] = resources # Merge global and local annotations annotations = merge(dask.get_annotations(), annotations) # Pack the high level graph before sending it to the scheduler keyset = set(keys) # Validate keys for key in keyset: validate_key(key) # Create futures before sending graph (helps avoid contention) futures = {key: Future(key, self) for key in keyset} # Circular import from distributed.protocol import serialize from distributed.protocol.serialize import ToPickle header, frames = serialize(ToPickle(dsk), on_error="raise") pickled_size = sum(map(nbytes, [header] + frames)) if pickled_size > parse_bytes( dask.config.get("distributed.admin.large-graph-warning-threshold") ): warnings.warn( f"Sending large graph of size {format_bytes(pickled_size)}.\n" "This may cause some slowdown.\n" "Consider loading the data with Dask directly\n or using futures or " "delayed objects to embed the data into the graph without repetition.\n" "See also https://docs.dask.org/en/stable/best-practices.html#load-data-with-dask for more information." ) computations = self._get_computation_code( nframes=dask.config.get("distributed.diagnostics.computations.nframes") ) self._send_to_scheduler( { "op": "update-graph", "graph_header": header, "graph_frames": frames, "keys": set(keys), "internal_priority": internal_priority, "submitting_task": getattr(thread_state, "key", None), "fifo_timeout": fifo_timeout, "actors": actors, "code": ToPickle(computations), "annotations": ToPickle(annotations), "span_metadata": ToPickle(span_metadata), } ) return futures def get( self, dsk, keys, workers=None, allow_other_workers=None, resources=None, sync=True, asynchronous=None, direct=None, retries=None, priority=0, fifo_timeout="60s", actors=None, **kwargs, ): """Compute dask graph Parameters ---------- dsk : dict keys : object, or nested lists of objects workers : string or iterable of strings A set of worker addresses or hostnames on which computations may be performed. Leave empty to default to all workers (common case) allow_other_workers : bool (defaults to False) Used with ``workers``. Indicates whether or not the computations may be performed on workers that are not in the `workers` set(s). resources : dict (defaults to {}) Defines the ``resources`` each instance of this mapped task requires on the worker; e.g. ``{'GPU': 2}``. See :doc:`worker resources ` for details on defining resources. sync : bool (optional) Returns Futures if False or concrete values if True (default). asynchronous: bool If True the client is in asynchronous mode direct : bool Whether or not to connect directly to the workers, or to ask the scheduler to serve as intermediary. This can also be set when creating the Client. retries : int (default to 0) Number of allowed automatic retries if computing a result fails priority : Number Optional prioritization of task. Zero is default. Higher priorities take precedence fifo_timeout : timedelta str (defaults to '60s') Allowed amount of time between calls to consider the same priority actors : bool or dict (default None) Whether these tasks should exist on the worker as stateful actors. Specified on a global (True/False) or per-task (``{'x': True, 'y': False}``) basis. See :doc:`actors` for additional details. Returns ------- results If 'sync' is True, returns the results. Otherwise, returns the known data packed If 'sync' is False, returns the known data. Otherwise, returns the results Examples -------- >>> from operator import add # doctest: +SKIP >>> c = Client('127.0.0.1:8787') # doctest: +SKIP >>> c.get({'x': (add, 1, 2)}, 'x') # doctest: +SKIP 3 See Also -------- Client.compute : Compute asynchronous collections """ futures = self._graph_to_futures( dsk, keys=set(flatten([keys])), workers=workers, allow_other_workers=allow_other_workers, resources=resources, fifo_timeout=fifo_timeout, retries=retries, user_priority=priority, actors=actors, span_metadata=SpanMetadata(collections=[{"type": "low-level-graph"}]), ) packed = pack_data(keys, futures) if sync: if getattr(thread_state, "key", False): try: secede() should_rejoin = True except Exception: should_rejoin = False try: results = self.gather(packed, asynchronous=asynchronous, direct=direct) finally: for f in futures.values(): f.release() if getattr(thread_state, "key", False) and should_rejoin: rejoin() return results return packed def _optimize_insert_futures(self, dsk, keys): """Replace known keys in dask graph with Futures When given a Dask graph that might have overlapping keys with our known results we replace the values of that graph with futures. This can be used as an optimization to avoid recomputation. This returns the same graph if unchanged but a new graph if any changes were necessary. """ with self._refcount_lock: changed = False for key in list(dsk): if key in self.futures: if not changed: changed = True dsk = ensure_dict(dsk) dsk[key] = Future(key, self) if changed: dsk, _ = dask.optimization.cull(dsk, keys) return dsk def normalize_collection(self, collection): """ Replace collection's tasks by already existing futures if they exist This normalizes the tasks within a collections task graph against the known futures within the scheduler. It returns a copy of the collection with a task graph that includes the overlapping futures. Parameters ---------- collection : dask object Collection like dask.array or dataframe or dask.value objects Returns ------- collection : dask object Collection with its tasks replaced with any existing futures. Examples -------- >>> len(x.__dask_graph__()) # x is a dask collection with 100 tasks # doctest: +SKIP 100 >>> set(client.futures).intersection(x.__dask_graph__()) # some overlap exists # doctest: +SKIP 10 >>> x = client.normalize_collection(x) # doctest: +SKIP >>> len(x.__dask_graph__()) # smaller computational graph # doctest: +SKIP 20 See Also -------- Client.persist : trigger computation of collection's tasks """ dsk_orig = collection.__dask_graph__() dsk = self._optimize_insert_futures(dsk_orig, collection.__dask_keys__()) if dsk is dsk_orig: return collection else: return redict_collection(collection, dsk) def compute( self, collections, sync=False, optimize_graph=True, workers=None, allow_other_workers=False, resources=None, retries=0, priority=0, fifo_timeout="60s", actors=None, traverse=True, **kwargs, ): """Compute dask collections on cluster Parameters ---------- collections : iterable of dask objects or single dask object Collections like dask.array or dataframe or dask.value objects sync : bool (optional) Returns Futures if False (default) or concrete values if True optimize_graph : bool Whether or not to optimize the underlying graphs workers : string or iterable of strings A set of worker hostnames on which computations may be performed. Leave empty to default to all workers (common case) allow_other_workers : bool (defaults to False) Used with `workers`. Indicates whether or not the computations may be performed on workers that are not in the `workers` set(s). retries : int (default to 0) Number of allowed automatic retries if computing a result fails priority : Number Optional prioritization of task. Zero is default. Higher priorities take precedence fifo_timeout : timedelta str (defaults to '60s') Allowed amount of time between calls to consider the same priority traverse : bool (defaults to True) By default dask traverses builtin python collections looking for dask objects passed to ``compute``. For large collections this can be expensive. If none of the arguments contain any dask objects, set ``traverse=False`` to avoid doing this traversal. resources : dict (defaults to {}) Defines the `resources` each instance of this mapped task requires on the worker; e.g. ``{'GPU': 2}``. See :doc:`worker resources ` for details on defining resources. actors : bool or dict (default None) Whether these tasks should exist on the worker as stateful actors. Specified on a global (True/False) or per-task (``{'x': True, 'y': False}``) basis. See :doc:`actors` for additional details. **kwargs Options to pass to the graph optimize calls Returns ------- List of Futures if input is a sequence, or a single future otherwise Examples -------- >>> from dask import delayed >>> from operator import add >>> x = delayed(add)(1, 2) >>> y = delayed(add)(x, x) >>> xx, yy = client.compute([x, y]) # doctest: +SKIP >>> xx # doctest: +SKIP >>> xx.result() # doctest: +SKIP 3 >>> yy.result() # doctest: +SKIP 6 Also support single arguments >>> xx = client.compute(x) # doctest: +SKIP See Also -------- Client.get : Normal synchronous dask.get function """ if isinstance(collections, (list, tuple, set, frozenset)): singleton = False else: collections = [collections] singleton = True if traverse: collections = tuple( ( dask.delayed(a) if isinstance(a, (list, set, tuple, dict, Iterator)) else a ) for a in collections ) variables = [a for a in collections if dask.is_dask_collection(a)] metadata = SpanMetadata( collections=[get_collections_metadata(v) for v in variables] ) dsk = self.collections_to_dsk(variables, optimize_graph, **kwargs) names = ["finalize-%s" % tokenize(v) for v in variables] dsk2 = {} for i, (name, v) in enumerate(zip(names, variables)): func, extra_args = v.__dask_postcompute__() keys = v.__dask_keys__() if func is single_key and len(keys) == 1 and not extra_args: names[i] = keys[0] else: t = Task(name, func, _convert_dask_keys(keys), *extra_args) dsk2[t.key] = t if not isinstance(dsk, HighLevelGraph): dsk = HighLevelGraph.from_collections(id(dsk), dsk, dependencies=()) # Let's append the finalize graph to dsk finalize_name = tokenize(names) layers = {finalize_name: dsk2} layers.update(dsk.layers) dependencies = {finalize_name: set(dsk.layers.keys())} dependencies.update(dsk.dependencies) dsk = HighLevelGraph(layers, dependencies) futures_dict = self._graph_to_futures( dsk, names, workers=workers, allow_other_workers=allow_other_workers, resources=resources, retries=retries, user_priority=priority, fifo_timeout=fifo_timeout, actors=actors, span_metadata=metadata, ) i = 0 futures = [] for arg in collections: if dask.is_dask_collection(arg): futures.append(futures_dict[names[i]]) i += 1 else: futures.append(arg) if sync: result = self.gather(futures) else: result = futures if singleton: return first(result) else: return result def persist( self, collections, optimize_graph=True, workers=None, allow_other_workers=None, resources=None, retries=None, priority=0, fifo_timeout="60s", actors=None, **kwargs, ): """Persist dask collections on cluster Starts computation of the collection on the cluster in the background. Provides a new dask collection that is semantically identical to the previous one, but now based off of futures currently in execution. Parameters ---------- collections : sequence or single dask object Collections like dask.array or dataframe or dask.value objects optimize_graph : bool Whether or not to optimize the underlying graphs workers : string or iterable of strings A set of worker hostnames on which computations may be performed. Leave empty to default to all workers (common case) allow_other_workers : bool (defaults to False) Used with `workers`. Indicates whether or not the computations may be performed on workers that are not in the `workers` set(s). retries : int (default to 0) Number of allowed automatic retries if computing a result fails priority : Number Optional prioritization of task. Zero is default. Higher priorities take precedence fifo_timeout : timedelta str (defaults to '60s') Allowed amount of time between calls to consider the same priority resources : dict (defaults to {}) Defines the `resources` each instance of this mapped task requires on the worker; e.g. ``{'GPU': 2}``. See :doc:`worker resources ` for details on defining resources. actors : bool or dict (default None) Whether these tasks should exist on the worker as stateful actors. Specified on a global (True/False) or per-task (``{'x': True, 'y': False}``) basis. See :doc:`actors` for additional details. **kwargs Options to pass to the graph optimize calls Returns ------- List of collections, or single collection, depending on type of input. Examples -------- >>> xx = client.persist(x) # doctest: +SKIP >>> xx, yy = client.persist([x, y]) # doctest: +SKIP See Also -------- Client.compute """ if isinstance(collections, (tuple, list, set, frozenset)): singleton = False else: singleton = True collections = [collections] assert all(map(dask.is_dask_collection, collections)) metadata = SpanMetadata( collections=[get_collections_metadata(v) for v in collections] ) dsk = self.collections_to_dsk(collections, optimize_graph, **kwargs) names = {k for c in collections for k in flatten(c.__dask_keys__())} futures = self._graph_to_futures( dsk, names, workers=workers, allow_other_workers=allow_other_workers, resources=resources, retries=retries, user_priority=priority, fifo_timeout=fifo_timeout, actors=actors, span_metadata=metadata, ) postpersists = [c.__dask_postpersist__() for c in collections] result = [ func({k: futures[k] for k in flatten(c.__dask_keys__())}, *args) for (func, args), c in zip(postpersists, collections) ] if singleton: return first(result) else: return result async def _restart( self, timeout: str | int | float | NoDefault, wait_for_workers: bool ) -> None: if timeout is no_default: timeout = self._timeout * 4 timeout = parse_timedelta(cast("str|int|float", timeout), "s") await self.scheduler.restart( timeout=timeout, wait_for_workers=wait_for_workers, stimulus_id=f"client-restart-{time()}", ) def restart( self, timeout: str | int | float | NoDefault = no_default, wait_for_workers: bool = True, ): """ Restart all workers. Reset local state. Optionally wait for workers to return. Workers without nannies are shut down, hoping an external deployment system will restart them. Therefore, if not using nannies and your deployment system does not automatically restart workers, ``restart`` will just shut down all workers, then time out! After ``restart``, all connected workers are new, regardless of whether ``TimeoutError`` was raised. Any workers that failed to shut down in time are removed, and may or may not shut down on their own in the future. Parameters ---------- timeout: How long to wait for workers to shut down and come back, if ``wait_for_workers`` is True, otherwise just how long to wait for workers to shut down. Raises ``asyncio.TimeoutError`` if this is exceeded. wait_for_workers: Whether to wait for all workers to reconnect, or just for them to shut down (default True). Use ``restart(wait_for_workers=False)`` combined with :meth:`Client.wait_for_workers` for granular control over how many workers to wait for. See also -------- Scheduler.restart Client.restart_workers """ return self.sync( self._restart, timeout=timeout, wait_for_workers=wait_for_workers ) async def _restart_workers( self, workers: list[str], timeout: str | int | float | NoDefault, raise_for_error: bool, ) -> dict[str, Literal["OK", "removed", "timed out"]]: if timeout is no_default: timeout = self._timeout * 4 timeout = parse_timedelta(cast("str|int|float", timeout), "s") info = self.scheduler_info() name_to_addr = {meta["name"]: addr for addr, meta in info["workers"].items()} worker_addrs = [name_to_addr.get(w, w) for w in workers] out: dict[str, Literal["OK", "removed", "timed out"]] = ( await self.scheduler.restart_workers( workers=worker_addrs, timeout=timeout, on_error="raise" if raise_for_error else "return", stimulus_id=f"client-restart-workers-{time()}", ) ) # Map keys back to original `workers` input names/addresses out = {w: out[w_addr] for w, w_addr in zip(workers, worker_addrs)} if raise_for_error: assert all(v == "OK" for v in out.values()), out return out def restart_workers( self, workers: list[str], timeout: str | int | float | NoDefault = no_default, raise_for_error: bool = True, ): """Restart a specified set of workers .. note:: Only workers being monitored by a :class:`distributed.Nanny` can be restarted. See ``Nanny.restart`` for more details. Parameters ---------- workers : list[str] Workers to restart. This can be a list of worker addresses, names, or a both. timeout : int | float | None Number of seconds to wait raise_for_error: bool (default True) Whether to raise a :py:class:`TimeoutError` if restarting worker(s) doesn't finish within ``timeout``, or another exception caused from restarting worker(s). Returns ------- dict[str, "OK" | "removed" | "timed out"] Mapping of worker and restart status, the keys will match the original values passed in via ``workers``. Notes ----- This method differs from :meth:`Client.restart` in that this method simply restarts the specified set of workers, while ``Client.restart`` will restart all workers and also reset local state on the cluster (e.g. all keys are released). Additionally, this method does not gracefully handle tasks that are being executed when a worker is restarted. These tasks may fail or have their suspicious count incremented. Examples -------- You can get information about active workers using the following: >>> workers = client.scheduler_info()['workers'] From that list you may want to select some workers to restart >>> client.restart_workers(workers=['tcp://address:port', ...]) See Also -------- Client.restart """ info = self.scheduler_info() for worker, meta in info["workers"].items(): if (worker in workers or meta["name"] in workers) and meta["nanny"] is None: raise ValueError( f"Restarting workers requires a nanny to be used. Worker " f"{worker} has type {info['workers'][worker]['type']}." ) return self.sync( self._restart_workers, workers=workers, timeout=timeout, raise_for_error=raise_for_error, ) async def _upload_large_file(self, local_filename, remote_filename=None): if remote_filename is None: remote_filename = os.path.split(local_filename)[1] with open(local_filename, "rb") as f: data = f.read() [future] = await self._scatter([data]) key = future.key await self._replicate(future) def dump_to_file(dask_worker=None): if not os.path.isabs(remote_filename): fn = os.path.join(dask_worker.local_directory, remote_filename) else: fn = remote_filename with open(fn, "wb") as f: f.write(dask_worker.data[key]) return len(dask_worker.data[key]) response = await self._run(dump_to_file) assert all(len(data) == v for v in response.values()) def upload_file(self, filename, load: bool = True): """Upload local package to scheduler and workers This sends a local file up to the scheduler and all worker nodes. This file is placed into the working directory of each node, see config option ``temporary-directory`` (defaults to :py:func:`tempfile.gettempdir`). This directory will be added to the Python's system path so any ``.py``, ``.egg`` or ``.zip`` files will be importable. Parameters ---------- filename : string Filename of ``.py``, ``.egg``, or ``.zip`` file to send to workers load : bool, optional Whether or not to import the module as part of the upload process. Defaults to ``True``. Examples -------- >>> client.upload_file('mylibrary.egg') # doctest: +SKIP >>> from mylibrary import myfunc # doctest: +SKIP >>> L = client.map(myfunc, seq) # doctest: +SKIP >>> >>> # Where did that file go? Use `dask_worker.local_directory`. >>> def where_is_mylibrary(dask_worker): >>> path = pathlib.Path(dask_worker.local_directory) / 'mylibrary.egg' >>> assert path.exists() >>> return str(path) >>> >>> client.run(where_is_mylibrary) # doctest: +SKIP """ name = filename + str(uuid.uuid4()) async def _(): results = await asyncio.gather( self.register_plugin( SchedulerUploadFile(filename, load=load), name=name ), # FIXME: Make scheduler plugin responsible for (de)registering worker plugin self.register_plugin(UploadFile(filename, load=load), name=name), ) return results[1] # Results from workers upload return self.sync(_) async def _rebalance(self, futures=None, workers=None): if futures is not None: await _wait(futures) keys = list({f.key for f in self.futures_of(futures)}) else: keys = None result = await self.scheduler.rebalance(keys=keys, workers=workers) if result["status"] == "partial-fail": raise KeyError(f"Could not rebalance keys: {result['keys']}") assert result["status"] == "OK", result def rebalance(self, futures=None, workers=None, **kwargs): """Rebalance data within network Move data between workers to roughly balance memory burden. This either affects a subset of the keys/workers or the entire network, depending on keyword arguments. For details on the algorithm and configuration options, refer to the matching scheduler-side method :meth:`~distributed.scheduler.Scheduler.rebalance`. .. warning:: This operation is generally not well tested against normal operation of the scheduler. It is not recommended to use it while waiting on computations. Parameters ---------- futures : list, optional A list of futures to balance, defaults all data workers : list, optional A list of workers on which to balance, defaults to all workers **kwargs : dict Optional keyword arguments for the function """ return self.sync(self._rebalance, futures, workers, **kwargs) async def _replicate(self, futures, n=None, workers=None, branching_factor=2): futures = self.futures_of(futures) await _wait(futures) keys = {f.key for f in futures} await self.scheduler.replicate( keys=list(keys), n=n, workers=workers, branching_factor=branching_factor ) def replicate(self, futures, n=None, workers=None, branching_factor=2, **kwargs): """Set replication of futures within network Copy data onto many workers. This helps to broadcast frequently accessed data and can improve resilience. This performs a tree copy of the data throughout the network individually on each piece of data. This operation blocks until complete. It does not guarantee replication of data to future workers. .. note:: This method is incompatible with the Active Memory Manager's :ref:`ReduceReplicas` policy. If you wish to use it, you must first disable the policy or disable the AMM entirely. Parameters ---------- futures : list of futures Futures we wish to replicate n : int, optional Number of processes on the cluster on which to replicate the data. Defaults to all. workers : list of worker addresses Workers on which we want to restrict the replication. Defaults to all. branching_factor : int, optional The number of workers that can copy data in each generation **kwargs : dict Optional keyword arguments for the remote function Examples -------- >>> x = c.submit(func, *args) # doctest: +SKIP >>> c.replicate([x]) # send to all workers # doctest: +SKIP >>> c.replicate([x], n=3) # send to three workers # doctest: +SKIP >>> c.replicate([x], workers=['alice', 'bob']) # send to specific # doctest: +SKIP >>> c.replicate([x], n=1, workers=['alice', 'bob']) # send to one of specific workers # doctest: +SKIP >>> c.replicate([x], n=1) # reduce replications # doctest: +SKIP See Also -------- Client.rebalance """ return self.sync( self._replicate, futures, n=n, workers=workers, branching_factor=branching_factor, **kwargs, ) def nthreads(self, workers=None, **kwargs): """The number of threads/cores available on each worker node Parameters ---------- workers : list (optional) A list of workers that we care about specifically. Leave empty to receive information about all workers. **kwargs : dict Optional keyword arguments for the remote function Examples -------- >>> c.nthreads() # doctest: +SKIP {'192.168.1.141:46784': 8, '192.167.1.142:47548': 8, '192.167.1.143:47329': 8, '192.167.1.144:37297': 8} See Also -------- Client.who_has Client.has_what """ if isinstance(workers, tuple) and all( isinstance(i, (str, tuple)) for i in workers ): workers = list(workers) if workers is not None and not isinstance(workers, (tuple, list, set)): workers = [workers] return self.sync(self.scheduler.ncores, workers=workers, **kwargs) ncores = nthreads def who_has(self, futures=None, **kwargs): """The workers storing each future's data Parameters ---------- futures : list (optional) A list of futures, defaults to all data **kwargs : dict Optional keyword arguments for the remote function Examples -------- >>> x, y, z = c.map(inc, [1, 2, 3]) # doctest: +SKIP >>> wait([x, y, z]) # doctest: +SKIP >>> c.who_has() # doctest: +SKIP {'inc-1c8dd6be1c21646c71f76c16d09304ea': ['192.168.1.141:46784'], 'inc-1e297fc27658d7b67b3a758f16bcf47a': ['192.168.1.141:46784'], 'inc-fd65c238a7ea60f6a01bf4c8a5fcf44b': ['192.168.1.141:46784']} >>> c.who_has([x, y]) # doctest: +SKIP {'inc-1c8dd6be1c21646c71f76c16d09304ea': ['192.168.1.141:46784'], 'inc-1e297fc27658d7b67b3a758f16bcf47a': ['192.168.1.141:46784']} See Also -------- Client.has_what Client.nthreads """ if futures is not None: futures = self.futures_of(futures) keys = list({f.key for f in futures}) else: keys = None async def _(): return WhoHas(await self.scheduler.who_has(keys=keys, **kwargs)) return self.sync(_) def has_what(self, workers=None, **kwargs): """Which keys are held by which workers This returns the keys of the data that are held in each worker's memory. Parameters ---------- workers : list (optional) A list of worker addresses, defaults to all **kwargs : dict Optional keyword arguments for the remote function Examples -------- >>> x, y, z = c.map(inc, [1, 2, 3]) # doctest: +SKIP >>> wait([x, y, z]) # doctest: +SKIP >>> c.has_what() # doctest: +SKIP {'192.168.1.141:46784': ['inc-1c8dd6be1c21646c71f76c16d09304ea', 'inc-fd65c238a7ea60f6a01bf4c8a5fcf44b', 'inc-1e297fc27658d7b67b3a758f16bcf47a']} See Also -------- Client.who_has Client.nthreads Client.processing """ if isinstance(workers, tuple) and all( isinstance(i, (str, tuple)) for i in workers ): workers = list(workers) if workers is not None and not isinstance(workers, (tuple, list, set)): workers = [workers] async def _(): return HasWhat(await self.scheduler.has_what(workers=workers, **kwargs)) return self.sync(_) def processing(self, workers=None): """The tasks currently running on each worker Parameters ---------- workers : list (optional) A list of worker addresses, defaults to all Examples -------- >>> x, y, z = c.map(inc, [1, 2, 3]) # doctest: +SKIP >>> c.processing() # doctest: +SKIP {'192.168.1.141:46784': ['inc-1c8dd6be1c21646c71f76c16d09304ea', 'inc-fd65c238a7ea60f6a01bf4c8a5fcf44b', 'inc-1e297fc27658d7b67b3a758f16bcf47a']} See Also -------- Client.who_has Client.has_what Client.nthreads """ if isinstance(workers, tuple) and all( isinstance(i, (str, tuple)) for i in workers ): workers = list(workers) if workers is not None and not isinstance(workers, (tuple, list, set)): workers = [workers] return self.sync(self.scheduler.processing, workers=workers) def nbytes(self, keys=None, summary=True, **kwargs): """The bytes taken up by each key on the cluster This is as measured by ``sys.getsizeof`` which may not accurately reflect the true cost. Parameters ---------- keys : list (optional) A list of keys, defaults to all keys summary : boolean, (optional) Summarize keys into key types **kwargs : dict Optional keyword arguments for the remote function Examples -------- >>> x, y, z = c.map(inc, [1, 2, 3]) # doctest: +SKIP >>> c.nbytes(summary=False) # doctest: +SKIP {'inc-1c8dd6be1c21646c71f76c16d09304ea': 28, 'inc-1e297fc27658d7b67b3a758f16bcf47a': 28, 'inc-fd65c238a7ea60f6a01bf4c8a5fcf44b': 28} >>> c.nbytes(summary=True) # doctest: +SKIP {'inc': 84} See Also -------- Client.who_has """ return self.sync(self.scheduler.nbytes, keys=keys, summary=summary, **kwargs) def call_stack(self, futures=None, keys=None): """The actively running call stack of all relevant keys You can specify data of interest either by providing futures or collections in the ``futures=`` keyword or a list of explicit keys in the ``keys=`` keyword. If neither are provided then all call stacks will be returned. Parameters ---------- futures : list (optional) List of futures, defaults to all data keys : list (optional) List of key names, defaults to all data Examples -------- >>> df = dd.read_parquet(...).persist() # doctest: +SKIP >>> client.call_stack(df) # call on collections >>> client.call_stack() # Or call with no arguments for all activity # doctest: +SKIP """ keys = keys or [] if futures is not None: futures = self.futures_of(futures) keys += list({f.key for f in futures}) return self.sync(self.scheduler.call_stack, keys=keys or None) def profile( self, key=None, start=None, stop=None, workers=None, merge_workers=True, plot=False, filename=None, server=False, scheduler=False, ): """Collect statistical profiling information about recent work Parameters ---------- key : str Key prefix to select, this is typically a function name like 'inc' Leave as None to collect all data start : time stop : time workers : list List of workers to restrict profile information server : bool If true, return the profile of the worker's administrative thread rather than the worker threads. This is useful when profiling Dask itself, rather than user code. scheduler : bool If true, return the profile information from the scheduler's administrative thread rather than the workers. This is useful when profiling Dask's scheduling itself. plot : boolean or string Whether or not to return a plot object filename : str Filename to save the plot Examples -------- >>> client.profile() # call on collections >>> client.profile(filename='dask-profile.html') # save to html file """ return self.sync( self._profile, key=key, workers=workers, merge_workers=merge_workers, start=start, stop=stop, plot=plot, filename=filename, server=server, scheduler=scheduler, ) async def _profile( self, key=None, start=None, stop=None, workers=None, merge_workers=True, plot=False, filename=None, server=False, scheduler=False, ): if isinstance(workers, (str, Number)): workers = [workers] state = await self.scheduler.profile( key=key, workers=workers, merge_workers=merge_workers, start=start, stop=stop, server=server, scheduler=scheduler, ) if filename: plot = True if plot: from distributed import profile data = profile.plot_data(state) figure, source = profile.plot_figure(data, sizing_mode="stretch_both") if plot == "save" and not filename: filename = "dask-profile.html" if filename: from bokeh.plotting import output_file, save output_file(filename=filename, title="Dask Profile") save(figure, filename=filename) return (state, figure) else: return state def scheduler_info(self, **kwargs): """Basic information about the workers in the cluster Parameters ---------- **kwargs : dict Optional keyword arguments for the remote function Examples -------- >>> c.scheduler_info() # doctest: +SKIP {'id': '2de2b6da-69ee-11e6-ab6a-e82aea155996', 'services': {}, 'type': 'Scheduler', 'workers': {'127.0.0.1:40575': {'active': 0, 'last-seen': 1472038237.4845693, 'name': '127.0.0.1:40575', 'services': {}, 'stored': 0, 'time-delay': 0.0061032772064208984}}} """ if not self.asynchronous: self.sync(self._update_scheduler_info) return self._scheduler_identity def dump_cluster_state( self, filename: str = "dask-cluster-dump", write_from_scheduler: bool | None = None, exclude: Collection[str] = (), format: Literal["msgpack", "yaml"] = "msgpack", **storage_options, ): """Extract a dump of the entire cluster state and persist to disk or a URL. This is intended for debugging purposes only. Warning: Memory usage on the scheduler (and client, if writing the dump locally) can be large. On a large or long-running cluster, this can take several minutes. The scheduler may be unresponsive while the dump is processed. Results will be stored in a dict:: { "scheduler": {...}, # scheduler state "workers": { worker_addr: {...}, # worker state ... } "versions": { "scheduler": {...}, "workers": { worker_addr: {...}, ... } } } Parameters ---------- filename: The path or URL to write to. The appropriate file suffix (``.msgpack.gz`` or ``.yaml``) will be appended automatically. Must be a path supported by :func:`fsspec.open` (like ``s3://my-bucket/cluster-dump``, or ``cluster-dumps/dump``). See ``write_from_scheduler`` to control whether the dump is written directly to ``filename`` from the scheduler, or sent back to the client over the network, then written locally. write_from_scheduler: If None (default), infer based on whether ``filename`` looks like a URL or a local path: True if the filename contains ``://`` (like ``s3://my-bucket/cluster-dump``), False otherwise (like ``local_dir/cluster-dump``). If True, write cluster state directly to ``filename`` from the scheduler. If ``filename`` is a local path, the dump will be written to that path on the *scheduler's* filesystem, so be careful if the scheduler is running on ephemeral hardware. Useful when the scheduler is attached to a network filesystem or persistent disk, or for writing to buckets. If False, transfer cluster state from the scheduler back to the client over the network, then write it to ``filename``. This is much less efficient for large dumps, but useful when the scheduler doesn't have access to any persistent storage. exclude: A collection of attribute names which are supposed to be excluded from the dump, e.g. to exclude code, tracebacks, logs, etc. Defaults to exclude ``run_spec``, which is the serialized user code. This is typically not required for debugging. To allow serialization of this, pass an empty tuple. format: Either ``"msgpack"`` or ``"yaml"``. If msgpack is used (default), the output will be stored in a gzipped file as msgpack. To read:: import gzip, msgpack with gzip.open("filename") as fd: state = msgpack.unpack(fd) or:: import yaml try: from yaml import CLoader as Loader except ImportError: from yaml import Loader with open("filename") as fd: state = yaml.load(fd, Loader=Loader) **storage_options: Any additional arguments to :func:`fsspec.open` when writing to a URL. """ return self.sync( self._dump_cluster_state, filename=filename, write_from_scheduler=write_from_scheduler, exclude=exclude, format=format, **storage_options, ) async def _dump_cluster_state( self, filename: str = "dask-cluster-dump", write_from_scheduler: bool | None = None, exclude: Collection[str] = cluster_dump.DEFAULT_CLUSTER_DUMP_EXCLUDE, format: Literal["msgpack", "yaml"] = cluster_dump.DEFAULT_CLUSTER_DUMP_FORMAT, **storage_options, ): filename = str(filename) if write_from_scheduler is None: write_from_scheduler = "://" in filename if write_from_scheduler: await self.scheduler.dump_cluster_state_to_url( url=filename, exclude=exclude, format=format, **storage_options, ) else: await cluster_dump.write_state( partial(self.scheduler.get_cluster_state, exclude=exclude), filename, format, **storage_options, ) def write_scheduler_file(self, scheduler_file): """Write the scheduler information to a json file. This facilitates easy sharing of scheduler information using a file system. The scheduler file can be used to instantiate a second Client using the same scheduler. Parameters ---------- scheduler_file : str Path to a write the scheduler file. Examples -------- >>> client = Client() # doctest: +SKIP >>> client.write_scheduler_file('scheduler.json') # doctest: +SKIP # connect to previous client's scheduler >>> client2 = Client(scheduler_file='scheduler.json') # doctest: +SKIP """ if self.scheduler_file: raise ValueError("Scheduler file already set") else: self.scheduler_file = scheduler_file with open(self.scheduler_file, "w") as f: json.dump(self.scheduler_info(), f, indent=2) def get_metadata(self, keys, default=no_default): """Get arbitrary metadata from scheduler See set_metadata for the full docstring with examples Parameters ---------- keys : key or list Key to access. If a list then gets within a nested collection default : optional If the key does not exist then return this value instead. If not provided then this raises a KeyError if the key is not present See Also -------- Client.set_metadata """ if not isinstance(keys, (list, tuple)): keys = (keys,) return self.sync(self.scheduler.get_metadata, keys=keys, default=default) def get_scheduler_logs(self, n=None): """Get logs from scheduler Parameters ---------- n : int Number of logs to retrieve. Maxes out at 10000 by default, configurable via the ``distributed.admin.log-length`` configuration value. Returns ------- Logs in reversed order (newest first) """ return self.sync(self.scheduler.logs, n=n) def get_worker_logs(self, n=None, workers=None, nanny=False): """Get logs from workers Parameters ---------- n : int Number of logs to retrieve. Maxes out at 10000 by default, configurable via the ``distributed.admin.log-length`` configuration value. workers : iterable List of worker addresses to retrieve. Gets all workers by default. nanny : bool, default False Whether to get the logs from the workers (False) or the nannies (True). If specified, the addresses in `workers` should still be the worker addresses, not the nanny addresses. Returns ------- Dictionary mapping worker address to logs. Logs are returned in reversed order (newest first) """ return self.sync(self.scheduler.worker_logs, n=n, workers=workers, nanny=nanny) def benchmark_hardware(self) -> dict: """ Run a benchmark on the workers for memory, disk, and network bandwidths Returns ------- result: dict A dictionary mapping the names "disk", "memory", and "network" to dictionaries mapping sizes to bandwidths. These bandwidths are averaged over many workers running computations across the cluster. """ return self.sync(self.scheduler.benchmark_hardware) def log_event(self, topic: str | Collection[str], msg: Any): """Log an event under a given topic Parameters ---------- topic : str, list[str] Name of the topic under which to log an event. To log the same event under multiple topics, pass a list of topic names. msg Event message to log. Note this must be msgpack serializable. Examples -------- >>> from time import time >>> client.log_event("current-time", time()) """ if not _is_dumpable(msg): raise TypeError( f"Message must be msgpack serializable. Got {type(msg)=} instead." ) return self.sync(self.scheduler.log_event, topic=topic, msg=msg) def get_events(self, topic: str | None = None): """Retrieve structured topic logs Parameters ---------- topic : str, optional Name of topic log to retrieve events for. If no ``topic`` is provided, then logs for all topics will be returned. """ return self.sync(self.scheduler.events, topic=topic) async def _handle_event(self, topic, event): if topic not in self._event_handlers: self.unsubscribe_topic(topic) return handler = self._event_handlers[topic] ret = handler(event) if inspect.isawaitable(ret): await ret def subscribe_topic(self, topic, handler): """Subscribe to a topic and execute a handler for every received event Parameters ---------- topic: str The topic name handler: callable or coroutine function A handler called for every received event. The handler must accept a single argument `event` which is a tuple `(timestamp, msg)` where timestamp refers to the clock on the scheduler. Examples -------- >>> import logging >>> logger = logging.getLogger("myLogger") # Log config not shown >>> client.subscribe_topic("topic-name", lambda: logger.info) See Also -------- dask.distributed.Client.unsubscribe_topic dask.distributed.Client.get_events dask.distributed.Client.log_event """ if topic in self._event_handlers: logger.info("Handler for %s already set. Overwriting.", topic) self._event_handlers[topic] = handler msg = {"op": "subscribe-topic", "topic": topic, "client": self.id} self._send_to_scheduler(msg) def unsubscribe_topic(self, topic): """Unsubscribe from a topic and remove event handler See Also -------- dask.distributed.Client.subscribe_topic dask.distributed.Client.get_events dask.distributed.Client.log_event """ if topic in self._event_handlers: msg = {"op": "unsubscribe-topic", "topic": topic, "client": self.id} self._send_to_scheduler(msg) else: raise ValueError(f"No event handler known for topic {topic}.") def retire_workers( self, workers: list[str] | None = None, close_workers: bool = True, **kwargs ): """Retire certain workers on the scheduler See :meth:`distributed.Scheduler.retire_workers` for the full docstring. Parameters ---------- workers close_workers **kwargs : dict Optional keyword arguments for the remote function Examples -------- You can get information about active workers using the following: >>> workers = client.scheduler_info()['workers'] From that list you may want to select some workers to close >>> client.retire_workers(workers=['tcp://address:port', ...]) See Also -------- dask.distributed.Scheduler.retire_workers """ return self.sync( self.scheduler.retire_workers, workers=workers, close_workers=close_workers, **kwargs, ) def set_metadata(self, key, value): """Set arbitrary metadata in the scheduler This allows you to store small amounts of data on the central scheduler process for administrative purposes. Data should be msgpack serializable (ints, strings, lists, dicts) If the key corresponds to a task then that key will be cleaned up when the task is forgotten by the scheduler. If the key is a list then it will be assumed that you want to index into a nested dictionary structure using those keys. For example if you call the following:: >>> client.set_metadata(['a', 'b', 'c'], 123) Then this is the same as setting >>> scheduler.task_metadata['a']['b']['c'] = 123 The lower level dictionaries will be created on demand. Examples -------- >>> client.set_metadata('x', 123) # doctest: +SKIP >>> client.get_metadata('x') # doctest: +SKIP 123 >>> client.set_metadata(['x', 'y'], 123) # doctest: +SKIP >>> client.get_metadata('x') # doctest: +SKIP {'y': 123} >>> client.set_metadata(['x', 'w', 'z'], 456) # doctest: +SKIP >>> client.get_metadata('x') # doctest: +SKIP {'y': 123, 'w': {'z': 456}} >>> client.get_metadata(['x', 'w']) # doctest: +SKIP {'z': 456} See Also -------- get_metadata """ if not isinstance(key, list): key = (key,) return self.sync(self.scheduler.set_metadata, keys=key, value=value) def get_versions( self, check: bool = False, packages: Sequence[str] | None = None ) -> VersionsDict | Coroutine[Any, Any, VersionsDict]: """Return version info for the scheduler, all workers and myself Parameters ---------- check raise ValueError if all required & optional packages do not match packages Extra package names to check Examples -------- >>> c.get_versions() # doctest: +SKIP >>> c.get_versions(packages=['sklearn', 'geopandas']) # doctest: +SKIP """ return self.sync(self._get_versions, check=check, packages=packages or []) async def _get_versions( self, check: bool = False, packages: Sequence[str] | None = None ) -> VersionsDict: packages = packages or [] client = version_module.get_versions(packages=packages) scheduler = await self.scheduler.versions(packages=packages) workers = await self.scheduler.broadcast( msg={"op": "versions", "packages": packages}, on_error="ignore", ) result = VersionsDict(scheduler=scheduler, workers=workers, client=client) if check: msg = version_module.error_message(scheduler, workers, client) if msg["warning"]: warnings.warn(msg["warning"]) if msg["error"]: raise ValueError(msg["error"]) return result def futures_of(self, futures): """Wrapper method of futures_of Parameters ---------- futures : tuple The futures """ return futures_of(futures, client=self) @classmethod def _expand_key(cls, k): """ Expand a user-provided task key specification, e.g. in a resources or retries dictionary. """ if not isinstance(k, tuple): k = (k,) for kk in k: if dask.is_dask_collection(kk): yield from kk.__dask_keys__() else: yield kk @staticmethod def collections_to_dsk(collections, *args, **kwargs): """Convert many collections into a single dask graph, after optimization""" return collections_to_dsk(collections, *args, **kwargs) async def _story(self, *keys_or_stimuli: str, on_error="raise"): assert on_error in ("raise", "ignore") try: flat_stories = await self.scheduler.get_story( keys_or_stimuli=keys_or_stimuli ) flat_stories = [("scheduler", *msg) for msg in flat_stories] except Exception: if on_error == "raise": raise elif on_error == "ignore": flat_stories = [] else: raise ValueError(f"on_error not in {'raise', 'ignore'}") responses = await self.scheduler.broadcast( msg={"op": "get_story", "keys_or_stimuli": keys_or_stimuli}, on_error=on_error, ) for worker, stories in responses.items(): flat_stories.extend((worker, *msg) for msg in stories) return flat_stories def story(self, *keys_or_stimuli, on_error="raise"): """Returns a cluster-wide story for the given keys or stimulus_id's""" return self.sync(self._story, *keys_or_stimuli, on_error=on_error) def get_task_stream( self, start=None, stop=None, count=None, plot=False, filename="task-stream.html", bokeh_resources=None, ): """Get task stream data from scheduler This collects the data present in the diagnostic "Task Stream" plot on the dashboard. It includes the start, stop, transfer, and deserialization time of every task for a particular duration. Note that the task stream diagnostic does not run by default. You may wish to call this function once before you start work to ensure that things start recording, and then again after you have completed. Parameters ---------- start : Number or string When you want to start recording If a number it should be the result of calling time() If a string then it should be a time difference before now, like '60s' or '500 ms' stop : Number or string When you want to stop recording count : int The number of desired records, ignored if both start and stop are specified plot : boolean, str If true then also return a Bokeh figure If plot == 'save' then save the figure to a file filename : str (optional) The filename to save to if you set ``plot='save'`` bokeh_resources : bokeh.resources.Resources (optional) Specifies if the resource component is INLINE or CDN Examples -------- >>> client.get_task_stream() # prime plugin if not already connected >>> x.compute() # do some work >>> client.get_task_stream() [{'task': ..., 'type': ..., 'thread': ..., ...}] Pass the ``plot=True`` or ``plot='save'`` keywords to get back a Bokeh figure >>> data, figure = client.get_task_stream(plot='save', filename='myfile.html') Alternatively consider the context manager >>> from dask.distributed import get_task_stream >>> with get_task_stream() as ts: ... x.compute() >>> ts.data [...] Returns ------- L: List[Dict] See Also -------- get_task_stream : a context manager version of this method """ return self.sync( self._get_task_stream, start=start, stop=stop, count=count, plot=plot, filename=filename, bokeh_resources=bokeh_resources, ) async def _get_task_stream( self, start=None, stop=None, count=None, plot=False, filename="task-stream.html", bokeh_resources=None, ): msgs = await self.scheduler.get_task_stream(start=start, stop=stop, count=count) if plot: from distributed.diagnostics.task_stream import rectangles rects = rectangles(msgs) from distributed.dashboard.components.scheduler import task_stream_figure source, figure = task_stream_figure(sizing_mode="stretch_both") source.data.update(rects) if plot == "save": from bokeh.plotting import output_file, save output_file(filename=filename, title="Dask Task Stream") save(figure, filename=filename, resources=bokeh_resources) return (msgs, figure) else: return msgs def register_plugin( self, plugin: NannyPlugin | SchedulerPlugin | WorkerPlugin, name: str | None = None, idempotent: bool | None = None, ): """Register a plugin. See https://distributed.readthedocs.io/en/latest/plugins.html Parameters ---------- plugin : A nanny, scheduler, or worker plugin to register. name : Name for the plugin; if None, a name is taken from the plugin instance or automatically generated if not present. idempotent : Do not re-register if a plugin of the given name already exists. If None, ``plugin.idempotent`` is taken if defined, False otherwise. """ if name is None: name = _get_plugin_name(plugin) assert name if idempotent is not None: warnings.warn( "The `idempotent` argument is deprecated and will be removed in a " "future version. Please mark your plugin as idempotent by setting its " "`.idempotent` attribute to `True`.", FutureWarning, stacklevel=2, ) else: idempotent = getattr(plugin, "idempotent", False) assert isinstance(idempotent, bool) return self._register_plugin(plugin, name, idempotent) @singledispatchmethod def _register_plugin( self, plugin: NannyPlugin | SchedulerPlugin | WorkerPlugin, name: str, idempotent: bool, ): if isinstance(plugin, type): raise TypeError("Please provide an instance of a plugin, not a type.") if any( "dask.distributed.diagnostics.plugin" in str(c) for c in plugin.__class__.__bases__ ): raise TypeError( "Importing plugin base classes from `dask.distributed.diagnostics.plugin` is not supported. " "Please import directly from `distributed.diagnostics.plugin` instead." ) raise TypeError( "Registering duck-typed plugins is not allowed. Please inherit from " "NannyPlugin, WorkerPlugin, or SchedulerPlugin to create a plugin." ) @_register_plugin.register def _(self, plugin: SchedulerPlugin, name: str, idempotent: bool): return self.sync( self._register_scheduler_plugin, plugin=plugin, name=name, idempotent=idempotent, ) @_register_plugin.register def _( self, plugin: NannyPlugin, name: str, idempotent: bool ) -> dict[str, OKMessage]: return self.sync( self._register_nanny_plugin, plugin=plugin, name=name, idempotent=idempotent, ) @_register_plugin.register def _(self, plugin: WorkerPlugin, name: str, idempotent: bool): return self.sync( self._register_worker_plugin, plugin=plugin, name=name, idempotent=idempotent, ) async def _register_scheduler_plugin( self, plugin: SchedulerPlugin, name: str, idempotent: bool ): return await self.scheduler.register_scheduler_plugin( plugin=dumps(plugin), name=name, idempotent=idempotent, ) def register_scheduler_plugin( self, plugin: SchedulerPlugin, name: str | None = None, idempotent: bool | None = None, ): """ Register a scheduler plugin. .. deprecated:: 2023.9.2 Use :meth:`Client.register_plugin` instead. See https://distributed.readthedocs.io/en/latest/plugins.html#scheduler-plugins Parameters ---------- plugin : SchedulerPlugin SchedulerPlugin instance to pass to the scheduler. name : str Name for the plugin; if None, a name is taken from the plugin instance or automatically generated if not present. idempotent : bool Do not re-register if a plugin of the given name already exists. """ warnings.warn( "`Client.register_scheduler_plugin` has been deprecated; " "please `Client.register_plugin` instead", DeprecationWarning, stacklevel=2, ) return cast(OKMessage, self.register_plugin(plugin, name, idempotent)) async def _unregister_scheduler_plugin(self, name): return await self.scheduler.unregister_scheduler_plugin(name=name) def unregister_scheduler_plugin(self, name): """Unregisters a scheduler plugin See https://distributed.readthedocs.io/en/latest/plugins.html#scheduler-plugins Parameters ---------- name : str Name of the plugin to unregister. See the :meth:`Client.register_scheduler_plugin` docstring for more information. Examples -------- >>> class MyPlugin(SchedulerPlugin): ... def __init__(self, *args, **kwargs): ... pass # the constructor is up to you ... async def start(self, scheduler: Scheduler) -> None: ... pass ... async def before_close(self) -> None: ... pass ... async def close(self) -> None: ... pass ... def restart(self, scheduler: Scheduler) -> None: ... pass >>> plugin = MyPlugin(1, 2, 3) >>> client.register_plugin(plugin, name='foo') >>> client.unregister_scheduler_plugin(name='foo') See Also -------- register_scheduler_plugin """ return self.sync(self._unregister_scheduler_plugin, name=name) def register_worker_callbacks(self, setup=None): """ Registers a setup callback function for all current and future workers. This registers a new setup function for workers in this cluster. The function will run immediately on all currently connected workers. It will also be run upon connection by any workers that are added in the future. Multiple setup functions can be registered - these will be called in the order they were added. If the function takes an input argument named ``dask_worker`` then that variable will be populated with the worker itself. Parameters ---------- setup : callable(dask_worker: Worker) -> None Function to register and run on all workers """ return self.register_plugin(_WorkerSetupPlugin(setup)) async def _register_worker_plugin( self, plugin: WorkerPlugin, name: str, idempotent: bool ) -> dict[str, OKMessage]: responses = await self.scheduler.register_worker_plugin( plugin=dumps(plugin), name=name, idempotent=idempotent ) for response in responses.values(): if response["status"] == "error": _, exc, tb = clean_exception( response["exception"], response["traceback"] ) assert exc raise exc.with_traceback(tb) return cast(dict[str, OKMessage], responses) async def _register_nanny_plugin( self, plugin: NannyPlugin, name: str, idempotent: bool ) -> dict[str, OKMessage]: responses = await self.scheduler.register_nanny_plugin( plugin=dumps(plugin), name=name, idempotent=idempotent ) for response in responses.values(): if response["status"] == "error": _, exc, tb = clean_exception( response["exception"], response["traceback"] ) assert exc raise exc.with_traceback(tb) return cast(dict[str, OKMessage], responses) def register_worker_plugin( self, plugin: NannyPlugin | WorkerPlugin, name: str | None = None, nanny: bool | None = None, ): """ Registers a lifecycle worker plugin for all current and future workers. .. deprecated:: 2023.9.2 Use :meth:`Client.register_plugin` instead. This registers a new object to handle setup, task state transitions and teardown for workers in this cluster. The plugin will instantiate itself on all currently connected workers. It will also be run on any worker that connects in the future. The plugin may include methods ``setup``, ``teardown``, ``transition``, and ``release_key``. See the ``dask.distributed.WorkerPlugin`` class or the examples below for the interface and docstrings. It must be serializable with the pickle or cloudpickle modules. If the plugin has a ``name`` attribute, or if the ``name=`` keyword is used then that will control idempotency. If a plugin with that name has already been registered, then it will be removed and replaced by the new one. For alternatives to plugins, you may also wish to look into preload scripts. Parameters ---------- plugin : WorkerPlugin or NannyPlugin WorkerPlugin or NannyPlugin instance to register. name : str, optional A name for the plugin. Registering a plugin with the same name will have no effect. If plugin has no name attribute a random name is used. nanny : bool, optional Whether to register the plugin with workers or nannies. Examples -------- >>> class MyPlugin(WorkerPlugin): ... def __init__(self, *args, **kwargs): ... pass # the constructor is up to you ... def setup(self, worker: dask.distributed.Worker): ... pass ... def teardown(self, worker: dask.distributed.Worker): ... pass ... def transition(self, key: str, start: str, finish: str, ... **kwargs): ... pass ... def release_key(self, key: str, state: str, cause: str | None, reason: None, report: bool): ... pass >>> plugin = MyPlugin(1, 2, 3) >>> client.register_plugin(plugin) You can get access to the plugin with the ``get_worker`` function >>> client.register_plugin(other_plugin, name='my-plugin') >>> def f(): ... worker = get_worker() ... plugin = worker.plugins['my-plugin'] ... return plugin.my_state >>> future = client.run(f) See Also -------- distributed.WorkerPlugin unregister_worker_plugin """ warnings.warn( "`Client.register_worker_plugin` has been deprecated; " "please use `Client.register_plugin` instead", DeprecationWarning, stacklevel=2, ) if name is None: name = _get_plugin_name(plugin) assert name method: Callable if isinstance(plugin, WorkerPlugin): method = self._register_worker_plugin if nanny is True: warnings.warn( "Registering a `WorkerPlugin` as a nanny plugin is not " "allowed, registering as a worker plugin instead. " "To register as a nanny plugin, inherit from `NannyPlugin`.", UserWarning, stacklevel=2, ) elif isinstance(plugin, NannyPlugin): method = self._register_nanny_plugin if nanny is False: warnings.warn( "Registering a `NannyPlugin` as a worker plugin is not " "allowed, registering as a nanny plugin instead. " "To register as a worker plugin, inherit from `WorkerPlugin`.", UserWarning, stacklevel=2, ) elif isinstance(plugin, SchedulerPlugin): # type: ignore[unreachable] if nanny: warnings.warn( "Registering a `SchedulerPlugin` as a nanny plugin is not " "allowed, registering as a scheduler plugin instead. " "To register as a nanny plugin, inherit from `NannyPlugin`.", UserWarning, stacklevel=2, ) else: warnings.warn( "Registering a `SchedulerPlugin` as a worker plugin is not " "allowed, registering as a scheduler plugin instead. " "To register as a worker plugin, inherit from `WorkerPlugin`.", UserWarning, stacklevel=2, ) method = self._register_scheduler_plugin else: warnings.warn( "Registering duck-typed plugins has been deprecated. " "Please make sure your plugin inherits from `NannyPlugin` " "or `WorkerPlugin`.", DeprecationWarning, stacklevel=2, ) if nanny is True: method = self._register_nanny_plugin else: method = self._register_worker_plugin return self.sync(method, plugin=plugin, name=name, idempotent=False) async def _unregister_worker_plugin(self, name, nanny=None): if nanny: responses = await self.scheduler.unregister_nanny_plugin(name=name) else: responses = await self.scheduler.unregister_worker_plugin(name=name) for response in responses.values(): if response["status"] == "error": _, exc, tb = clean_exception(**response) raise exc.with_traceback(tb) return responses def unregister_worker_plugin(self, name, nanny=None): """Unregisters a lifecycle worker plugin This unregisters an existing worker plugin. As part of the unregistration process the plugin's ``teardown`` method will be called. Parameters ---------- name : str Name of the plugin to unregister. See the :meth:`Client.register_plugin` docstring for more information. Examples -------- >>> class MyPlugin(WorkerPlugin): ... def __init__(self, *args, **kwargs): ... pass # the constructor is up to you ... def setup(self, worker: dask.distributed.Worker): ... pass ... def teardown(self, worker: dask.distributed.Worker): ... pass ... def transition(self, key: str, start: str, finish: str, **kwargs): ... pass ... def release_key(self, key: str, state: str, cause: str | None, reason: None, report: bool): ... pass >>> plugin = MyPlugin(1, 2, 3) >>> client.register_plugin(plugin, name='foo') >>> client.unregister_worker_plugin(name='foo') See Also -------- register_plugin """ return self.sync(self._unregister_worker_plugin, name=name, nanny=nanny) @property def amm(self): """Convenience accessors for the :doc:`active_memory_manager`""" from distributed.active_memory_manager import AMMClientProxy return AMMClientProxy(self) def _handle_forwarded_log_record(self, event): _, record_attrs = event record = logging.makeLogRecord(record_attrs) dest_logger = logging.getLogger(record.name) dest_logger.handle(record) def forward_logging(self, logger_name=None, level=logging.NOTSET): """ Begin forwarding the given logger (by default the root) and all loggers under it from worker tasks to the client process. Whenever the named logger handles a LogRecord on the worker-side, the record will be serialized, sent to the client, and handled by the logger with the same name on the client-side. Note that worker-side loggers will only handle LogRecords if their level is set appropriately, and the client-side logger will only emit the forwarded LogRecord if its own level is likewise set appropriately. For example, if your submitted task logs a DEBUG message to logger "foo", then in order for ``forward_logging()`` to cause that message to be emitted in your client session, you must ensure that the logger "foo" have its level set to DEBUG (or lower) in the worker process *and* in the client process. Parameters ---------- logger_name : str, optional The name of the logger to begin forwarding. The usual rules of the ``logging`` module's hierarchical naming system apply. For example, if ``name`` is ``"foo"``, then not only ``"foo"``, but also ``"foo.bar"``, ``"foo.baz"``, etc. will be forwarded. If ``name`` is ``None``, this indicates the root logger, and so *all* loggers will be forwarded. Note that a logger will only forward a given LogRecord if the logger's level is sufficient for the LogRecord to be handled at all. level : str | int, optional Optionally restrict forwarding to LogRecords of this level or higher, even if the forwarded logger's own level is lower. Examples -------- For purposes of the examples, suppose we configure client-side logging as a user might: with a single StreamHandler attached to the root logger with an output level of INFO and a simple output format:: import logging import distributed import io, yaml TYPICAL_LOGGING_CONFIG = ''' version: 1 handlers: console: class : logging.StreamHandler formatter: default level : INFO formatters: default: format: '%(asctime)s %(levelname)-8s [worker %(worker)s] %(name)-15s %(message)s' datefmt: '%Y-%m-%d %H:%M:%S' root: handlers: - console ''' config = yaml.safe_load(io.StringIO(TYPICAL_LOGGING_CONFIG)) logging.config.dictConfig(config) Now create a client and begin forwarding the root logger from workers back to our local client process. >>> client = distributed.Client() >>> client.forward_logging() # forward the root logger at any handled level Then submit a task that does some error logging on a worker. We see output from the client-side StreamHandler. >>> def do_error(): ... logging.getLogger("user.module").error("Hello error") ... return 42 >>> client.submit(do_error).result() 2022-11-09 03:43:25 ERROR [worker tcp://127.0.0.1:34783] user.module Hello error 42 Note how an attribute ``"worker"`` is also added by dask to the forwarded LogRecord, which our custom formatter uses. This is useful for identifying exactly which worker logged the error. One nuance worth highlighting: even though our client-side root logger is configured with a level of INFO, the worker-side root loggers still have their default level of ERROR because we haven't done any explicit logging configuration on the workers. Therefore worker-side INFO logs will *not* be forwarded because they never even get handled in the first place. >>> def do_info_1(): ... # no output on the client side ... logging.getLogger("user.module").info("Hello info the first time") ... return 84 >>> client.submit(do_info_1).result() 84 It is necessary to set the client-side logger's level to INFO before the info message will be handled and forwarded to the client. In other words, the "effective" level of the client-side forwarded logging is the maximum of each logger's client-side and worker-side levels. >>> def do_info_2(): ... logger = logging.getLogger("user.module") ... logger.setLevel(logging.INFO) ... # now produces output on the client side ... logger.info("Hello info the second time") ... return 84 >>> client.submit(do_info_2).result() 2022-11-09 03:57:39 INFO [worker tcp://127.0.0.1:42815] user.module Hello info the second time 84 """ plugin_name = f"forward-logging-{logger_name or ''}" topic = f"{TOPIC_PREFIX_FORWARDED_LOG_RECORD}-{plugin_name}" # note that subscription is idempotent self.subscribe_topic(topic, self._handle_forwarded_log_record) # note that any existing plugin with the same name will automatically be # removed and torn down (see distributed.worker.Worker.plugin_add()), so # this is effectively idempotent, i.e., forwarding the same logger twice # won't cause every LogRecord to be forwarded twice return self.register_plugin( ForwardLoggingPlugin(logger_name, level, topic), plugin_name ) def unforward_logging(self, logger_name=None): """ Stop forwarding the given logger (default root) from worker tasks to the client process. """ plugin_name = f"forward-logging-{logger_name or ''}" topic = f"{TOPIC_PREFIX_FORWARDED_LOG_RECORD}-{plugin_name}" self.unsubscribe_topic(topic) return self.unregister_worker_plugin(plugin_name) def _convert_dask_keys(keys: NestedKeys) -> List: assert isinstance(keys, list) new_keys: list[List | TaskRef] = [] for key in keys: if isinstance(key, list): new_keys.append(_convert_dask_keys(key)) else: new_keys.append(TaskRef(key)) return List(*new_keys) class _WorkerSetupPlugin(WorkerPlugin): """This is used to support older setup functions as callbacks""" def __init__(self, setup): self._setup = setup def setup(self, worker): if has_keyword(self._setup, "dask_worker"): return self._setup(dask_worker=worker) else: return self._setup() def CompatibleExecutor(*args, **kwargs): raise Exception("This has been moved to the Client.get_executor() method") ALL_COMPLETED = "ALL_COMPLETED" FIRST_COMPLETED = "FIRST_COMPLETED" async def _wait(fs, timeout=None, return_when=ALL_COMPLETED): if timeout is not None and not isinstance(timeout, Number): raise TypeError( "timeout= keyword received a non-numeric value.\n" "Beware that wait expects a list of values\n" " Bad: wait(x, y, z)\n" " Good: wait([x, y, z])" ) fs = futures_of(fs) if return_when == ALL_COMPLETED: future = distributed.utils.All({f._state.wait() for f in fs}) elif return_when == FIRST_COMPLETED: future = distributed.utils.Any({f._state.wait() for f in fs}) else: raise NotImplementedError( "Only return_when='ALL_COMPLETED' and 'FIRST_COMPLETED' are supported" ) if timeout is not None: future = wait_for(future, timeout) await future done, not_done = ( {fu for fu in fs if fu.status != "pending"}, {fu for fu in fs if fu.status == "pending"}, ) cancelled_errors = defaultdict(list) for f in done: if not f.cancelled(): continue exception = f._state.exception assert isinstance(exception, FutureCancelledError) cancelled_errors[exception.reason].append(exception) if cancelled_errors: groups = [ CancelledFuturesGroup(errors=errors, reason=reason) for reason, errors in cancelled_errors.items() ] raise FuturesCancelledError(groups) return DoneAndNotDoneFutures(done, not_done) def wait(fs, timeout=None, return_when=ALL_COMPLETED): """Wait until all/any futures are finished Parameters ---------- fs : List[Future] timeout : number, string, optional Time after which to raise a ``dask.distributed.TimeoutError``. Can be a string like ``"10 minutes"`` or a number of seconds to wait. return_when : str, optional One of `ALL_COMPLETED` or `FIRST_COMPLETED` Returns ------- Named tuple of completed, not completed """ if timeout is not None and isinstance(timeout, (Number, str)): timeout = parse_timedelta(timeout, default="s") client = default_client() result = client.sync(_wait, fs, timeout=timeout, return_when=return_when) return result async def _as_completed(fs, queue): fs = futures_of(fs) groups = groupby(lambda f: f.key, fs) firsts = [v[0] for v in groups.values()] wait_iterator = gen.WaitIterator( *map(asyncio.ensure_future, [f._state.wait() for f in firsts]) ) while not wait_iterator.done(): await wait_iterator.next() # TODO: handle case of restarted futures future = firsts[wait_iterator.current_index] for f in groups[future.key]: queue.put_nowait(f) async def _first_completed(futures): """Return a single completed future See Also: _as_completed """ q = asyncio.Queue() await _as_completed(futures, q) result = await q.get() return result class as_completed: """ Return futures in the order in which they complete This returns an iterator that yields the input future objects in the order in which they complete. Calling ``next`` on the iterator will block until the next future completes, irrespective of order. Additionally, you can also add more futures to this object during computation with the ``.add`` method Parameters ---------- futures: Collection of futures A list of Future objects to be iterated over in the order in which they complete with_results: bool (False) Whether to wait and include results of futures as well; in this case ``as_completed`` yields a tuple of (future, result) raise_errors: bool (True) Whether we should raise when the result of a future raises an exception; only affects behavior when ``with_results=True``. timeout: int (optional) The returned iterator raises a ``dask.distributed.TimeoutError`` if ``__next__()`` or ``__anext__()`` is called and the result isn't available after timeout seconds from the original call to ``as_completed()``. If timeout is not specified or ``None``, there is no limit to the wait time. Examples -------- >>> x, y, z = client.map(inc, [1, 2, 3]) # doctest: +SKIP >>> for future in as_completed([x, y, z]): # doctest: +SKIP ... print(future.result()) # doctest: +SKIP 3 2 4 Add more futures during computation >>> x, y, z = client.map(inc, [1, 2, 3]) # doctest: +SKIP >>> ac = as_completed([x, y, z]) # doctest: +SKIP >>> for future in ac: # doctest: +SKIP ... print(future.result()) # doctest: +SKIP ... if random.random() < 0.5: # doctest: +SKIP ... ac.add(c.submit(double, future)) # doctest: +SKIP 4 2 8 3 6 12 24 Optionally wait until the result has been gathered as well >>> ac = as_completed([x, y, z], with_results=True) # doctest: +SKIP >>> for future, result in ac: # doctest: +SKIP ... print(result) # doctest: +SKIP 2 4 3 """ def __init__( self, futures=None, loop=None, with_results=False, raise_errors=True, *, timeout=None, ): if futures is None: futures = [] self.futures = defaultdict(int) self.queue = pyQueue() self.lock = threading.Lock() self.loop = loop or default_client().loop self.thread_condition = threading.Condition() self.with_results = with_results self.raise_errors = raise_errors self._deadline = Deadline.after(parse_timedelta(timeout)) if futures: self.update(futures) @property def condition(self): try: return self._condition except AttributeError: self._condition = asyncio.Condition() return self._condition async def _track_future(self, future): try: await _wait(future) except CancelledError: pass if self.with_results: try: result = await future._result(raiseit=False) except CancelledError as exc: result = exc with self.lock: if future in self.futures: self.futures[future] -= 1 if not self.futures[future]: del self.futures[future] if self.with_results: self.queue.put_nowait((future, result)) else: self.queue.put_nowait(future) async with self.condition: self.condition.notify() with self.thread_condition: self.thread_condition.notify() def update(self, futures): """Add multiple futures to the collection. The added futures will emit from the iterator once they finish""" from distributed.actor import BaseActorFuture with self.lock: for f in futures: if not isinstance(f, (Future, BaseActorFuture)): raise TypeError("Input must be a future, got %s" % f) self.futures[f] += 1 self.loop.add_callback(self._track_future, f) def add(self, future): """Add a future to the collection This future will emit from the iterator once it finishes """ self.update((future,)) def is_empty(self): """Returns True if there no completed or computing futures""" return not self.count() def has_ready(self): """Returns True if there are completed futures available.""" return not self.queue.empty() def count(self): """Return the number of futures yet to be returned This includes both the number of futures still computing, as well as those that are finished, but have not yet been returned from this iterator. """ with self.lock: return len(self.futures) + len(self.queue.queue) def __repr__(self): return "".format( len(self.futures), len(self.queue.queue) ) def __iter__(self): return self def __aiter__(self): return self def _get_and_raise(self): res = self.queue.get() if self.with_results: future, result = res if self.raise_errors and future.status == "error": typ, exc, tb = result raise exc.with_traceback(tb) return res def __next__(self): while self.queue.empty(): if self._deadline.expired: raise TimeoutError() if self.is_empty(): raise StopIteration() with self.thread_condition: self.thread_condition.wait(timeout=0.100) return self._get_and_raise() async def __anext__(self): if not self._deadline.expires: return await self._anext() return await wait_for(self._anext(), self._deadline.remaining) async def _anext(self): if not self.futures and self.queue.empty(): raise StopAsyncIteration while self.queue.empty(): if not self.futures: raise StopAsyncIteration async with self.condition: await self.condition.wait() return self._get_and_raise() next = __next__ def next_batch(self, block=True): """Get the next batch of completed futures. Parameters ---------- block : bool, optional If True then wait until we have some result, otherwise return immediately, even with an empty list. Defaults to True. Examples -------- >>> ac = as_completed(futures) # doctest: +SKIP >>> client.gather(ac.next_batch()) # doctest: +SKIP [4, 1, 3] >>> client.gather(ac.next_batch(block=False)) # doctest: +SKIP [] Returns ------- List of futures or (future, result) tuples """ if block: batch = [next(self)] else: batch = [] while not self.queue.empty(): batch.append(self.queue.get()) return batch def batches(self): """ Yield all finished futures at once rather than one-by-one This returns an iterator of lists of futures or lists of (future, result) tuples rather than individual futures or individual (future, result) tuples. It will yield these as soon as possible without waiting. Examples -------- >>> for batch in as_completed(futures).batches(): # doctest: +SKIP ... results = client.gather(batch) ... print(results) [4, 2] [1, 3, 7] [5] [6] """ while True: try: yield self.next_batch(block=True) except StopIteration: return def clear(self): """Clear out all submitted futures""" with self.lock: self.futures.clear() while not self.queue.empty(): self.queue.get() def AsCompleted(*args, **kwargs): raise Exception("This has moved to as_completed") def default_client(c=None): """Return a client if one has started Parameters ---------- c : Client The client to return. If None, the default client is returned. Returns ------- c : Client The client, if one has started See also -------- Client.current (alias) """ c = c or _get_global_client() if c: return c else: raise ValueError( "No clients found\n" "Start a client and point it to the scheduler address\n" " from distributed import Client\n" " client = Client('ip-addr-of-scheduler:8786')\n" ) def ensure_default_client(client): """Ensures the client passed as argument is set as the default Parameters ---------- client : Client The client """ _set_global_client(client) def redict_collection(c, dsk): """Change the dictionary in the collection Parameters ---------- c : collection The collection dsk : dict The dictionary Returns ------- c : Delayed If the collection is a 'Delayed' object the collection is returned cc : collection If the collection is not a 'Delayed' object a copy of the collection with xthe new dictionary is returned """ from dask.delayed import Delayed if isinstance(c, Delayed): return Delayed(c.key, dsk) else: cc = copy.copy(c) cc.dask = dsk return cc def futures_of(o, client=None): """Future objects in a collection Parameters ---------- o : collection A possibly nested collection of Dask objects client : Client, optional The client Examples -------- >>> futures_of(my_dask_dataframe) [, ] Raises ------ CancelledError If one of the futures is cancelled a CancelledError is raised Returns ------- futures : List[Future] A list of futures held by those collections """ stack = [o] seen = set() futures = list() while stack: x = stack.pop() if type(x) in (tuple, set, list): stack.extend(x) elif type(x) is dict: stack.extend(x.values()) elif type(x) is SubgraphCallable: stack.extend(x.dsk.values()) elif isinstance(x, TaskRef): if x not in seen: seen.add(x) futures.append(x) elif dask.is_dask_collection(x): stack.extend(x.__dask_graph__().values()) if client is not None: cancelled_errors = defaultdict(list) for f in futures: if not f.cancelled(): continue exception = f._state.exception assert isinstance(exception, FutureCancelledError) cancelled_errors[exception.reason].append(exception) if cancelled_errors: groups = [ CancelledFuturesGroup(errors=errors, reason=reason) for reason, errors in cancelled_errors.items() ] raise FuturesCancelledError(groups) return futures[::-1] def fire_and_forget(obj): """Run tasks at least once, even if we release the futures Under normal operation Dask will not run any tasks for which there is not an active future (this avoids unnecessary work in many situations). However sometimes you want to just fire off a task, not track its future, and expect it to finish eventually. You can use this function on a future or collection of futures to ask Dask to complete the task even if no active client is tracking it. The results will not be kept in memory after the task completes (unless there is an active future) so this is only useful for tasks that depend on side effects. Parameters ---------- obj : Future, list, dict, dask collection The futures that you want to run at least once Examples -------- >>> fire_and_forget(client.submit(func, *args)) # doctest: +SKIP """ futures = futures_of(obj) for future in futures: future.client._send_to_scheduler( { "op": "client-desires-keys", "keys": [future.key], "client": "fire-and-forget", } ) class get_task_stream: """ Collect task stream within a context block This provides diagnostic information about every task that was run during the time when this block was active. This must be used as a context manager. Parameters ---------- plot: boolean, str If true then also return a Bokeh figure If plot == 'save' then save the figure to a file filename: str (optional) The filename to save to if you set ``plot='save'`` Examples -------- >>> with get_task_stream() as ts: ... x.compute() >>> ts.data [...] Get back a Bokeh figure and optionally save to a file >>> with get_task_stream(plot='save', filename='task-stream.html') as ts: ... x.compute() >>> ts.figure To share this file with others you may wish to upload and serve it online. A common way to do this is to upload the file as a gist, and then serve it on https://raw.githack.com :: $ python -m pip install gist $ gist task-stream.html https://gist.github.com/8a5b3c74b10b413f612bb5e250856ceb You can then navigate to that site, click the "Raw" button to the right of the ``task-stream.html`` file, and then provide that URL to https://raw.githack.com . This process should provide a sharable link that others can use to see your task stream plot. See Also -------- Client.get_task_stream: Function version of this context manager """ def __init__(self, client=None, plot=False, filename="task-stream.html"): self.data = [] self._plot = plot self._filename = filename self.figure = None self.client = client or default_client() self.client.get_task_stream(start=0, stop=0) # ensure plugin def __enter__(self): self.start = time() return self def __exit__(self, exc_type, exc_value, traceback): L = self.client.get_task_stream( start=self.start, plot=self._plot, filename=self._filename ) if self._plot: L, self.figure = L self.data.extend(L) async def __aenter__(self): return self async def __aexit__(self, exc_type, exc_value, traceback): L = await self.client.get_task_stream( start=self.start, plot=self._plot, filename=self._filename ) if self._plot: L, self.figure = L self.data.extend(L) class performance_report: """Gather performance report This creates a static HTML file that includes many of the same plots of the dashboard for later viewing. The resulting file uses JavaScript, and so must be viewed with a web browser. Locally we recommend using ``python -m http.server`` or hosting the file live online. Parameters ---------- filename: str, optional The filename to save the performance report locally stacklevel: int, optional The code execution frame utilized for populating the Calling Code section of the report. Defaults to `1` which is the frame calling ``performance_report`` mode: str, optional Mode parameter to pass to :func:`bokeh.io.output.output_file`. Defaults to ``None``. storage_options: dict, optional Any additional arguments to :func:`fsspec.open` when writing to a URL. Examples -------- >>> with performance_report(filename="myfile.html", stacklevel=1): ... x.compute() """ def __init__( self, filename="dask-report.html", stacklevel=1, mode=None, storage_options=None ): self.filename = filename # stacklevel 0 or less - shows dask internals which likely isn't helpful self._stacklevel = stacklevel if stacklevel > 0 else 1 self.mode = mode self.storage_options = storage_options or {} async def __aenter__(self): self.start = time() self.last_count = await get_client().run_on_scheduler( lambda dask_scheduler: dask_scheduler.monitor.count ) await get_client().get_task_stream(start=0, stop=0) # ensure plugin async def __aexit__(self, exc_type, exc_value, traceback, code=None): import fsspec client = get_client() if code is None: frames = client._get_computation_code(self._stacklevel + 1, nframes=1) code = frames[0].code if frames else "" data = await client.scheduler.performance_report( start=self.start, last_count=self.last_count, code=code, mode=self.mode ) with fsspec.open( self.filename, mode="w", compression="infer", **self.storage_options ) as f: f.write(data) def __enter__(self): get_client().sync(self.__aenter__) def __exit__(self, exc_type, exc_value, traceback): client = get_client() frames = client._get_computation_code(self._stacklevel + 1, nframes=1) code = frames[0].code if frames else "" client.sync(self.__aexit__, exc_type, exc_value, traceback, code=code) class get_task_metadata: """Collect task metadata within a context block This gathers ``TaskState`` metadata and final state from the scheduler for tasks which are submitted and finished within the scope of this context manager. Examples -------- >>> with get_task_metadata() as tasks: ... x.compute() >>> tasks.metadata {...} >>> tasks.state {...} """ def __init__(self): self.name = f"task-metadata-{uuid.uuid4().hex}" self.keys = set() self.metadata = None self.state = None async def __aenter__(self): await get_client().scheduler.start_task_metadata(name=self.name) return self async def __aexit__(self, exc_type, exc_value, traceback): response = await get_client().scheduler.stop_task_metadata(name=self.name) self.metadata = response["metadata"] self.state = response["state"] def __enter__(self): return get_client().sync(self.__aenter__) def __exit__(self, exc_type, exc_value, traceback): return get_client().sync(self.__aexit__, exc_type, exc_value, traceback) @contextmanager def temp_default_client(c): """Set the default client for the duration of the context .. note:: This function should be used exclusively for unit testing the default client functionality. In all other cases, please use ``Client.as_current`` instead. .. note:: Unlike ``Client.as_current``, this context manager is neither thread-local nor task-local. Parameters ---------- c : Client This is what default_client() will return within the with-block. """ old_exec = default_client() _set_global_client(c) try: with c.as_current(): yield finally: _set_global_client(old_exec) def _close_global_client(): """ Force close of global client. This cleans up when a client wasn't close explicitly, e.g. interactive sessions. """ c = _get_global_client() if c is not None: c._should_close_loop = False with suppress(TimeoutError, RuntimeError): if c.asynchronous: c.loop.add_callback(c.close, timeout=3) else: c.close(timeout=3) def get_collections_metadata(collection): return { "type": type(collection).__name__, } atexit.register(_close_global_client)