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 0028:     merge_with,

 0029:     valmap,

 0030:     second,

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 0033:     concat,

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 0267:     """

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 0272:        This worker's unique key.  This can be its connected address

 0273:        (such as ``'tcp://127.0.0.1:8891'``) or an alias (such as ``'alice'``).

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 0275:     .. attribute:: processing: {TaskState: cost}

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 0277:        A dictionary of tasks that have been submitted to this worker.

 0278:        Each task state is asssociated with the expected cost in seconds

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 0282:        Multiple tasks may be submitted to a worker in advance and the worker

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 0286:        All the tasks here are in the "processing" state.

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 0288:        This attribute is kept in sync with :attr:`TaskState.processing_on`.

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 0290:     .. attribute:: executing: {TaskState: duration}

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 0292:        A dictionary of tasks that are currently being run on this worker.

 0293:        Each task state is asssociated with the duration in seconds which

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 0296:     .. attribute:: has_what: {TaskState}

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 0298:        The set of tasks which currently reside on this worker.

 0299:        All the tasks here are in the "memory" state.

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 0301:        This is the reverse mapping of :class:`TaskState.who_has`.

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 0310:        The number of CPU threads made available on this worker.

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 0312:     .. attribute:: resources: {str: Number}

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 0314:        The available resources on this worker like ``{'gpu': 2}``.

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 0318:     .. attribute:: used_resources: {str: Number}

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 0320:        The sum of each resource used by all tasks allocated to this worker.

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 0324:     .. attribute:: occupancy: double

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 0326:        The total expected runtime, in seconds, of all tasks currently

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 0330:     .. attribute:: status: str

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 0332:        The current status of the worker, either ``'running'`` or ``'closed'``

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 0343:     .. attribute:: actors: {TaskState}

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 0867: 

 0868: 

 0869: @final

 0870: @cclass

+0871: class TaskState:

struct __pyx_obj_11distributed_9scheduler_TaskState {
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  struct __pyx_vtabstruct_11distributed_9scheduler_TaskState *__pyx_vtab;
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  PyObject *_run_spec;
  PyObject *_priority;
  PyObject *_state;
  PyObject *_dependencies;
  PyObject *_dependents;
  int _has_lost_dependencies;
  PyObject *_waiting_on;
  PyObject *_waiters;
  PyObject *_who_wants;
  PyObject *_who_has;
  struct __pyx_obj_11distributed_9scheduler_WorkerState *_processing_on;
  Py_ssize_t _retries;
  Py_ssize_t _nbytes;
  PyObject *_type;
  PyObject *_exception;
  PyObject *_traceback;
  PyObject *_exception_blame;
  Py_ssize_t _suspicious;
  PyObject *_host_restrictions;
  PyObject *_worker_restrictions;
  PyObject *_resource_restrictions;
  int _loose_restrictions;
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static struct __pyx_vtabstruct_11distributed_9scheduler_TaskState *__pyx_vtabptr_11distributed_9scheduler_TaskState;
static PyObject *__pyx_f_11distributed_9scheduler_9TaskState_add_dependency(struct __pyx_obj_11distributed_9scheduler_TaskState *, struct __pyx_obj_11distributed_9scheduler_TaskState *, int __pyx_skip_dispatch);
static CYTHON_INLINE Py_ssize_t __pyx_f_11distributed_9scheduler_9TaskState_get_nbytes(struct __pyx_obj_11distributed_9scheduler_TaskState *, int __pyx_skip_dispatch);
static PyObject *__pyx_f_11distributed_9scheduler_9TaskState_set_nbytes(struct __pyx_obj_11distributed_9scheduler_TaskState *, Py_ssize_t, int __pyx_skip_dispatch);
static PyObject *__pyx_f_11distributed_9scheduler_9TaskState_validate(struct __pyx_obj_11distributed_9scheduler_TaskState *, int __pyx_skip_dispatch);

 0872:     """

 0873:     A simple object holding information about a task.

 0874: 

 0875:     .. attribute:: key: str

 0876: 

 0877:        The key is the unique identifier of a task, generally formed

 0878:        from the name of the function, followed by a hash of the function

 0879:        and arguments, like ``'inc-ab31c010444977004d656610d2d421ec'``.

 0880: 

 0881:     .. attribute:: prefix: TaskPrefix

 0882: 

 0883:        The broad class of tasks to which this task belongs like "inc" or

 0884:        "read_csv"

 0885: 

 0886:     .. attribute:: run_spec: object

 0887: 

 0888:        A specification of how to run the task.  The type and meaning of this

 0889:        value is opaque to the scheduler, as it is only interpreted by the

 0890:        worker to which the task is sent for executing.

 0891: 

 0892:        As a special case, this attribute may also be ``None``, in which case

 0893:        the task is "pure data" (such as, for example, a piece of data loaded

 0894:        in the scheduler using :meth:`Client.scatter`).  A "pure data" task

 0895:        cannot be computed again if its value is lost.

 0896: 

 0897:     .. attribute:: priority: tuple

 0898: 

 0899:        The priority provides each task with a relative ranking which is used

 0900:        to break ties when many tasks are being considered for execution.

 0901: 

 0902:        This ranking is generally a 2-item tuple.  The first (and dominant)

 0903:        item corresponds to when it was submitted.  Generally, earlier tasks

 0904:        take precedence.  The second item is determined by the client, and is

 0905:        a way to prioritize tasks within a large graph that may be important,

 0906:        such as if they are on the critical path, or good to run in order to

 0907:        release many dependencies.  This is explained further in

 0908:        :doc:`Scheduling Policy <scheduling-policies>`.

 0909: 

 0910:     .. attribute:: state: str

 0911: 

 0912:        This task's current state.  Valid states include ``released``,

 0913:        ``waiting``, ``no-worker``, ``processing``, ``memory``, ``erred``

 0914:        and ``forgotten``.  If it is ``forgotten``, the task isn't stored

 0915:        in the ``tasks`` dictionary anymore and will probably disappear

 0916:        soon from memory.

 0917: 

 0918:     .. attribute:: dependencies: {TaskState}

 0919: 

 0920:        The set of tasks this task depends on for proper execution.  Only

 0921:        tasks still alive are listed in this set.  If, for whatever reason,

 0922:        this task also depends on a forgotten task, the

 0923:        :attr:`has_lost_dependencies` flag is set.

 0924: 

 0925:        A task can only be executed once all its dependencies have already

 0926:        been successfully executed and have their result stored on at least

 0927:        one worker.  This is tracked by progressively draining the

 0928:        :attr:`waiting_on` set.

 0929: 

 0930:     .. attribute:: dependents: {TaskState}

 0931: 

 0932:        The set of tasks which depend on this task.  Only tasks still alive

 0933:        are listed in this set.

 0934: 

 0935:        This is the reverse mapping of :attr:`dependencies`.

 0936: 

 0937:     .. attribute:: has_lost_dependencies: bool

 0938: 

 0939:        Whether any of the dependencies of this task has been forgotten.

 0940:        For memory consumption reasons, forgotten tasks are not kept in

 0941:        memory even though they may have dependent tasks.  When a task is

 0942:        forgotten, therefore, each of its dependents has their

 0943:        :attr:`has_lost_dependencies` attribute set to ``True``.

 0944: 

 0945:        If :attr:`has_lost_dependencies` is true, this task cannot go

 0946:        into the "processing" state anymore.

 0947: 

 0948:     .. attribute:: waiting_on: {TaskState}

 0949: 

 0950:        The set of tasks this task is waiting on *before* it can be executed.

 0951:        This is always a subset of :attr:`dependencies`.  Each time one of the

 0952:        dependencies has finished processing, it is removed from the

 0953:        :attr:`waiting_on` set.

 0954: 

 0955:        Once :attr:`waiting_on` becomes empty, this task can move from the

 0956:        "waiting" state to the "processing" state (unless one of the

 0957:        dependencies errored out, in which case this task is instead

 0958:        marked "erred").

 0959: 

 0960:     .. attribute:: waiters: {TaskState}

 0961: 

 0962:        The set of tasks which need this task to remain alive.  This is always

 0963:        a subset of :attr:`dependents`.  Each time one of the dependents

 0964:        has finished processing, it is removed from the :attr:`waiters`

 0965:        set.

 0966: 

 0967:        Once both :attr:`waiters` and :attr:`who_wants` become empty, this

 0968:        task can be released (if it has a non-empty :attr:`run_spec`) or

 0969:        forgotten (otherwise) by the scheduler, and by any workers

 0970:        in :attr:`who_has`.

 0971: 

 0972:        .. note:: Counter-intuitively, :attr:`waiting_on` and

 0973:           :attr:`waiters` are not reverse mappings of each other.

 0974: 

 0975:     .. attribute:: who_wants: {ClientState}

 0976: 

 0977:        The set of clients who want this task's result to remain alive.

 0978:        This is the reverse mapping of :attr:`ClientState.wants_what`.

 0979: 

 0980:        When a client submits a graph to the scheduler it also specifies

 0981:        which output tasks it desires, such that their results are not released

 0982:        from memory.

 0983: 

 0984:        Once a task has finished executing (i.e. moves into the "memory"

 0985:        or "erred" state), the clients in :attr:`who_wants` are notified.

 0986: 

 0987:        Once both :attr:`waiters` and :attr:`who_wants` become empty, this

 0988:        task can be released (if it has a non-empty :attr:`run_spec`) or

 0989:        forgotten (otherwise) by the scheduler, and by any workers

 0990:        in :attr:`who_has`.

 0991: 

 0992:     .. attribute:: who_has: {WorkerState}

 0993: 

 0994:        The set of workers who have this task's result in memory.

 0995:        It is non-empty iff the task is in the "memory" state.  There can be

 0996:        more than one worker in this set if, for example, :meth:`Client.scatter`

 0997:        or :meth:`Client.replicate` was used.

 0998: 

 0999:        This is the reverse mapping of :attr:`WorkerState.has_what`.

 1000: 

 1001:     .. attribute:: processing_on: WorkerState (or None)

 1002: 

 1003:        If this task is in the "processing" state, which worker is currently

 1004:        processing it.  Otherwise this is ``None``.

 1005: 

 1006:        This attribute is kept in sync with :attr:`WorkerState.processing`.

 1007: 

 1008:     .. attribute:: retries: int

 1009: 

 1010:        The number of times this task can automatically be retried in case

 1011:        of failure.  If a task fails executing (the worker returns with

 1012:        an error), its :attr:`retries` attribute is checked.  If it is

 1013:        equal to 0, the task is marked "erred".  If it is greater than 0,

 1014:        the :attr:`retries` attribute is decremented and execution is

 1015:        attempted again.

 1016: 

 1017:     .. attribute:: nbytes: int (or None)

 1018: 

 1019:        The number of bytes, as determined by ``sizeof``, of the result

 1020:        of a finished task.  This number is used for diagnostics and to

 1021:        help prioritize work.

 1022: 

 1023:     .. attribute:: type: str

 1024: 

 1025:        The type of the object as a string.  Only present for tasks that have

 1026:        been computed.

 1027: 

 1028:     .. attribute:: exception: object

 1029: 

 1030:        If this task failed executing, the exception object is stored here.

 1031:        Otherwise this is ``None``.

 1032: 

 1033:     .. attribute:: traceback: object

 1034: 

 1035:        If this task failed executing, the traceback object is stored here.

 1036:        Otherwise this is ``None``.

 1037: 

 1038:     .. attribute:: exception_blame: TaskState (or None)

 1039: 

 1040:        If this task or one of its dependencies failed executing, the

 1041:        failed task is stored here (possibly itself).  Otherwise this

 1042:        is ``None``.

 1043: 

 1044:     .. attribute:: suspicious: int

 1045: 

 1046:        The number of times this task has been involved in a worker death.

 1047: 

 1048:        Some tasks may cause workers to die (such as calling ``os._exit(0)``).

 1049:        When a worker dies, all of the tasks on that worker are reassigned

 1050:        to others.  This combination of behaviors can cause a bad task to

 1051:        catastrophically destroy all workers on the cluster, one after

 1052:        another.  Whenever a worker dies, we mark each task currently

 1053:        processing on that worker (as recorded by

 1054:        :attr:`WorkerState.processing`) as suspicious.

 1055: 

 1056:        If a task is involved in three deaths (or some other fixed constant)

 1057:        then we mark the task as ``erred``.

 1058: 

 1059:     .. attribute:: host_restrictions: {hostnames}

 1060: 

 1061:        A set of hostnames where this task can be run (or ``None`` if empty).

 1062:        Usually this is empty unless the task has been specifically restricted

 1063:        to only run on certain hosts.  A hostname may correspond to one or

 1064:        several connected workers.

 1065: 

 1066:     .. attribute:: worker_restrictions: {worker addresses}

 1067: 

 1068:        A set of complete worker addresses where this can be run (or ``None``

 1069:        if empty).  Usually this is empty unless the task has been specifically

 1070:        restricted to only run on certain workers.

 1071: 

 1072:        Note this is tracking worker addresses, not worker states, since

 1073:        the specific workers may not be connected at this time.

 1074: 

 1075:     .. attribute:: resource_restrictions: {resource: quantity}

 1076: 

 1077:        Resources required by this task, such as ``{'gpu': 1}`` or

 1078:        ``{'memory': 1e9}`` (or ``None`` if empty).  These are user-defined

 1079:        names and are matched against the contents of each

 1080:        :attr:`WorkerState.resources` dictionary.

 1081: 

 1082:     .. attribute:: loose_restrictions: bool

 1083: 

 1084:        If ``False``, each of :attr:`host_restrictions`,

 1085:        :attr:`worker_restrictions` and :attr:`resource_restrictions` is

 1086:        a hard constraint: if no worker is available satisfying those

 1087:        restrictions, the task cannot go into the "processing" state and

 1088:        will instead go into the "no-worker" state.

 1089: 

 1090:        If ``True``, the above restrictions are mere preferences: if no worker

 1091:        is available satisfying those restrictions, the task can still go

 1092:        into the "processing" state and be sent for execution to another

 1093:        connected worker.

 1094: 

 1095:     .. attribute:: metadata: dict

 1096: 

 1097:        Metadata related to task.

 1098: 

 1099:     .. attribute:: actor: bool

 1100: 

 1101:        Whether or not this task is an Actor.

 1102: 

 1103:     .. attribute:: group: TaskGroup

 1104: 

 1105:         The group of tasks to which this one belongs.

 1106: 

 1107:     .. attribute:: annotations: dict

 1108: 

 1109:         Task annotations

 1110:     """

 1111: 

 1112:     _key: str

 1113:     _hash: Py_hash_t

 1114:     _prefix: TaskPrefix

 1115:     _run_spec: object

 1116:     _priority: tuple

 1117:     _state: str

 1118:     _dependencies: set

 1119:     _dependents: set

 1120:     _has_lost_dependencies: bint

 1121:     _waiting_on: set

 1122:     _waiters: set

 1123:     _who_wants: set

 1124:     _who_has: set

 1125:     _processing_on: WorkerState

 1126:     _retries: Py_ssize_t

 1127:     _nbytes: Py_ssize_t

 1128:     _type: str

 1129:     _exception: object

 1130:     _traceback: object

 1131:     _exception_blame: object

 1132:     _suspicious: Py_ssize_t

 1133:     _host_restrictions: set

 1134:     _worker_restrictions: set

 1135:     _resource_restrictions: dict

 1136:     _loose_restrictions: bint

 1137:     _metadata: dict

 1138:     _annotations: dict

 1139:     _actor: bint

 1140:     _group: TaskGroup

 1141:     _group_key: str

 1142: 

 1143:     __slots__ = (

 1144:         # === General description ===

+1145:         "_actor",

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 1146:         # Key name

 1147:         "_key",

 1148:         # Hash of the key name

 1149:         "_hash",

 1150:         # Key prefix (see key_split())

 1151:         "_prefix",

 1152:         # How to run the task (None if pure data)

 1153:         "_run_spec",

 1154:         # Alive dependents and dependencies

 1155:         "_dependencies",

 1156:         "_dependents",

 1157:         # Compute priority

 1158:         "_priority",

 1159:         # Restrictions

 1160:         "_host_restrictions",

 1161:         "_worker_restrictions",  # not WorkerStates but addresses

 1162:         "_resource_restrictions",

 1163:         "_loose_restrictions",

 1164:         # === Task state ===

 1165:         "_state",

 1166:         # Whether some dependencies were forgotten

 1167:         "_has_lost_dependencies",

 1168:         # If in 'waiting' state, which tasks need to complete

 1169:         # before we can run

 1170:         "_waiting_on",

 1171:         # If in 'waiting' or 'processing' state, which tasks needs us

 1172:         # to complete before they can run

 1173:         "_waiters",

 1174:         # In in 'processing' state, which worker we are processing on

 1175:         "_processing_on",

 1176:         # If in 'memory' state, Which workers have us

 1177:         "_who_has",

 1178:         # Which clients want us

 1179:         "_who_wants",

 1180:         "_exception",

 1181:         "_traceback",

 1182:         "_exception_blame",

 1183:         "_suspicious",

 1184:         "_retries",

 1185:         "_nbytes",

 1186:         "_type",

 1187:         "_group_key",

 1188:         "_group",

 1189:         "_metadata",

 1190:         "_annotations",

 1191:     )

 1192: 

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 1535:     """Underlying task state of dynamic scheduler

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 1555:     **State**

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 1561:     * **tasks:** ``{task key: TaskState}``

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 1573:     * **clients:** ``{client key: ClientState}``

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 1576:     * **task_duration:** ``{key-prefix: time}``

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 1578:     """

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 1580:     _aliases: dict

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+2508:             ts: TaskState = self._tasks[key]

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+2528:             recommendations: dict = {}

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 2765:     """Dynamic distributed task scheduler

 2766: 

 2767:     The scheduler tracks the current state of workers, data, and computations.

 2768:     The scheduler listens for events and responds by controlling workers

 2769:     appropriately.  It continuously tries to use the workers to execute an ever

 2770:     growing dask graph.

 2771: 

 2772:     All events are handled quickly, in linear time with respect to their input

 2773:     (which is often of constant size) and generally within a millisecond.  To

 2774:     accomplish this the scheduler tracks a lot of state.  Every operation

 2775:     maintains the consistency of this state.

 2776: 

 2777:     The scheduler communicates with the outside world through Comm objects.

 2778:     It maintains a consistent and valid view of the world even when listening

 2779:     to several clients at once.

 2780: 

 2781:     A Scheduler is typically started either with the ``dask-scheduler``

 2782:     executable::

 2783: 

 2784:          $ dask-scheduler

 2785:          Scheduler started at 127.0.0.1:8786

 2786: 

 2787:     Or within a LocalCluster a Client starts up without connection

 2788:     information::

 2789: 

 2790:         >>> c = Client()  # doctest: +SKIP

 2791:         >>> c.cluster.scheduler  # doctest: +SKIP

 2792:         Scheduler(...)

 2793: 

 2794:     Users typically do not interact with the scheduler directly but rather with

 2795:     the client object ``Client``.

 2796: 

 2797:     **State**

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 2799:     The scheduler contains the following state variables.  Each variable is

 2800:     listed along with what it stores and a brief description.

 2801: 

 2802:     * **tasks:** ``{task key: TaskState}``

 2803:         Tasks currently known to the scheduler

 2804:     * **unrunnable:** ``{TaskState}``

 2805:         Tasks in the "no-worker" state

 2806: 

 2807:     * **workers:** ``{worker key: WorkerState}``

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 2809:     * **idle:** ``{WorkerState}``:

 2810:         Set of workers that are not fully utilized

 2811:     * **saturated:** ``{WorkerState}``:

 2812:         Set of workers that are not over-utilized

 2813: 

 2814:     * **host_info:** ``{hostname: dict}``:

 2815:         Information about each worker host

 2816: 

 2817:     * **clients:** ``{client key: ClientState}``

 2818:         Clients currently connected to the scheduler

 2819: 

 2820:     * **services:** ``{str: port}``:

 2821:         Other services running on this scheduler, like Bokeh

 2822:     * **loop:** ``IOLoop``:

 2823:         The running Tornado IOLoop

 2824:     * **client_comms:** ``{client key: Comm}``

 2825:         For each client, a Comm object used to receive task requests and

 2826:         report task status updates.

 2827:     * **stream_comms:** ``{worker key: Comm}``

 2828:         For each worker, a Comm object from which we both accept stimuli and

 2829:         report results

 2830:     * **task_duration:** ``{key-prefix: time}``

 2831:         Time we expect certain functions to take, e.g. ``{'sum': 0.25}``

 2832:     """

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 6304:         # HTML

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 6309:         <i> Select different tabs on the top for additional information </i>

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 6311:         <h2> Duration: {time} </h2>

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 6314:          <li> number of tasks: {ntasks} </li>

 6315:          {tasks_timings}

 6316:         </ul>

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 6318:         <h2> Scheduler Information </h2>

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 6320:           <li> Address: {address} </li>

 6321:           <li> Workers: {nworkers} </li>

 6322:           <li> Threads: {threads} </li>

 6323:           <li> Memory: {memory} </li>

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 6325:           <li> Dask.Distributed Version: {distributed_version} </li>

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 6328:         <h2> Calling Code </h2>

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