# Python API (advanced)

In some rare cases, experts may want to create `Scheduler`, `Worker`, and `Nanny` objects explicitly in Python. This is often necessary when making tools to automatically deploy Dask in custom settings.

It is more common to create a Local cluster with Client() on a single machine or use the Command Line Interface (CLI). New readers are recommended to start there.

If you do want to start Scheduler and Worker objects yourself you should be a little familiar with `async`/`await` style Python syntax. These objects are awaitable and are commonly used within `async with` context managers. Here are a few examples to show a few ways to start and finish things.

## Full Example

`Scheduler`([loop, services, service_kwargs, ...]) | Dynamic distributed task scheduler
`Worker`(scheduler_ip, scheduler_port, *, ...) | Worker node in a Dask distributed cluster
`Client`([address, loop, timeout, ...]) | Connect to and submit computation to a Dask cluster

We first start with a comprehensive example of setting up a Scheduler, two Workers, and one Client in the same event loop, running a simple computation, and then cleaning everything up.

    import asyncio
    from dask.distributed import Scheduler, Worker, Client

    async def f():
        async with Scheduler() as s:
            async with Worker(s.address) as w1, Worker(s.address) as w2:
                async with Client(s.address, asynchronous=True) as client:
                    future = client.submit(lambda x: x + 1, 10)
                    result = await future
                    print(result)

    asyncio.get_event_loop().run_until_complete(f())

Now we look at simpler examples that build up to this case.

## Scheduler

`Scheduler`([loop, services, service_kwargs, ...]) | Dynamic distributed task scheduler

We create scheduler by creating a `Scheduler()` object, and then `await` that object to wait for it to start up. We can then wait on the `.finished` method to wait until it closes. In the meantime the scheduler will be active managing the cluster..

    import asyncio
    from dask.distributed import Scheduler, Worker

    async def f():
        s = Scheduler()        # scheduler created, but not yet running
        s = await s            # the scheduler is running
        await s.finished()     # wait until the scheduler closes

    asyncio.get_event_loop().run_until_complete(f())

This program will run forever, or until some external process connects to the scheduler and tells it to stop. If you want to close things yourself you can close any `Scheduler`, `Worker`, `Nanny`, or `Client` class by awaiting the `.close` method:

    await s.close()

## Worker

`Worker`(scheduler_ip, scheduler_port, *, ...) | Worker node in a Dask distributed cluster

The worker follows the same API. The only difference is that the worker needs to know the address of the scheduler.

    import asyncio
    from dask.distributed import Scheduler, Worker

    async def f(scheduler_address):
        w = await Worker(scheduler_address)
        await w.finished()

    asyncio.get_event_loop().run_until_complete(f("tcp://127.0.0.1:8786"))

## Start many in one event loop

`Scheduler`([loop, services, service_kwargs, ...]) | Dynamic distributed task scheduler
`Worker`(scheduler_ip, scheduler_port, *, ...) | Worker node in a Dask distributed cluster

We can run as many of these objects as we like in the same event loop.

    import asyncio
    from dask.distributed import Scheduler, Worker

    async def f():
        s = await Scheduler()
        w = await Worker(s.address)
        await w.finished()
        await s.finished()

    asyncio.get_event_loop().run_until_complete(f())

## Use Context Managers

We can also use `async with` context managers to make sure that we clean up properly. Here is the same example as from above:

    import asyncio
    from dask.distributed import Scheduler, Worker

    async def f():
        async with Scheduler() as s:
            async with Worker(s.address) as w:
                await w.finished()
                await s.finished()

    asyncio.get_event_loop().run_until_complete(f())

Alternatively, in the example below we also include a `Client`, run a small computation, and then allow things to clean up after that computation..

    import asyncio
    from dask.distributed import Scheduler, Worker, Client

    async def f():
        async with Scheduler() as s:
            async with Worker(s.address) as w1, Worker(s.address) as w2:
                async with Client(s.address, asynchronous=True) as client:
                    future = client.submit(lambda x: x + 1, 10)
                    result = await future
                    print(result)

    asyncio.get_event_loop().run_until_complete(f())

This is equivalent to creating and `awaiting` each server, and then calling `.close` on each as we leave the context. In this example we don’t wait on `s.finished()`, so this will terminate relatively quickly. You could have called `await s.finished()` though if you wanted this to run forever.

## Nanny

`Nanny`([scheduler_ip, scheduler_port, ...]) | A process to manage worker processes

Alternatively, we can replace `Worker` with `Nanny` if we want your workers to be managed in a separate process. The `Nanny` constructor follows the same API. This allows workers to restart themselves in case of failure. Also, it provides some additional monitoring, and is useful when coordinating many workers that should live in different processes in order to avoid the GIL.

    # w = await Worker(s.address)
    w = await Nanny(s.address)

## API

These classes have a variety of keyword arguments that you can use to control their behavior. See the API documentation below for more information.

### Scheduler

class distributed.Scheduler(loop=None, services=None, service_kwargs=None, allowed_failures=None, extensions=None, validate=None, scheduler_file=None, security=None, worker_ttl=None, idle_timeout=None, interface=None, host=None, port=0, protocol=None, dashboard_address=None, dashboard=None, http_prefix='/', preload=None, preload_argv=(), plugins=(), contact_address=None, transition_counter_max=False, jupyter=False, **kwargs)

  Dynamic distributed task scheduler

  The scheduler tracks the current state of workers, data, and computations. The scheduler listens for events and responds by controlling workers appropriately. It continuously tries to use the workers to execute an ever growing dask graph.

  All events are handled quickly, in linear time with respect to their input (which is often of constant size) and generally within a millisecond. To accomplish this the scheduler tracks a lot of state. Every operation maintains the consistency of this state.

  The scheduler communicates with the outside world through Comm objects. It maintains a consistent and valid view of the world even when listening to several clients at once.

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

      $ dask scheduler
      Scheduler started at 127.0.0.1:8786

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

      >>> c = Client()
      >>> c.cluster.scheduler
      Scheduler(...)

  Users typically do not interact with the scheduler directly but rather with the client object `Client`.

  The `contact_address` parameter allows to advertise a specific address to the workers for communication with the scheduler, which is different than the address the scheduler binds to. This is useful when the scheduler listens on a private address, which therefore cannot be used by the workers to contact it.

  State

  The scheduler contains the following state variables. Each variable is listed along with what it stores and a brief description.

  - tasks: `{task key: TaskState}`

      Tasks currently known to the scheduler

  - unrunnable: `{TaskState}`

      Tasks in the “no-worker” state

  - workers: `{worker key: WorkerState}`

      Workers currently connected to the scheduler

  - idle: `{WorkerState}`:

      Set of workers that are not fully utilized

  - saturated: `{WorkerState}`:

      Set of workers that are not over-utilized

  - host_info: `{hostname: dict}`:

      Information about each worker host

  - clients: `{client key: ClientState}`

      Clients currently connected to the scheduler

  - services: `{str: port}`:

      Other services running on this scheduler, like Bokeh

  - loop: `IOLoop`:

      The running Tornado IOLoop

  - client_comms: `{client key: Comm}`

      For each client, a Comm object used to receive task requests and report task status updates.

  - stream_comms: `{worker key: Comm}`

      For each worker, a Comm object from which we both accept stimuli and report results

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

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

  MEMORY_REBALANCE_HALF_GAP: float

    distributed.worker.memory.rebalance.sender-recipient-gap / 2

  MEMORY_REBALANCE_MEASURE: str

    distributed.worker.memory.rebalance.measure

  MEMORY_REBALANCE_RECIPIENT_MAX: float

    distributed.worker.memory.rebalance.recipient-max

  MEMORY_REBALANCE_SENDER_MIN: float

    distributed.worker.memory.rebalance.sender-min

  MEMORY_RECENT_TO_OLD_TIME: float

    distributed.worker.memory.recent-to-old-time

  UNKNOWN_TASK_DURATION: float

    distributed.scheduler.unknown-task-duration

  WORKER_SATURATION: float

    distributed.scheduler.worker-saturation

  adaptive_target(target_duration=None)

    Desired number of workers based on the current workload

    This looks at the current running tasks and memory use, and returns a number of desired workers. This is often used by adaptive scheduling.

    Parameters:
      target_duration str

        A desired duration of time for computations to take. This affects how rapidly the scheduler will ask to scale.

    See also

    `distributed.deploy.Adaptive`
  async add_client(comm: Comm, client: str, versions: dict[str, Any]) → None

    Add client to network

    We listen to all future messages from this Comm.

  add_keys(worker: str, keys: Collection[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]] = (), stimulus_id: str | None = None) → Literal['OK', 'not found']

    Learn that a worker has certain keys

    This should not be used in practice and is mostly here for legacy reasons. However, it is sent by workers from time to time.

  add_plugin(plugin: SchedulerPlugin, *, idempotent: bool = False, name: str | None = None, **kwargs: Any) → None

    Add external plugin to scheduler.

    See https://distributed.readthedocs.io/en/latest/plugins.html

    Parameters:
      plugin SchedulerPlugin

        SchedulerPlugin instance to add

      idempotent bool

        If true, the plugin is assumed to already exist and no action is taken.

      name str

        A name for the plugin, if None, the name attribute is checked on the Plugin instance and generated if not discovered.

  async add_worker(comm: Comm, *, address: str, status: str, server_id: str, nthreads: int, name: str, resolve_address: bool = True, now: float, resources: dict[str, float], host_info: None = None, memory_limit: int | None, metrics: dict[str, Any], pid: int = 0, services: dict[str, int], local_directory: str, versions: dict[str, Any], nanny: str, extra: dict, stimulus_id: str) → None

    Add a new worker to the cluster

  aliases: dict[Hashable, str]

    Worker {name: address}

  async benchmark_hardware() → dict[str, dict[str, float]]

    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.

  async broadcast(*, msg: dict, workers: Collection[str] | None = None, hosts: Collection[str] | None = None, nanny: bool = False, serializers: Any = None, on_error: Literal['raise', 'return', 'return_pickle', 'ignore'] = 'raise') → dict[str, Any]

    Broadcast message to workers, return all results

  client_heartbeat(client: str) → None

    Handle heartbeats from Client

  client_releases_keys(keys: Collection[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]], client: str, stimulus_id: str | None = None) → None

    Remove keys from client desired list

  client_send(client, msg)

    Send message to client

  clients: dict[str, ClientState]

    Clients currently connected to the scheduler

  async close(fast=None, close_workers=None, reason='unknown')

    Send cleanup signal to all coroutines then wait until finished

    See also

    `Scheduler.cleanup`
  close_worker(worker: str) → None

    Ask a worker to shut itself down. Do not wait for it to take effect. Note that there is no guarantee that the worker will actually accept the command.

    Note that `remove_worker()` sends the same command internally if close=True.

    See also

    `retire_workers`
    `remove_worker`
  coerce_address(addr: str | tuple, resolve: bool = True) → str

    Coerce possible input addresses to canonical form. resolve can be disabled for testing with fake hostnames.

    Handles strings, tuples, or aliases.

  computations: deque[Computation]

    History of computations. The length can be tweaked through distributed.diagnostics.computations.max-history

  async delete_worker_data(worker_address: str, keys: Collection[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]], stimulus_id: str) → None

    Delete data from a worker and update the corresponding worker/task states

    Parameters:
      worker_address: str

        Worker address to delete keys from

      keys: list[Key]

        List of keys to delete on the specified worker

  async dump_cluster_state_to_url(url: str, exclude: Collection[str], format: Literal['msgpack', 'yaml'], **storage_options: dict[str, Any]) → None

    Write a cluster state dump to an fsspec-compatible URL.

  erred_tasks: deque[ErredTask]

    History of erred tasks. The length can be tweaked through distributed.diagnostics.erred-tasks.max-history

  async feed(comm: Comm, function: bytes | None = None, setup: bytes | None = None, teardown: bytes | None = None, interval: str | float = '1s', **kwargs: Any) → None

    Provides a data Comm to external requester

    Caution: this runs arbitrary Python code on the scheduler. This should eventually be phased out. It is mostly used by diagnostics.

  async gather(keys: Collection[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]], serializers: list[str] | None = None) → dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], object]

    Collect data from workers to the scheduler

  async gather_on_worker(worker_address: str, who_has: dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], list[str]]) → set

    Peer-to-peer copy of keys from multiple workers to a single worker

    Parameters:
      worker_address: str

        Recipient worker address to copy keys to

      who_has: dict[Key, list[str]]

        {key: [sender address, sender address, …], key: …}

    Returns:
      returns:

        set of keys that failed to be copied

  async get_cluster_state(exclude: Collection[str]) → dict

    Produce the state dict used in a cluster state dump

  async get_story(keys_or_stimuli: Iterable[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]]) → list[Transition]

    RPC hook for `SchedulerState.story()`.

    Note that the msgpack serialization/deserialization round-trip will transform the `Transition` namedtuples into regular tuples.

  get_worker_service_addr(worker: str, service_name: str, protocol: bool = False) → tuple[str, int] | str | None

    Get the (host, port) address of the named service on the worker. Returns None if the service doesn’t exist.

    Parameters:
      worker address
      service_name str

        Common services include ‘bokeh’ and ‘nanny’

      protocol boolean

        Whether or not to include a full address with protocol (True) or just a (host, port) pair

  handle_long_running(key: str | bytes | int | float | tuple[Key, ...], worker: str, compute_duration: float | None, stimulus_id: str) → None

    A task has seceded from the thread pool

    We stop the task from being stolen in the future, and change task duration accounting as if the task has stopped.

  handle_request_refresh_who_has(keys: Iterable[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]], worker: str, stimulus_id: str) → None

    Request from a Worker to refresh the who_has for some keys. Not to be confused with scheduler.who_has, which is a dedicated comm RPC request from a Client.

  async handle_worker(comm: Comm, worker: str) → None

    Listen to responses from a single worker

    This is the main loop for scheduler-worker interaction

    See also

    `Scheduler.handle_client`

      Equivalent coroutine for clients

  identity() → dict[str, Any]

    Basic information about ourselves and our cluster

  idle: dict[str, WorkerState]

    Workers that are currently in running state and not fully utilized Definition based on occupancy (actually a SortedDict, but the sortedcontainers package isn’t annotated). Not to be confused with `is_idle()`.

  idle_task_count: set[WorkerState]

    Similar to idle Definition based on assigned tasks

  log_event(topic: str | Collection[str], msg: Any) → None

    Log an event under a given topic

    Parameters:
      topic str, list[str]

        Name of the topic under which to log an event. To log the same event under multiple topics, pass a list of topic names.

      msg

        Event message to log. Note this must be msgpack serializable.

    See also

    `Client.log_event`
  n_tasks: int

    Total number of tasks ever processed

  async proxy(msg: dict, worker: str, serializers: Any = None) → Any

    Proxy a communication through the scheduler to some other worker

  queued: HeapSet[TaskState]

    Tasks in the “queued” state, ordered by priority

  async rebalance(keys: Iterable[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]] | None = None, workers: Iterable[str] | None = None, stimulus_id: str | None = None) → dict

    Rebalance keys so that each worker ends up with roughly the same process memory (managed+unmanaged).

    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.

    Algorithm

    1. Find the mean occupancy of the cluster, defined as data managed by dask + unmanaged process memory that has been there for at least 30 seconds (`distributed.worker.memory.recent-to-old-time`). This lets us ignore temporary spikes caused by task heap usage.

       Alternatively, you may change how memory is measured both for the individual workers as well as to calculate the mean through `distributed.worker.memory.rebalance.measure`. Namely, this can be useful to disregard inaccurate OS memory measurements.

    2. Discard workers whose occupancy is within 5% of the mean cluster occupancy (`distributed.worker.memory.rebalance.sender-recipient-gap` / 2). This helps avoid data from bouncing around the cluster repeatedly.

    3. Workers above the mean are senders; those below are recipients.

    4. Discard senders whose absolute occupancy is below 30% (`distributed.worker.memory.rebalance.sender-min`). In other words, no data is moved regardless of imbalancing as long as all workers are below 30%.

    5. Discard recipients whose absolute occupancy is above 60% (`distributed.worker.memory.rebalance.recipient-max`). Note that this threshold by default is the same as `distributed.worker.memory.target` to prevent workers from accepting data and immediately spilling it out to disk.

    6. Iteratively pick the sender and recipient that are farthest from the mean and move the least recently inserted key between the two, until either all senders or all recipients fall within 5% of the mean.

       A recipient will be skipped if it already has a copy of the data. In other words, this method does not degrade replication. A key will be skipped if there are no recipients available with enough memory to accept the key and that don’t already hold a copy.

    The least recently insertd (LRI) policy is a greedy choice with the advantage of being O(1), trivial to implement (it relies on python dict insertion-sorting) and hopefully good enough in most cases. Discarded alternative policies were:

    - Largest first. O(n*log(n)) save for non-trivial additional data structures and risks causing the largest chunks of data to repeatedly move around the cluster like pinballs.

    - Least recently used (LRU). This information is currently available on the workers only and not trivial to replicate on the scheduler; transmitting it over the network would be very expensive. Also, note that dask will go out of its way to minimise the amount of time intermediate keys are held in memory, so in such a case LRI is a close approximation of LRU.

    Parameters:
      keys: optional

        allowlist of dask keys that should be considered for moving. All other keys will be ignored. Note that this offers no guarantee that a key will actually be moved (e.g. because it is unnecessary or because there are no viable recipient workers for it).

      workers: optional

        allowlist of workers addresses to be considered as senders or recipients. All other workers will be ignored. The mean cluster occupancy will be calculated only using the allowed workers.

  async register_nanny_plugin(comm: None, plugin: bytes, name: str, idempotent: bool | None = None) → dict[str, OKMessage]

    Registers a nanny plugin on all running and future nannies

  async register_scheduler_plugin(plugin: bytes | SchedulerPlugin, name: str | None = None, idempotent: bool | None = None) → None

    Register a plugin on the scheduler.

  async register_worker_plugin(comm: None, plugin: bytes, name: str, idempotent: bool | None = None) → dict[str, OKMessage]

    Registers a worker plugin on all running and future workers

  remove_client(client: str, stimulus_id: str | None = None) → None

    Remove client from network

  remove_plugin(name: str | None = None) → None

    Remove external plugin from scheduler

    Parameters:
      name str

        Name of the plugin to remove

  remove_worker(address: str, *, stimulus_id: str, expected: bool = False, close: bool = True) → Literal['OK', 'already-removed']

    Remove worker from cluster.

    We do this when a worker reports that it plans to leave or when it appears to be unresponsive. This may send its tasks back to a released state.

    See also

    `retire_workers`
    `close_worker`
  async replicate(comm=None, keys=None, n=None, workers=None, branching_factor=2, delete=True, stimulus_id=None)

    Replicate data throughout cluster

    This performs a tree copy of the data throughout the network individually on each piece of data.

    Parameters:
      keys: Iterable

        list of keys to replicate

      n: int

        Number of replications we expect to see within the cluster

      branching_factor: int, optional

        The number of workers that can copy data in each generation. The larger the branching factor, the more data we copy in a single step, but the more a given worker risks being swamped by data requests.

    See also

    `Scheduler.rebalance`
  replicated_tasks: set[TaskState]

    Subset of tasks that exist in memory on more than one worker

  report(msg: dict, ts: TaskState | None = None, client: str | None = None) → None

    Publish updates to all listening Queues and Comms

    If the message contains a key then we only send the message to those comms that care about the key.

  request_acquire_replicas(addr: str, keys: Iterable[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]], *, stimulus_id: str) → None

    Asynchronously ask a worker to acquire a replica of the listed keys from other workers. This is a fire-and-forget operation which offers no feedback for success or failure, and is intended for housekeeping and not for computation.

  request_remove_replicas(addr: str, keys: list[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]], *, stimulus_id: str) → None

    Asynchronously ask a worker to discard its replica of the listed keys. This must never be used to destroy the last replica of a key. This is a fire-and-forget operation, intended for housekeeping and not for computation.

    The replica disappears immediately from TaskState.who_has on the Scheduler side; if the worker refuses to delete, e.g. because the task is a dependency of another task running on it, it will (also asynchronously) inform the scheduler to re-add itself to who_has. If the worker agrees to discard the task, there is no feedback.

  resources: dict[str, dict[str, float]]

    Cluster-wide resources. {resource name: {worker address: amount}}

  async restart(*, client: str | None = None, timeout: float = 30, wait_for_workers: bool = True, stimulus_id: str) → None

    Forget all tasks and call restart_workers on all workers.

    Parameters:
      timeout:

        See restart_workers

      wait_for_workers:

        See restart_workers

    See also

    `Client.restart`
    `Client.restart_workers`
    `Scheduler.restart_workers`
  async restart_workers(workers: list[str] | None = None, *, client: str | None = None, timeout: float = 30, wait_for_workers: bool = True, on_error: Literal['raise', 'return'] = 'raise', stimulus_id: str) → dict[str, Literal['OK', 'removed', 'timed out']]

    Restart selected workers. 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:
      workers:

        List of worker addresses to restart. If omitted, restart all workers.

      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 `Client.wait_for_workers()` for granular control over how many workers to wait for.

      on_error:

        If ‘raise’ (the default), raise if any nanny times out while restarting the worker. If ‘return’, return error messages.

    Returns:
      {worker address: “OK”, “no nanny”, or “timed out” or error message}

    See also

    `Client.restart`
    `Client.restart_workers`
    `Scheduler.restart`
  async retire_workers(workers: list[str], *, close_workers: bool = False, remove: bool = True, stimulus_id: str | None = None) → dict[str, Any]
  async retire_workers(*, names: list, close_workers: bool = False, remove: bool = True, stimulus_id: str | None = None) → dict[str, Any]
  async retire_workers(*, close_workers: bool = False, remove: bool = True, stimulus_id: str | None = None, memory_ratio: int | float | None = None, n: int | None = None, key: Callable[[WorkerState], Hashable] | bytes | None = None, minimum: int | None = None, target: int | None = None, attribute: str = 'address') → dict[str, Any]

    Gracefully retire workers from cluster. Any key that is in memory exclusively on the retired workers is replicated somewhere else.

    Parameters:
      workers: list[str] (optional)

        List of worker addresses to retire.

      names: list (optional)

        List of worker names to retire. Mutually exclusive with `workers`. If neither `workers` nor `names` are provided, we call `workers_to_close` which finds a good set.

      close_workers: bool (defaults to False)

        Whether to actually close the worker explicitly from here. Otherwise, we expect some external job scheduler to finish off the worker.

      remove: bool (defaults to True)

        Whether to remove the worker metadata immediately or else wait for the worker to contact us.

        If close_workers=False and remove=False, this method just flushes the tasks in memory out of the workers and then returns. If close_workers=True and remove=False, this method will return while the workers are still in the cluster, although they won’t accept new tasks. If close_workers=False or for whatever reason a worker doesn’t accept the close command, it will be left permanently unable to accept new tasks and it is expected to be closed in some other way.

      **kwargs: dict

        Extra options to pass to workers_to_close to determine which workers we should drop. Only accepted if `workers` and `names` are omitted.

    Returns:
      Dictionary mapping worker ID/address to dictionary of information about
      that worker for each retired worker.
      If there are keys that exist in memory only on the workers being retired and it
      was impossible to replicate them somewhere else (e.g. because there aren’t
      any other running workers), the workers holding such keys won’t be retired and
      won’t appear in the returned dict.

    See also

    `Scheduler.workers_to_close`
  run_function(comm: Comm, function: Callable, args: tuple = (), kwargs: dict | None = None, wait: bool = True) → Any

    Run a function within this process

    See also

    `Client.run_on_scheduler`
  running: set[WorkerState]

    Workers that are currently in running state

  saturated: set[WorkerState]

    Workers that are fully utilized. May include non-running workers.

  async scatter(comm=None, data=None, workers=None, client=None, broadcast=False, timeout=2)

    Send data out to workers

    See also

    `Scheduler.broadcast`
  send_all(client_msgs: dict[str, list[dict[str, Any]]], worker_msgs: dict[str, list[dict[str, Any]]]) → None

    Send messages to client and workers

  send_task_to_worker(worker: str, ts: TaskState, duration: float = -1) → None

    Send a single computational task to a worker

  async start_unsafe() → Self

    Clear out old state and restart all running coroutines

  stimulus_cancel(keys: Collection[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]], client: str, force: bool = False) → None

    Stop execution on a list of keys

  stimulus_queue_slots_maybe_opened(*, stimulus_id: str) → None

    Respond to an event which may have opened spots on worker threadpools

    Selects the appropriate number of tasks from the front of the queue according to the total number of task slots available on workers (potentially 0), and transitions them to `processing`.

    Notes

    Other transitions related to this stimulus should be fully processed beforehand, so any tasks that became runnable are already in `processing`. Otherwise, overproduction can occur if queued tasks get scheduled before downstream tasks.

    Must be called after check_idle_saturated; i.e. idle_task_count must be up to date.

  stimulus_task_erred(key=None, worker=None, exception=None, stimulus_id=None, traceback=None, run_id=None, **kwargs)

    Mark that a task has erred on a particular worker

  stimulus_task_finished(key: str | bytes | int | float | tuple[Key, ...], worker: str, stimulus_id: str, run_id: int, **kwargs: Any) → tuple[dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], Literal['released', 'waiting', 'no-worker', 'queued', 'processing', 'memory', 'erred', 'forgotten']], dict[str, list[dict[str, Any]]], dict[str, list[dict[str, Any]]]]

    Mark that a task has finished execution on a particular worker

  tasks: dict[Key, TaskState]

    All tasks currently known to the scheduler

  total_nthreads: int

    Current number of threads across all workers

  total_nthreads_history: list[tuple[float, int]]

    History of number of threads (timestamp, new number of threads)

  transition(key: str | bytes | int | float | tuple[Key, ...], finish: Literal['released', 'waiting', 'no-worker', 'queued', 'processing', 'memory', 'erred', 'forgotten'], stimulus_id: str, **kwargs: Any) → dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], Literal['released', 'waiting', 'no-worker', 'queued', 'processing', 'memory', 'erred', 'forgotten']]

    Transition a key from its current state to the finish state

    Returns:
      Dictionary of recommendations for future transitions

    See also

    `Scheduler.transitions`

      transitive version of this function

    Examples

        >>> self.transition('x', 'waiting')
        {'x': 'processing'}

  transition_counter: int

    Total number of transitions since the cluster was started

  transition_counter_max: int | Literal[False]

    Raise an error if the `transition_counter` ever reaches this value. This is meant for debugging only, to catch infinite recursion loops. In production, it should always be set to False.

  transition_log: deque[Transition]

    History of task state transitions. The length can be tweaked through distributed.admin.low-level-log-length

  transitions(recommendations: dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], Literal['released', 'waiting', 'no-worker', 'queued', 'processing', 'memory', 'erred', 'forgotten']], stimulus_id: str) → None

    Process transitions until none are left

    This includes feedback from previous transitions and continues until we reach a steady state

  unknown_durations: dict[str, set[TaskState]]

    Tasks with unknown duration, grouped by prefix {task prefix: {ts, ts, …}}

  async unregister_nanny_plugin(comm: None, name: str) → dict[str, ErrorMessage | OKMessage]

    Unregisters a worker plugin

  async unregister_scheduler_plugin(name: str) → None

    Unregister a plugin on the scheduler.

  async unregister_worker_plugin(comm: None, name: str) → dict[str, ErrorMessage | OKMessage]

    Unregisters a worker plugin

  unrunnable: dict[TaskState, float]

    Tasks in the “no-worker” state with the (monotonic) time when they became unrunnable

  update_data(*, who_has: dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], list[str]], nbytes: dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], int], client: str | None = None) → None

    Learn that new data has entered the network from an external source

  validate: bool

    If True, enable expensive internal consistency check. Typically disabled in production.

  worker_send(worker: str, msg: dict[str, Any]) → None

    Send message to worker

    This also handles connection failures by adding a callback to remove the worker on the next cycle.

  workers: dict[str, WorkerState]

    Workers currently connected to the scheduler (actually a SortedDict, but the sortedcontainers package isn’t annotated)

  workers_list(workers: Iterable[str] | None) → list[str]

    List of qualifying workers

    Takes a list of worker addresses or hostnames. Returns a list of all worker addresses that match

  workers_to_close(memory_ratio: int | float | None = None, n: int | None = None, key: Callable[[WorkerState], Hashable] | bytes | None = None, minimum: int | None = None, target: int | None = None, attribute: str = 'address') → list[str]

    Find workers that we can close with low cost

    This returns a list of workers that are good candidates to retire. These workers are not running anything and are storing relatively little data relative to their peers. If all workers are idle then we still maintain enough workers to have enough RAM to store our data, with a comfortable buffer.

    This is for use with systems like `distributed.deploy.adaptive`.

    Parameters:
      memory_ratio Number

        Amount of extra space we want to have for our stored data. Defaults to 2, or that we want to have twice as much memory as we currently have data.

      n int

        Number of workers to close

      minimum int

        Minimum number of workers to keep around

      key Callable(WorkerState)

        An optional callable mapping a WorkerState object to a group affiliation. Groups will be closed together. This is useful when closing workers must be done collectively, such as by hostname.

      target int

        Target number of workers to have after we close

      attribute str

        The attribute of the WorkerState object to return, like “address” or “name”. Defaults to “address”.

    Returns:
      to_close: list of worker addresses that are OK to close

    See also

    `Scheduler.retire_workers`

    Examples

        >>> scheduler.workers_to_close()
        ['tcp://192.168.0.1:1234', 'tcp://192.168.0.2:1234']

    Group workers by hostname prior to closing

        >>> scheduler.workers_to_close(key=lambda ws: ws.host)
        ['tcp://192.168.0.1:1234', 'tcp://192.168.0.1:4567']

    Remove two workers

        >>> scheduler.workers_to_close(n=2)

    Keep enough workers to have twice as much memory as we we need.

        >>> scheduler.workers_to_close(memory_ratio=2)

### Worker

class distributed.Worker(scheduler_ip: str | None = None, scheduler_port: int | None = None, *, scheduler_file: str | None = None, nthreads: int | None = None, loop: IOLoop | None = None, local_directory: str | None = None, services: dict | None = None, name: Any | None = None, reconnect: bool | None = None, executor: Executor | dict[str, Executor] | Literal['offload'] | None = None, resources: dict[str, float] | None = None, silence_logs: int | None = None, death_timeout: Any | None = None, preload: list[str] | None = None, preload_argv: list[str] | list[list[str]] | None = None, security: Security | dict[str, Any] | None = None, contact_address: str | None = None, heartbeat_interval: Any = '1s', extensions: dict[str, type] | None = None, metrics: Mapping[str, Callable[[Worker], Any]] = {}, startup_information: Mapping[str, Callable[[Worker], Any]] = {}, interface: str | None = None, host: str | None = None, port: int | str | Collection[int] | None = None, protocol: str | None = None, dashboard_address: str | None = None, dashboard: bool = False, http_prefix: str = '/', nanny: Nanny | None = None, plugins: tuple[WorkerPlugin, ...] = (), low_level_profiler: bool | None = None, validate: bool | None = None, profile_cycle_interval=None, lifetime: Any | None = None, lifetime_stagger: Any | None = None, lifetime_restart: bool | None = None, transition_counter_max: int | Literal[False] = False, memory_limit: str | float = 'auto', data: WorkerDataParameter = None, memory_target_fraction: float | Literal[False] | None = None, memory_spill_fraction: float | Literal[False] | None = None, memory_pause_fraction: float | Literal[False] | None = None, scheduler_sni: str | None = None, WorkerStateClass: type = <class 'distributed.worker_state_machine.WorkerState'>, **kwargs)

  Worker node in a Dask distributed cluster

  Workers perform two functions:

  1. Serve data from a local dictionary

  2. Perform computation on that data and on data from peers

  Workers keep the scheduler informed of their data and use that scheduler to gather data from other workers when necessary to perform a computation.

  You can start a worker with the `dask worker` command line application:

      $ dask worker scheduler-ip:port

  Use the `--help` flag to see more options:

      $ dask worker --help

  The rest of this docstring is about the internal state that the worker uses to manage and track internal computations.

  State

  Informational State

  These attributes don’t change significantly during execution.

  - nthreads: `int`:

      Number of nthreads used by this worker process

  - executors: `dict[str, concurrent.futures.Executor]`:

      Executors used to perform computation. Always contains the default executor.

  - local_directory: `path`:

      Path on local machine to store temporary files

  - scheduler: `PooledRPCCall`:

      Location of scheduler. See `.ip/.port` attributes.

  - name: `string`:

      Alias

  - services: `{str: Server}`:

      Auxiliary web servers running on this worker

  - service_ports: `{str: port}`:

  - transfer_outgoing_count_limit: `int`

      The maximum number of concurrent outgoing data transfers. See also `distributed.worker_state_machine.WorkerState.transfer_incoming_count_limit`.

  - batched_stream: `BatchedSend`

      A batched stream along which we communicate to the scheduler

  - log: `[(message)]`

      A structured and queryable log. See `Worker.story`

  Volatile State

  These attributes track the progress of tasks that this worker is trying to complete. In the descriptions below a `key` is the name of a task that we want to compute and `dep` is the name of a piece of dependent data that we want to collect from others.

  - threads: `{key: int}`

      The ID of the thread on which the task ran

  - active_threads: `{int: key}`

      The keys currently running on active threads

  - state: `WorkerState`

      Encapsulated state machine. See `BaseWorker` and `WorkerState`

  Parameters:
    scheduler_ip: str, optional
    scheduler_port: int, optional
    scheduler_file: str, optional
    host: str, optional
    data: MutableMapping, type, None

      The object to use for storage, builds a disk-backed LRU dict by default.

      If a callable to construct the storage object is provided, it will receive the worker’s attr:`local_directory` as an argument if the calling signature has an argument named `worker_local_directory`.

    nthreads: int, optional
    local_directory: str, optional

      Directory where we place local resources

    name: str, optional
    memory_limit: int, float, string

      Number of bytes of memory that this worker should use. Set to zero for no limit. Set to ‘auto’ to calculate as system.MEMORY_LIMIT * min(1, nthreads / total_cores) Use strings or numbers like 5GB or 5e9

    memory_target_fraction: float or False

      Fraction of memory to try to stay beneath (default: read from config key distributed.worker.memory.target)

    memory_spill_fraction: float or False

      Fraction of memory at which we start spilling to disk (default: read from config key distributed.worker.memory.spill)

    memory_pause_fraction: float or False

      Fraction of memory at which we stop running new tasks (default: read from config key distributed.worker.memory.pause)

    max_spill: int, string or False

      Limit of number of bytes to be spilled on disk. (default: read from config key distributed.worker.memory.max-spill)

    executor: concurrent.futures.Executor, dict[str, concurrent.futures.Executor], “offload”
      The executor(s) to use. Depending on the type, it has the following meanings:

        - Executor instance: The default executor.

        - Dict[str, Executor]: mapping names to Executor instances. If the “default” key isn’t in the dict, a “default” executor will be created using `ThreadPoolExecutor(nthreads)`.

        - Str: The string “offload”, which refer to the same thread pool used for offloading communications. This results in the same thread being used for deserialization and computation.

    resources: dict

      Resources that this worker has like `{'GPU': 2}`

    nanny: str

      Address on which to contact nanny, if it exists

    lifetime: str

      Amount of time like “1 hour” after which we gracefully shut down the worker. This defaults to None, meaning no explicit shutdown time.

    lifetime_stagger: str

      Amount of time like “5 minutes” to stagger the lifetime value The actual lifetime will be selected uniformly at random between lifetime +/- lifetime_stagger

    lifetime_restart: bool

      Whether or not to restart a worker after it has reached its lifetime Default False

    kwargs: optional

      Additional parameters to ServerNode constructor

  See also

  `distributed.scheduler.Scheduler`
  `distributed.nanny.Nanny`

  Examples

  Use the command line to start a worker:

      $ dask scheduler
      Start scheduler at 127.0.0.1:8786

      $ dask worker 127.0.0.1:8786
      Start worker at:               127.0.0.1:1234
      Registered with scheduler at:  127.0.0.1:8786

  batched_send(msg: dict[str, Any]) → None

    Implements BaseWorker abstract method.

    Send a fire-and-forget message to the scheduler through bulk comms.

    If we’re not currently connected to the scheduler, the message will be silently dropped!

    See also

    `distributed.worker_state_machine.BaseWorker.batched_send`
  async close(timeout: float = 30, executor_wait: bool = True, nanny: bool = True, reason: str = 'worker-close') → str | None

    Close the worker

    Close asynchronous operations running on the worker, stop all executors and comms. If requested, this also closes the nanny.

    Parameters:
      timeout

        Timeout in seconds for shutting down individual instructions

      executor_wait

        If True, shut down executors synchronously, otherwise asynchronously

      nanny

        If True, close the nanny

      reason

        Reason for closing the worker

    Returns:
      str | None

        None if worker already in closing state or failed, “OK” otherwise

  async close_gracefully(restart=None, reason: str = 'worker-close-gracefully')

    Gracefully shut down a worker

    This first informs the scheduler that we’re shutting down, and asks it to move our data elsewhere. Afterwards, we close as normal

  property data: MutableMapping[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], object]

    {task key: task payload} of all completed tasks, whether they were computed on this Worker or computed somewhere else and then transferred here over the network.

    When using the default configuration, this is a zict buffer that automatically spills to disk whenever the target threshold is exceeded. If spilling is disabled, it is a plain dict instead. It could also be a user-defined arbitrary dict-like passed when initialising the Worker or the Nanny. Worker logic should treat this opaquely and stick to the MutableMapping API.

    Note

    This same collection is also available at `self.state.data` and `self.memory_manager.data`.

  digest_metric(name: Hashable, value: float) → None

    Implement BaseWorker.digest_metric by calling Server.digest_metric

  async execute(key: str | bytes | int | float | tuple[Key, ...], *, stimulus_id: str) → StateMachineEvent

    Execute a task. Implements BaseWorker abstract method.

    See also

    `distributed.worker_state_machine.BaseWorker.execute`
  async gather(who_has: dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], list[str]]) → dict[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...], object]

    Endpoint used by Scheduler.rebalance() and Scheduler.replicate()

  async gather_dep(worker: str, to_gather: Collection[str | bytes | int | float | tuple[str | bytes | int | float | tuple[Key, ...], ...]], total_nbytes: int, *, stimulus_id: str) → StateMachineEvent

    Implements BaseWorker abstract method

    See also

    `distributed.worker_state_machine.BaseWorker.gather_dep`
  get_current_task() → str | bytes | int | float | tuple[Key, ...]

    Get the key of the task we are currently running

    This only makes sense to run within a task

    See also

    `get_worker`

    Examples

        >>> from dask.distributed import get_worker
        >>> def f():
        ...     return get_worker().get_current_task()

        >>> future = client.submit(f)
        >>> future.result()
        'f-1234'

  handle_stimulus(*stims: StateMachineEvent) → None

    Override BaseWorker method for added validation

    See also

    `distributed.worker_state_machine.BaseWorker.handle_stimulus`
    `distributed.worker_state_machine.WorkerState.handle_stimulus`
  log_event(topic: str | Collection[str], msg: Any) → None

    Log an event under a given topic

    Parameters:
      topic str, list[str]

        Name of the topic under which to log an event. To log the same event under multiple topics, pass a list of topic names.

      msg

        Event message to log. Note this must be msgpack serializable.

    See also

    `Client.log_event`
  async retry_busy_worker_later(worker: str) → StateMachineEvent

    Wait some time, then take a peer worker out of busy state. Implements BaseWorker abstract method.

    See also

    `distributed.worker_state_machine.BaseWorker.retry_busy_worker_later`
  async start_unsafe()

    Attempt to start the server. This is not idempotent and not protected against concurrent startup attempts.

    This is intended to be overwritten or called by subclasses. For a safe startup, please use `Server.start` instead.

    If `death_timeout` is configured, we will require this coroutine to finish before this timeout is reached. If the timeout is reached we will close the instance and raise an `asyncio.TimeoutError`

  transfer_outgoing_bytes: int

    Current total size of open data transfers to other workers

  transfer_outgoing_bytes_total: int

    Total size of data transfers to other workers (including in-progress and failed transfers)

  transfer_outgoing_count: int

    Current number of open data transfers to other workers

  transfer_outgoing_count_total: int

    Total number of data transfers to other workers since the worker was started

  trigger_profile() → None

    Get a frame from all actively computing threads

    Merge these frames into existing profile counts

  property worker_address

    For API compatibility with Nanny

### Nanny

class distributed.Nanny(scheduler_ip=None, scheduler_port=None, scheduler_file=None, worker_port: int | str | Collection[int] | None = 0, nthreads=None, loop=None, local_directory=None, services=None, name=None, memory_limit='auto', reconnect=True, validate=False, quiet=False, resources=None, silence_logs=None, death_timeout=None, preload=None, preload_argv=None, preload_nanny=None, preload_nanny_argv=None, security=None, contact_address=None, listen_address=None, worker_class=None, env=None, interface=None, host=None, port: int | str | Collection[int] | None = None, protocol=None, config=None, **worker_kwargs)

  A process to manage worker processes

  The nanny spins up Worker processes, watches them, and kills or restarts them as necessary. It is necessary if you want to use the `Client.restart` method, or to restart the worker automatically if it gets to the terminate fraction of its memory limit.

  The parameters for the Nanny are mostly the same as those for the Worker with exceptions listed below.

  Parameters:
    env: dict, optional

      Environment variables set at time of Nanny initialization will be ensured to be set in the Worker process as well. This argument allows to overwrite or otherwise set environment variables for the Worker. It is also possible to set environment variables using the option `distributed.nanny.environ`. Precedence as follows

      > 1. Nanny arguments

      > 2. Existing environment variables

      > 3. Dask configuration

      Note

      Some environment variables, like `OMP_NUM_THREADS`, must be set before importing numpy to have effect. Others, like `MALLOC_TRIM_THRESHOLD_` (see memtrim), must be set before starting the Linux process. Such variables would be ineffective if set here or in `distributed.nanny.environ`; they must be set in `distributed.nanny.pre-spawn-environ` so that they are set before spawning the subprocess, even if this means poisoning the process running the Nanny.

      For the same reason, be warned that changing `distributed.worker.multiprocessing-method` from `spawn` to `fork` or `forkserver` may inhibit some environment variables; if you do, you should set the variables yourself in the shell before you start `dask-worker`.

  See also

  `Worker`
  async close(timeout: float = 5, reason: str = 'nanny-close') → Literal['OK']

    Close the worker process, stop all comms.

  close_gracefully(reason: str = 'nanny-close-gracefully') → None

    A signal that we shouldn’t try to restart workers if they go away

    This is used as part of the cluster shutdown process.

  async instantiate() → Status

    Start a local worker process

    Blocks until the process is up and the scheduler is properly informed

  async kill(timeout: float = 5, reason: str = 'nanny-kill') → None

    Kill the local worker process

    Blocks until both the process is down and the scheduler is properly informed

  log_event(topic, msg)

    Log an event under a given topic

    Parameters:
      topic str, list[str]

        Name of the topic under which to log an event. To log the same event under multiple topics, pass a list of topic names.

      msg

        Event message to log. Note this must be msgpack serializable.

    See also

    `Client.log_event`
  async start_unsafe()

    Start nanny, start local process, start watching