Dask supports a real-time task framework that extends Python’s concurrent.futures interface. This interface is good for arbitrary task scheduling like dask.delayed, but is immediate rather than lazy, which provides some more flexibility in situations where the computations may evolve over time.

These features depend on the second generation task scheduler found in dask.distributed (which, despite its name, runs very well on a single machine).


Visit https://examples.dask.org/futures.html to see and run examples using futures with Dask.

Start Dask Client

You must start a Client to use the futures interface. This tracks state among the various worker processes or threads:

from dask.distributed import Client

client = Client()  # start local workers as processes
# or
client = Client(processes=False)  # start local workers as threads

If you have Bokeh installed, then this starts up a diagnostic dashboard at http://localhost:8787 .

Submit Tasks


You can submit individual tasks using the submit method:

def inc(x):
    return x + 1

def add(x, y):
    return x + y

a = client.submit(inc, 10)  # calls inc(10) in background thread or process
b = client.submit(inc, 20)  # calls inc(20) in background thread or process

The submit function returns a Future, which refers to a remote result. This result may not yet be completed:

>>> a
<Future: status: pending, key: inc-b8aaf26b99466a7a1980efa1ade6701d>

Eventually it will complete. The result stays in the remote thread/process/worker until you ask for it back explicitly:

>>> a
<Future: status: finished, type: int, key: inc-b8aaf26b99466a7a1980efa1ade6701d>

>>> a.result()  # blocks until task completes and data arrives

You can pass futures as inputs to submit. Dask automatically handles dependency tracking; once all input futures have completed, they will be moved onto a single worker (if necessary), and then the computation that depends on them will be started. You do not need to wait for inputs to finish before submitting a new task; Dask will handle this automatically:

c = client.submit(add, a, b)  # calls add on the results of a and b

Similar to Python’s map, you can use Client.map to call the same function and many inputs:

futures = client.map(inc, range(1000))

However, note that each task comes with about 1ms of overhead. If you want to map a function over a large number of inputs, then you might consider dask.bag or dask.dataframe instead.

Move Data


Given any future, you can call the .result method to gather the result. This will block until the future is done computing and then transfer the result back to your local process if necessary:

>>> c.result()

You can gather many results concurrently using the Client.gather method. This can be more efficient than calling .result() on each future sequentially:

>>> # results = [future.result() for future in futures]
>>> results = client.gather(futures)  # this can be faster

If you have important local data that you want to include in your computation, you can either include it as a normal input to a submit or map call:

>>> df = pd.read_csv('training-data.csv')
>>> future = client.submit(my_function, df)

Or you can scatter it explicitly. Scattering moves your data to a worker and returns a future pointing to that data:

>>> remote_df = client.scatter(df)
>>> remote_df
<Future: status: finished, type: DataFrame, key: bbd0ca93589c56ea14af49cba470006e>

>>> future = client.submit(my_function, remote_df)

Both of these accomplish the same result, but using scatter can sometimes be faster. This is especially true if you use processes or distributed workers (where data transfer is necessary) and you want to use df in many computations. Scattering the data beforehand avoids excessive data movement.

Calling scatter on a list scatters all elements individually. Dask will spread these elements evenly throughout workers in a round-robin fashion:

>>> client.scatter([1, 2, 3])
[<Future: status: finished, type: int, key: c0a8a20f903a4915b94db8de3ea63195>,
 <Future: status: finished, type: int, key: 58e78e1b34eb49a68c65b54815d1b158>,
 <Future: status: finished, type: int, key: d3395e15f605bc35ab1bac6341a285e2>]

References, Cancellation, and Exceptions


Dask will only compute and hold onto results for which there are active futures. In this way, your local variables define what is active in Dask. When a future is garbage collected by your local Python session, Dask will feel free to delete that data or stop ongoing computations that were trying to produce it:

>>> del future  # deletes remote data once future is garbage collected

You can also explicitly cancel a task using the Future.cancel or Client.cancel methods:

>>> future.cancel()  # deletes data even if other futures point to it

If a future fails, then Dask will raise the remote exceptions and tracebacks if you try to get the result:

def div(x, y):
    return x / y

>>> a = client.submit(div, 1, 0)  # 1 / 0 raises a ZeroDivisionError
>>> a
<Future: status: error, key: div-3601743182196fb56339e584a2bf1039>

>>> a.result()
      1 def div(x, y):
----> 2     return x / y

ZeroDivisionError: division by zero

All futures that depend on an erred future also err with the same exception:

>>> b = client.submit(inc, a)
>>> b
<Future: status: error, key: inc-15e2e4450a0227fa38ede4d6b1a952db>

You can collect the exception or traceback explicitly with the Future.exception or Future.traceback methods.

Waiting on Futures


You can wait on a future or collection of futures using the wait function:

from dask.distributed import wait

>>> wait(futures)

This blocks until all futures are finished or have erred.

You can also iterate over the futures as they complete using the as_completed function:

from dask.distributed import as_completed

futures = client.map(score, x_values)

best = -1
for future in as_completed(futures):
   y = future.result()
   if y > best:
       best = y

For greater efficiency, you can also ask as_completed to gather the results in the background:

for future, result in as_completed(futures, with_results=True):
    # y = future.result()  # don't need this

Or collect all futures in batches that had arrived since the last iteration:

for batch in as_completed(futures, with_results=True).batches():
   for future, result in batch:

Additionally, for iterative algorithms, you can add more futures into the as_completed iterator during iteration:

seq = as_completed(futures)

for future in seq:
    y = future.result()
    if condition(y):
        new_future = client.submit(...)
        seq.add(new_future)  # add back into the loop

Fire and Forget


Sometimes we don’t care about gathering the result of a task, and only care about side effects that it might have like writing a result to a file:

>>> a = client.submit(load, filename)
>>> b = client.submit(process, a)
>>> c = client.submit(write, b, out_filename)

As noted above, Dask will stop work that doesn’t have any active futures. It thinks that because no one has a pointer to this data that no one cares. You can tell Dask to compute a task anyway, even if there are no active futures, using the fire_and_forget function:

from dask.distributed import fire_and_forget

>>> fire_and_forget(c)

This is particularly useful when a future may go out of scope, for example, as part of a function:

def process(filename):
    out_filename = 'out-' + filename
    a = client.submit(load, filename)
    b = client.submit(process, a)
    c = client.submit(write, b, out_filename)
    return  # here we lose the reference to c, but that's now ok

for filename in filenames:

Submit Tasks from Tasks


This is an advanced feature and is rarely necessary in the common case.

Tasks can launch other tasks by getting their own client. This enables complex and highly dynamic workloads:

from dask.distributed import get_client

def my_function(x):

    # Get locally created client
    client = get_client()

    # Do normal client operations, asking cluster for computation
    a = client.submit(...)
    b = client.submit(...)
    a, b = client.gather([a, b])

    return a + b

It also allows you to set up long running tasks that watch other resources like sockets or physical sensors:

def monitor(device):
   client = get_client()
   while True:
       data = device.read_data()
       future = client.submit(process, data)

for device in devices:

However, each running task takes up a single thread, and so if you launch many tasks that launch other tasks, then it is possible to deadlock the system if you are not careful. You can call the secede function from within a task to have it remove itself from the dedicated thread pool into an administrative thread that does not take up a slot within the Dask worker:

from dask.distributed import get_client, secede

def monitor(device):
   client = get_client()
   secede()  # remove this task from the thread pool
   while True:
       data = device.read_data()
       future = client.submit(process, data)

If you intend to do more work in the same thread after waiting on client work, you may want to explicitly block until the thread is able to rejoin the thread pool. This allows some control over the number of threads that are created and stops too many threads from being active at once, over-saturating your hardware:

def f(n):  # assume that this runs as a task
   client = get_client()

   secede()  # secede while we wait for results to come back
   futures = client.map(func, range(n))
   results = client.gather(futures)

   rejoin()  # block until a slot is open in the thread pool
   result = analyze(results)
   return result

Alternatively, you can just use the normal compute function within a task. This will automatically call secede and rejoin appropriately:

def f(name, fn):
    df = dd.read_csv(fn)  # note that this is a dask collection
    result = df[df.name == name].count()

    # This calls secede
    # Then runs the computation on the cluster (including this worker)
    # Then blocks on rejoin, and finally delivers the answer
    result = result.compute()

    return result

Coordination Primitives


Sometimes situations arise where tasks, workers, or clients need to coordinate with each other in ways beyond normal task scheduling with futures. In these cases Dask provides additional primitives to help in complex situations.

Dask provides distributed versions of coordination primitives like locks, events, queues, global variables, and pub-sub systems that, where appropriate, match their in-memory counterparts. These can be used to control access to external resources, track progress of ongoing computations, or share data in side-channels between many workers, clients, and tasks sensibly.

These features are rarely necessary for common use of Dask. We recommend that beginning users stick with using the simpler futures found above (like Client.submit and Client.gather) rather than embracing needlessly complex techniques.



Dask queues follow the API for the standard Python Queue, but now move futures or small messages between clients. Queues serialize sensibly and reconnect themselves on remote clients if necessary:

from dask.distributed import Queue

def load_and_submit(filename):
    data = load(filename)
    client = get_client()
    future = client.submit(process, data)

client = Client()

queue = Queue()

for filename in filenames:
    future = client.submit(load_and_submit, filename)

while True:
    future = queue.get()

Queues can also send small pieces of information, anything that is msgpack encodable (ints, strings, bools, lists, dicts, etc.). This can be useful to send back small scores or administrative messages:

def func(x):
    except Exception as e:

error_queue = Queue()

Queues are mediated by the central scheduler, and so they are not ideal for sending large amounts of data (everything you send will be routed through a central point). They are well suited to move around small bits of metadata, or futures. These futures may point to much larger pieces of data safely:

>>> x = ... # my large numpy array

# Don't do this!
>>> q.put(x)

# Do this instead
>>> future = client.scatter(x)
>>> q.put(future)

# Or use futures for metadata
>>> q.put({'status': 'OK', 'stage=': 1234})

If you’re looking to move large amounts of data between workers, then you might also want to consider the Pub/Sub system described a few sections below.

Global Variables


Variables are like Queues in that they communicate futures and small data between clients. However, variables hold only a single value. You can get or set that value at any time:

>>> var = Variable('stopping-criterion')
>>> var.set(False)

>>> var.get()

This is often used to signal stopping criteria or current parameters between clients.

If you want to share large pieces of information, then scatter the data first:

>>> parameters = np.array(...)
>>> future = client.scatter(parameters)
>>> var.set(future)



You can also hold onto cluster-wide locks using the Lock object. Dask Locks have the same API as normal threading.Lock objects, except that they work across the cluster:

from dask.distributed import Lock
lock = Lock()

with lock:
    # access protected resource

You can manage several locks at the same time. Lock can either be given a consistent name or you can pass the lock object around itself.

Using a consistent name is convenient when you want to lock some known named resource:

from dask.distributed import Lock

def load(fn):
    with Lock('the-production-database'):
        # read data from filename using some sensitive source
        return ...

futures = client.map(load, filenames)

Passing around a lock works as well and is easier when you want to create short-term locks for a particular situation:

from dask.distributed import Lock
lock = Lock()

def load(fn, lock=None):
    with lock:
        # read data from filename using some sensitive source
        return ...

futures = client.map(load, filenames, lock=lock)

This can be useful if you want to control concurrent access to some external resource like a database or un-thread-safe library.



Dask Events mimic asyncio.Event objects, but on a cluster scope. They hold a single flag which can be set or cleared. Clients can wait until the event flag is set. Different from a Lock, every client can set or clear the flag and there is no “ownership” of an event.

You can use events to e.g. synchronize multiple clients:

# One one client
from dask.distributed import Event

event = Event("my-event-1")

The call to wait will block until the event is set, e.g. in another client

# In another client
from dask.distributed import Event

event = Event("my-event-1")

# do some work


Events can be set, cleared and waited on multiple times. Every waiter referencing the same event name will be notified on event set (and not only the first one as in the case of a lock):

from dask.distributed import Event

def wait_for_event(x):
   event = Event("my-event")

   # at this point, all function calls
   # are in sync once the event is set

futures = client.map(wait_for_event, range(10))




Similar to the single-valued Lock it is also possible to use a cluster-wide semaphore to coordinate and limit access to a sensitive resource like a database.

from dask.distributed import Semaphore

sem = Semaphore(max_leases=2, name="database")

def access_limited(val, sem):
   with sem:
      # Interact with the DB

futures = client.map(access_limited, range(10), sem=sem)



Dask implements the Publish Subscribe pattern, providing an additional channel of communication between ongoing tasks.



This is an advanced feature and is rarely necessary in the common case.


This is an experimental feature and is subject to change without notice.

Actors allow workers to manage rapidly changing state without coordinating with the central scheduler. This has the advantage of reducing latency (worker-to-worker roundtrip latency is around 1ms), reducing pressure on the centralized scheduler (workers can coordinate actors entirely among each other), and also enabling workflows that require stateful or in-place memory manipulation.

However, these benefits come at a cost. The scheduler is unaware of actors and so they don’t benefit from diagnostics, load balancing, or resilience. Once an actor is running on a worker it is forever tied to that worker. If that worker becomes overburdened or dies, then there is no opportunity to recover the workload.

Because Actors avoid the central scheduler they can be high-performing, but not resilient.

Example: Counter

An actor is a class containing both state and methods that is submitted to a worker:

class Counter:
    n = 0

    def __init__(self):
        self.n = 0

    def increment(self):
        self.n += 1
        return self.n

from dask.distributed import Client
client = Client()

future = client.submit(Counter, actor=True)
counter = future.result()

>>> counter
<Actor: Counter, key=Counter-afa1cdfb6b4761e616fa2cfab42398c8>

Method calls on this object produce ActorFutures, which are similar to normal Futures, but interact only with the worker holding the Actor:

>>> future = counter.increment()
>>> future

>>> future.result()

Attribute access is synchronous and blocking:

>>> counter.n

Example: Parameter Server

This example will perform the following minimization with a parameter server:

\[\min_{p\in\mathbb{R}^{1000}} \sum_{i=1}^{1000} (p_i - 1)^2\]

This is a simple minimization that will serve as an illustrative example.

The Dask Actor will serve as the parameter server that will hold the model. The client will calculate the gradient of the loss function above.

import numpy as np

from dask.distributed import Client
client = Client(processes=False)

class ParameterServer:
    def __init__(self):
        self.data = dict()

    def put(self, key, value):
        self.data[key] = value

    def get(self, key):
        return self.data[key]

def train(params, lr=0.1):
    grad = 2 * (params - 1)  # gradient of (params - 1)**2
    new_params = params - lr * grad
    return new_params

ps_future = client.submit(ParameterServer, actor=True)
ps = ps_future.result()

ps.put('parameters', np.random.random(1000))
for k in range(20):
    params = ps.get('parameters').result()
    new_params = train(params)
    ps.put('parameters', new_params)
    # k=0: "0.5988202981316124"
    # k=10: "0.9569236575164062"

This example works, and the loss function is minimized. The (simple) equation above is minimize, so each \(p_i\) converges to 1. If desired, this example could be adapted to machine learning with a more complex function to minimize.

Asynchronous Operation

All operations that require talking to the remote worker are awaitable:

async def f():
    future = client.submit(Counter, actor=True)
    counter = await future  # gather actor object locally

    counter.increment()  # send off a request asynchronously
    await counter.increment()  # or wait until it was received

    n = await counter.n  # attribute access also must be awaited