The Client is the primary entry point for users of dask.distributed
.
After we setup a cluster, we initialize a Client
by pointing it to the address of a Scheduler
:
>>> from distributed import Client
>>> client = Client('127.0.0.1:8786')
There are a few different ways to interact with the cluster through the client:
- The Client satisfies most of the standard concurrent.futures - PEP-3148 interface with
.submit
,.map
functions andFuture
objects, allowing the immediate and direct submission of tasks. - The Client registers itself as the default Dask scheduler, and so runs all dask collections like dask.array, dask.bag, dask.dataframe and dask.delayed
- The Client has additional methods for manipulating data remotely. See the full
API <api>
for a thorough list.
We can submit individual function calls with the client.submit
method or many function calls with the client.map
method
>>> def inc(x):
return x + 1
>>> x = client.submit(inc, 10)
>>> x
<Future - key: inc-e4853cffcc2f51909cdb69d16dacd1a5>
>>> L = client.map(inc, range(1000))
>>> L
[<Future - key: inc-e4853cffcc2f51909cdb69d16dacd1a5>,
<Future - key: inc-...>,
<Future - key: inc-...>,
<Future - key: inc-...>, ...]
These results live on distributed workers.
We can submit tasks on futures. The function will go to the machine where the futures are stored and run on the result once it has completed.
>>> y = client.submit(inc, x) # Submit on x, a Future
>>> total = client.submit(sum, L) # Map on L, a list of Futures
We gather back the results using either the Future.result
method for single futures or client.gather
method for many futures at once.
>>> x.result()
11
>>> client.gather(L)
[1, 2, 3, 4, 5, ...]
But, as always, we want to minimize communicating results back to the local process. It's often best to leave data on the cluster and operate on it remotely with functions like submit
, map
, get
and compute
. See efficiency <efficiency>
for more information on efficient use of distributed.
The parent library Dask contains objects like dask.array, dask.dataframe, dask.bag, and dask.delayed, which automatically produce parallel algorithms on larger datasets. All dask collections work smoothly with the distributed scheduler.
When we create a Client
object it registers itself as the default Dask scheduler. All .compute()
methods will automatically start using the distributed system.
client = Client('scheduler:8786')
my_dataframe.sum().compute() # Now uses the distributed system by default
We can stop this behavior by using the set_as_default=False
keyword argument when starting the Client.
Dask's normal .compute()
methods are synchronous, meaning that they block the interpreter until they complete. Dask.distributed allows the new ability of asynchronous computing, we can trigger computations to occur in the background and persist in memory while we continue doing other work. This is typically handled with the Client.persist
and Client.compute
methods which are used for larger and smaller result sets respectively.
>>> df = client.persist(df) # trigger all computations, keep df in memory
>>> type(df)
dask.DataFrame
For more information see the page on Managing Computation <manage-computation>
.
By default, distributed
assumes that all functions are pure. Pure functions:
- always return the same output for a given set of inputs
- do not have side effects, like modifying global state or creating files
If this is not the case, you should use the pure=False
keyword argument in methods like Client.map()
and Client.submit()
.
The client associates a key to all computations. This key is accessible on the Future object.
>>> from operator import add
>>> x = client.submit(add, 1, 2)
>>> x.key
'add-ebf39f96ad7174656f97097d658f3fa2'
This key should be the same across all computations with the same inputs and across all machines. If we run the computation above on any computer with the same environment then we should get the exact same key.
The scheduler avoids redundant computations. If the result is already in memory from a previous call then that old result will be used rather than recomputing it. Calls to submit or map are idempotent in the common case.
While convenient, this feature may be undesired for impure functions, like random
. In these cases two calls to the same function with the same inputs should produce different results. We accomplish this with the pure=False
keyword argument. In this case keys are randomly generated (by uuid4
.)
>>> import numpy as np
>>> client.submit(np.random.random, 1000, pure=False).key
'random_sample-fc814a39-ee00-42f3-8b6f-cac65bcb5556'
>>> client.submit(np.random.random, 1000, pure=False).key
'random_sample-a24e7220-a113-47f2-a030-72209439f093'
If we are operating in an asynchronous environment then the blocking functions listed above become asynchronous equivalents. You must start your client with the asynchronous=True
keyword and yield
or await
blocking functions.
async def f():
client = await Client(asynchronous=True)
future = client.submit(func, *args)
result = await future
return result
If you want to reuse the same client in asynchronous and synchronous environments you can apply the asynchronous=True
keyword at each method call.
client = Client() # normal blocking client
async def f():
futures = client.map(func, L)
results = await client.gather(futures, asynchronous=True)
return results
See the Asynchronous <asynchronous>
documentation for more information.
For more information on how to use dask.distributed you may want to look at the following pages:
Managing Memory <memory>
Managing Computation <manage-computation>
Data Locality <locality>
API <api>