The Client is the primary entry point for users of
After we setup a cluster, we initialize a
Client by pointing
it to the address of a
>>> 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
Futureobjects, 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 for a thorough list.
We can submit individual function calls with the
client.submit method or
many function calls with the
>>> 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
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
See efficiency for more information on efficient use of
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
.compute() methods will automatically start using the
client = Client('scheduler:8786') my_dataframe.sum().compute() # Now uses the distributed system by default
We can stop this behavior by using the
argument when starting the Client.
.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
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.
Pure Functions 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
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
keyword argument. In this case keys are randomly generated (by
>>> 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
asynchronous=True keyword and
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
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 documentation for more information.