Quickstart¶
Install¶
$ python -m pip install dask distributed --upgrade
See installation document for more information.
Setup Dask.distributed the Easy Way¶
If you create a client without providing an address it will start up a local scheduler and worker for you.
>>> from dask.distributed import Client
>>> client = Client() # set up local cluster on your laptop
>>> client
<Client: scheduler="127.0.0.1:8786" processes=8 cores=8>
Setup Dask.distributed the Hard Way¶
This allows dask.distributed to use multiple machines as workers.
Set up scheduler and worker processes on your local computer:
$ dask-scheduler
Scheduler started at 127.0.0.1:8786
$ dask-worker 127.0.0.1:8786
$ dask-worker 127.0.0.1:8786
$ dask-worker 127.0.0.1:8786
Note
At least one dask-worker must be running after launching a
scheduler.
Launch a Client and point it to the IP/port of the scheduler.
>>> from dask.distributed import Client
>>> client = Client('127.0.0.1:8786')
See setup documentation for advanced use.
Map and Submit Functions¶
Use the map and submit methods to launch computations on the cluster.
The map/submit functions send the function and arguments to the remote
workers for processing. They return Future objects that refer to remote
data on the cluster. The Future returns immediately while the computations
run remotely in the background.
>>> def square(x):
return x ** 2
>>> def neg(x):
return -x
>>> A = client.map(square, range(10))
>>> B = client.map(neg, A)
>>> total = client.submit(sum, B)
>>> total.result()
-285
Gather¶
The map/submit functions return Future objects, lightweight tokens that
refer to results on the cluster. By default the results of computations
stay on the cluster.
>>> total # Function hasn't yet completed
<Future: status: waiting, key: sum-58999c52e0fa35c7d7346c098f5085c7>
>>> total # Function completed, result ready on remote worker
<Future: status: finished, key: sum-58999c52e0fa35c7d7346c098f5085c7>
Gather results to your local machine either with the Future.result method
for a single future, or with the Client.gather method for many futures at
once.
>>> total.result() # result for single future
-285
>>> client.gather(A) # gather for many futures
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]