Setup Dask.distributed the Easy Way¶
If you create an 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 lapotp >>> 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
At least one
dask-worker must be running after launching a
Launch an 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 for advanced use.
Map and Submit Functions¶
submit methods to launch computations on the cluster.
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
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
for a single future, or with the
Client.gather method for many futures at
>>> total.result() # result for single future -285 >>> client.gather(A) # gather for many futures [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]