Quickstart¶
Setup Dask.distributed the Hard Way¶
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 an Executor and point it to the IP/port of the scheduler.
>>> from distributed import Executor
>>> executor = Executor('127.0.0.1:8786')
See setup for advanced use.
Setup Dask.distributed the Easy Way¶
If you create an executor without providing an address it will start up a local scheduler and worker for you.
>>> from distributed import Executor
>>> executor = Executor()
>>> executor
<Executor: scheduler="127.0.0.1:8786" processes=8 cores=8>
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 = executor.map(square, range(10))
>>> B = executor.map(neg, A)
>>> total = executor.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 Executor.gather method for many futures at
once.
>>> total.result() # result for single future
-285
>>> executor.gather(A) # gather for many futures
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]