Using dask distributed for single-machine parallel computing

This example shows the simplest usage of the dask distributed backend, on the local computer.

This is useful for prototyping a solution, to later be run on a truly distributed cluster, as the only change to be made is the address of the scheduler.

Another realistic usage scenario: combining dask code with joblib code, for instance using dask for preprocessing data, and scikit-learn for machine learning. In such a setting, it may be interesting to use distributed as a backend scheduler for both dask and joblib, to orchestrate well the computation.

Setup the distributed client

from dask.distributed import Client

# If you have a remote cluster running Dask
# client = Client('tcp://scheduler-address:8786')

# If you want Dask to set itself up on your personal computer
client = Client(processes=False)

Run parallel computation using dask.distributed

import time
import joblib

def long_running_function(i):
    return i

The verbose messages below show that the backend is indeed the dask.distributed one

with joblib.parallel_backend('dask'):
        for i in range(10))


[Parallel(n_jobs=-1)]: Using backend DaskDistributedBackend with 2 concurrent workers.
[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-1)]: Done   3 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-1)]: Done   5 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-1)]: Done   6 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-1)]: Done   7 tasks      | elapsed:    0.5s
[Parallel(n_jobs=-1)]: Done   8 out of  10 | elapsed:    0.6s remaining:    0.1s
[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:    0.7s remaining:    0.0s
[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:    0.7s finished

Progress in computation can be followed on the distributed web interface, see

Total running time of the script: ( 0 minutes 0.868 seconds)

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