Many Scikit-Learn algorithms are written for parallel execution using Joblib, which natively provides thread-based and process-based parallelism. Joblib is what backs the n_jobs= parameter in normal use of Scikit-Learn.

Dask can scale these Joblib-backed algorithms out to a cluster of machines by providing an alternative Joblib backend. The following video demonstrates how to use Dask to parallelize a grid search across a cluster.

To use the Dask backend to Joblib you have to create a Client, and wrap your code with joblib.parallel_backend('dask').

from dask.distributed import Client
from scikit_learn.externals import joblib

client = Client(processes=False)             # create local cluster
# client = Client("scheduler-address:8786")  # or connect to remote cluster

with joblib.parallel_backend('dask'):
    # Your scikit-learn code

As an example you might distribute a randomized cross validated parameter search as follows:

import numpy as np
from dask.distributed import Client

from sklearn.externals import joblib
from sklearn.datasets import load_digits
from sklearn.model_selection import RandomizedSearchCV
from sklearn.svm import SVC

client = Client(processes=False)             # create local cluster

digits = load_digits()

param_space = {
    'C': np.logspace(-6, 6, 13),
    'gamma': np.logspace(-8, 8, 17),
    'tol': np.logspace(-4, -1, 4),
    'class_weight': [None, 'balanced'],

model = SVC(kernel='rbf')
search = RandomizedSearchCV(model, param_space, cv=3, n_iter=50, verbose=10)

with joblib.parallel_backend('dask'):,

Note that the Dask joblib backend is useful for scaling out CPU-bound workloads; workloads with datasets that fit in RAM, but have many individual operations that can be done in parallel. To scale out to RAM-bound workloads (larger-than-memory datasets) use one of the following alternatives: