This work is supported by Continuum Analytics the XDATA Program and the Data Driven Discovery Initiative from the Moore Foundation

Summary

This post describes two simple ways to use Dask to parallelize Scikit-Learn operations either on a single computer or across a cluster.

Use the Dask Joblib backend Use the dklearn projects drop-in replacements for Pipeline , GridSearchCV , and RandomSearchCV

For the impatient, these look like the following:

### Joblib from joblib import parallel_backend with parallel_backend ( 'dask.distributed' , scheduler_host = 'scheduler-address:8786' ): # your now-cluster-ified sklearn code here ### Dask-learn pipeline and GridSearchCV drop-in replacements # from sklearn.grid_search import GridSearchCV from dklearn.grid_search import GridSearchCV # from sklearn.pipeline import Pipeline from dklearn.pipeline import Pipeline

However, neither of these techniques are perfect. These are the easiest things to try, but not always the best solutions. This blogpost focuses on low-hanging fruit.

Joblib

Scikit-Learn already parallelizes across a multi-core CPU using Joblib, a simple but powerful and mature library that provides an extensible map operation. Here is a simple example of using Joblib on its own without sklearn:

# Sequential code from time import sleep def slowinc ( x ): sleep ( 1 ) # take a bit of time to simulate real work return x + 1 >>> [ slowinc ( i ) for i in range ( 10 )] # this takes 10 seconds [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] # Parallel code from joblib import Parallel , delayed >>> Parallel ( n_jobs = 4 )( delayed ( slowinc )( i ) for i in range ( 10 )) # this takes 3 seconds [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ]

Dask users will recognize the delayed function modifier. Dask stole the delayed decorator from Joblib.

Many of Scikit-learn’s parallel algorithms use Joblib internally. If we can extend Joblib to clusters then we get some added parallelism from joblib-enabled Scikit-learn functions immediately.

Distributed Joblib

Fortunately Joblib provides an interface for other parallel systems to step in and act as an execution engine. We can do this with the parallel_backend context manager to run with hundreds or thousands of cores in a nearby cluster:

import distributed.joblib from joblib import parallel_backend with parallel_backend ( 'dask.distributed' , scheduler_host = 'scheduler-address:8786' ): print ( Parallel ()( delayed ( slowinc )( i ) for i in list ( range ( 100 ))))

The main value for Scikit-learn users here is that Scikit-learn already uses joblib.Parallel within its code, so this trick works with the Scikit-learn code that you already have.

So we can use Joblib to parallelize normally on our multi-core processor:

estimator = GridSearchCV ( n_jobs = 4 , ...) # use joblib on local multi-core processor

or we can use Joblib together with Dask.distributed to parallelize across a multi-node cluster:

with parallel_backend ( 'dask.distributed' , scheduler_host = 'scheduler-address:8786' ): estimator = GridSearchCV (...) # use joblib with Dask cluster

(There will be a more thorough example towards the end)

Limitations

Joblib is used throughout many algorithms in Scikit-learn, but not all. Generally any operation that accepts an n_jobs= parameter is a possible choice.

From Dask’s perspective Joblib’s interface isn’t ideal. For example it will always collect intermediate results back to the main process, rather than leaving them on the cluster until necessary. For computationally intense operations this is fine but does add some unnecessary communication overhead. Also Joblib doesn’t allow for operations more complex than a parallel map, so the range of algorithms that this can parallelize is somewhat limited.

Still though, given the wide use of Joblib-accelerated workflows (particularly within Scikit-learn) this is a simple thing to try if you have a cluster nearby with a possible large payoff.

Dask-learn Pipeline and Gridsearch

In July 2016, Jim Crist built and wrote about a small project, dask-learn. This project was a collaboration with SKLearn developers and an attempt to see which parts of Scikit-learn were trivially and usefully parallelizable. By far the most productive thing to come out of this work were Dask variants of Scikit-learn’s Pipeline, GridsearchCV, and RandomSearchCV objects that better handle nested parallelism. Jim observed significant speedups over SKLearn code by using these drop-in replacements.

So if you replace the following imports you may get both better single-threaded performance and the ability to scale out to a cluster:

# from sklearn.grid_search import GridSearchCV from dklearn.grid_search import GridSearchCV # from sklearn.pipeline import Pipeline from dklearn.pipeline import Pipeline

Here is a simple example from Jim’s more in-depth blogpost:

from sklearn.datasets import make_classification X , y = make_classification ( n_samples = 10000 , n_features = 500 , n_classes = 2 , n_redundant = 250 , random_state = 42 ) from sklearn import linear_model , decomposition from sklearn.pipeline import Pipeline from dklearn.pipeline import Pipeline logistic = linear_model . LogisticRegression () pca = decomposition . PCA () pipe = Pipeline ( steps = [( 'pca' , pca ), ( 'logistic' , logistic )]) #Parameters of pipelines can be set using ‘__’ separated parameter names: grid = dict ( pca__n_components = [ 50 , 100 , 150 , 250 ], logistic__C = [ 1e-4 , 1.0 , 10 , 1e4 ], logistic__penalty = [ 'l1' , 'l2' ]) # from sklearn.grid_search import GridSearchCV from dklearn.grid_search import GridSearchCV estimator = GridSearchCV ( pipe , grid ) estimator . fit ( X , y )

SKLearn performs this computation in around 40 seconds while the dask-learn drop-in replacements take around 10 seconds. Also, if you add the following lines to connect to a running cluster the whole thing scales out:

from dask.distributed import Client c = Client ( 'scheduler-address:8786' )

Here is a live Bokeh plot of the computation on a tiny eight process “cluster” running on my own laptop. I’m using processes here to highlight the costs of communication between processes (red). It’s actually about 30% faster to run this computation within the same single process.

Conclusion

This post showed a couple of simple mechanisms for scikit-learn users to accelerate their existing workflows with Dask. These aren’t particularly sophisticated, nor are they performance-optimal, but they are easy to understand and easy to try out. In a future blogpost I plan to cover more complex ways in which Dask can accelerate sophisticated machine learning workflows.

What we could have done better

As always, I include a brief section on what went wrong or what we could have done better with more time.

See the bottom of Jim’s post for a more thorough explanation of “what we could have done better” for dask-learn’s pipeline and gridsearch

Joblib + Dask.distributed interaction is convenient, but leaves some performance on the table. It’s not clear how Dask can help the sklearn codebase without being too invasive.

It would have been nice to spin up an actual cluster on parallel hardware for this post. I wrote this quickly (in a few hours) so decided to skip this. If anyone wants to write a follow-on experiment I would be happy to publish it.

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