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Distributed Model Training with Dask and SciKit-Learn

Matthieu Lamairesse shows us how we can use Dask to perform distributed ML model training:

Dask is an open-source parallel computing framework written natively in Python (initially released 2014). It has a significant following and support largely due to its good integration with the popular Python ML ecosystem triumvirate that is NumPy, Pandas and Scikit-learn. 

Why Dask over other distributed machine learning frameworks? 

In the context of this article it’s about Dask’s tight integration with Sckit-learn’s JobLib parallel computing library that allows us to distribute Scikit-learn code with (almost) no code change, making it a very interesting framework to accelerate ML training. 

Click through for an interesting article and an example of using this on Cloudera’s ML platform.

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