This tutorial was initally given at the Dask Distributed Summit 2021.

Dask-ML provides scalable machine learning in Python using Dask alongside popular machine learning libraries like Scikit-Learn, XGBoost, and others.

We generally consider two primary challenges when scaling machine learning code, scaling model size and data size. dask-ml offers ways to address each.

git clone http://github.com/programmylife/dask-ml-tutorial-summit-2021

and then install necessary packages. There are three different ways to achieve this, pick the one that best suits you, and only pick one option. They are, in order of preference:

In the main repo directory

conda env create -f environment.yml
conda activate dask-ml-tutorial
jupyter labextension install dask-labextension
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install @bokeh/jupyter_bokeh