This repository contains the materials for my "Parallel Computing in Python with Dask" talk at Data Driven Wisconsin 2019.

An interactive version of the notebook from this talk is available by clicking the "launch binder" button below:

Step 1: Create Conda environment

A Conda environment with the dependencies needed to run the notebook from this talk can be created with:

conda env create --name ddw-dask --file binder/environment.yml

Step 2: Activate Conda environment

Activate the Conda environment:

conda activate ddw-dask

Step 3: Install Dask JupyterLab extension (optional)

The Dask JupyterLab extension can be installed with:

jupyter labextension install dask-labextension

inside the activated Conda environment.

Step 4: Launch JupyterLab

The notebook can then be launched with:

jupyter lab ddw-dask.ipynb

• Dask links:

  • GitHub repository: https://github.com/dask/dask

  • Documentation: https://docs.dask.org

• There are lots of great Dask tutorial from various conference on YouTube. For example:

  • "Parallelizing Scientific Python with Dask" @ SciPy 2018: YouTube

  • "Scalable Machine Learning with Dask" @ SciPy 2018: YouTube

• If you have a Dask usage questions, please ask it on Stack Overflow with the #dask tag. Dask developers monitor this tag and will answer questions.

• If you run into a bug or have a feature request, please feel free to file a report on the Dask GitHub issue tracker.