Parallel Computing in Python with Dask @ Data-Driven Wisconsin
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
There are lots of great Dask tutorial from various conference on YouTube. For example:
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.