https://github.com/jcrist/dask-tutorial-pydata-seattle-2017
Dask Tutorial for PyData Seattle 2017
https://github.com/jcrist/dask-tutorial-pydata-seattle-2017
Last synced: 28 days ago
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Dask Tutorial for PyData Seattle 2017
- Host: GitHub
- URL: https://github.com/jcrist/dask-tutorial-pydata-seattle-2017
- Owner: jcrist
- Created: 2017-06-29T22:17:26.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2022-11-01T02:55:38.000Z (almost 3 years ago)
- Last Synced: 2025-04-08T22:34:24.742Z (6 months ago)
- Language: Jupyter Notebook
- Size: 41.1 MB
- Stars: 21
- Watchers: 5
- Forks: 12
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# Dask Tutorial for PyData Seattle 2017
This contains materials for the dask tutorial [Parallelizing Scientific Python
with Dask](https://pydata.org/seattle2017/schedule/presentation/58/).
## Setup
This tutorial is designed to run in an online environment. You shouldn't need
to install anything, instead just go to the following link and click the big
blue button:
[https://pycon-parallel.jovyan.org](https://pycon-parallel.jovyan.org)
### Running Locally
If you want to run this tutorial locally instead, you can use the following
instructions. Note that the material in notebooks 3 & 4 will not work 100%
without the environment setup in the link above. Everything else should work
fine though.
To setup locally, clone the repo and install all required dependencies:
```bash
$ git clone https://github.com/jcrist/dask-tutorial-pydata-seattle-2017
$ conda install dask distributed matplotlib s3fs jupyter -c conda-forge
$ pip install graphviz
```
Then start a jupyter notebook inside the cloned directory
```bash
$ cd dask-tutorial-pydata-seattle-2017
$ jupyter notebook
```
## Acknowledgements
This tutorial wouldn't be possible without the work done by the larger Dask
community implementing much of the functionality found here. The materials here
are based off of a [previous dask
tutorial](https://github.com/mrocklin/dask-workshop) by Matt Rocklin.
We also thank Google for generously providing compute credits on [Google
Compute Engine](https://cloud.google.com/compute/), which was backed the
distributd clusters used during the tutorial at PyData Seattle.