Talks & Tutorials
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Dask Tutorial
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`Dask Tutorial `__ provides an overview of Dask and is typically delivered in 3 hours.
See `Parallel and Distributed Computing in Python with Dask `__ for the
latest Dask Tutorial recording from SciPy 2020.
Dask Slides
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`Dask Slides `__ provide a quick overview of the motivation for Dask.
Dask YouTube channel
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You can find lots of videos about Dask on the `Dask YouTube channel `__
.. contents:: :local:
Presentations
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* Dask Summit 2021
* `Keynotes `__
* `Workshops and Tutorials `__
* `Talks `__
* PyCon US 2021
* `Tutorial: Hacking Dask: Diving into Dask's Internals `__ (`materials `__)
* `Dask-SQL: Empowering Pythonistas for Scalable End-to-End Data Engineering `__
* BlazingSQL Webinars, May 2021
* `Intro to distributed computing on GPUs with Dask in Python `__ (`materials `__)
* PyData DC, August 2021
* `Inside Dask `__ (`materials `__)
* PyCon US 2020
* `Deploying Python at Scale with Dask `__
* PyCon Australia 2020
* `dask-image: distributed image processing for large data `__
* PyCon Korea 2019, August 2019
* `Adapting from Spark to Dask: what to expect (18 minutes)
`__
* SciPy 2019, July 2019
* `Refactoring the SciPy Ecosystem for Heterogeneous Computing (29 minutes)
`__
* `Renewable Power Forecast Generation with Dask & Visualization with Bokeh (31 minutes)
`__
* `Efficient Atmospheric Analogue Selection with Xarray and Dask (18 minutes)
`__
* `Better and Faster Hyper Parameter Optimization with Dask (27 minutes)
`__
* `Dask image:A Library for Distributed Image Processing (22 minutes)
`__
* EuroPython 2019, July 2019
* `Distributed Multi-GPU Computing with Dask, CuPy and RAPIDS (29 minutes)
`__
* SciPy 2018, July 2018
* `Scalable Machine Learning with Dask (30 minutes)
`__
* PyCon 2018, May 2018
* `Democratizing Distributed Computing with Dask and JupyterHub (32 minutes)
`__
* AMS & ESIP, January 2018
* `Pangeo quick demo: Dask, XArray, Zarr on the cloud with JupyterHub (3 minutes)
`__
* `Pangeo talk: An open-source big data science platform with Dask, XArray, Zarr on the cloud with JupyterHub (43 minutes)
`__
* PYCON.DE 2017, November 2017
* `Dask: Parallelism in Python (1 hour, 2 minutes)
`__
* PYCON 2017, May 2017
* `Dask: A Pythonic Distributed Data Science Framework (46 minutes)
`__
* PLOTCON 2016, December 2016
* `Visualizing Distributed Computations with Dask and Bokeh (33 minutes)
`__
* PyData DC, October 2016
* `Using Dask for Parallel Computing in Python (44 minutes)
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* SciPy 2016, July 2016
* `Dask Parallel and Distributed Computing (28 minutes)
`__
* PyData NYC, December 2015
* `Dask Parallelizing NumPy and Pandas through Task Scheduling (33 minutes)
`__
* PyData Seattle, August 2015
* `Dask: out of core arrays with task scheduling (1 hour, 50 minutes)
`__
* SciPy 2015, July 2015
* `Dask Out of core NumPy:Pandas through Task Scheduling (16 minutes)
`__