diff --git a/docs/source/index.rst b/docs/source/index.rst index a31b63e50a4..23f17dc1b9b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -136,7 +136,7 @@ messy situations in everyday problems. Install Dask 10-minutes-to-dask.rst - presentations.rst + deploying.rst Best Practices faq.rst @@ -150,7 +150,6 @@ messy situations in everyday problems. DataFrame Delayed futures.rst - deploying.rst .. toctree:: :maxdepth: 1 @@ -174,6 +173,7 @@ messy situations in everyday problems. changelog.rst configuration.rst how-to/index.rst + presentations.rst maintainers.rst .. _`Anaconda Inc`: https://www.anaconda.com diff --git a/docs/source/presentations.rst b/docs/source/presentations.rst index 487a736f171..2f1099f8b7c 100644 --- a/docs/source/presentations.rst +++ b/docs/source/presentations.rst @@ -13,127 +13,13 @@ Talks & Tutorials Dask Tutorial ------------- + `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 ------------ -`Dask Slides `__ provide a quick overview of the motivation for Dask. - Dask YouTube channel -------------------- You can find lots of videos about Dask on the `Dask YouTube channel `__ .. contents:: :local: - -Presentations -------------- - -* 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) - `__ - -* 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) - `__