Presentations On Dask ===================== * 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) `__