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)