0.17.5

Getting Started

  • Install Dask
  • Setup
  • Use Cases
  • Examples
  • Community
  • Why Dask?

User Interface

  • User Interfaces
  • Array
  • Bag
  • Dataframe
  • Delayed
  • Futures
  • Machine Learning
  • API

Scheduling

  • Scheduling
  • Distributed Scheduling

Diagnostics

  • Understanding Performance
  • Visualize task graphs
  • Diagnostics (local)
  • Diagnostics (distributed)
  • Debugging

Graphs

  • Overview
  • Specification
  • Custom Graphs
  • Optimization
  • Custom Collections

Help & reference

  • Development Guidelines
  • Changelog
  • Presentations On Dask
  • Dask Cheat Sheet
  • Comparison to Spark
  • Opportunistic Caching
  • Internal Data Ingestion
  • Remote Data
  • Citations
  • Funding
  • Images and Logos
Dask
  • Docs »
  • Presentations On Dask
  • View page source

Presentations On DaskΒΆ

  • 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)
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