0.17.5
Getting Started
Install Dask
Setup
Use Cases
Examples
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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
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Dask
Docs
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Presentations On Dask
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Presentations On Dask
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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)