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Dask Blog

Motivation
Setup
Setup
Introduction
Introduction

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Motivation
Setup
Setup
Introduction
Introduction
Introduction
Introduction
Dask arrays work
Introduction
Evaluate dask graphs
Most Parallel Computation is Simple
Recent Parallel Work Focuses on Big Collections
Humans Repeat Stuff
Distributed Computing
Collections
GitHub Archive Data on S3
Play with Distributed Data
Setup
Identify a Problem
TL;DR.
What is grid search?
Introduction
Major Changes and Features
Biggest difference: Worker state and communication
Example: Kubernetes
Embedded Bokeh Servers in Dask Workers
Dask array without known chunk sizes
Rewriting Load Balancing
Load Balancing Cleanup
Summary
Summary
Summary
Stability enhancements and micro-release
Summary
Stability enhancements and micro-release
Summary
Summary
Dask-GLM and iterative algorithms
Summary
Summary
Arrays
Summary
Summary
Joblib
Arrays
NumPy ufuncs operate as Dask.array ufuncs
CSV is convenient, but slow
Summary
New dask-core and dask conda packages
Deploying Dask with MPI
TL;DR:
Masked Arrays
Summary
Summary
Cython
Breaking Changes
Community Communication
The Problem
This is a guest post
Deprecations
Minimal Complete Verifiable Examples
Tornado 5.0
Executive Summary
Context
History
Yarn deployment
Easy to Contribute
Dask on HPC Machines
Pickle is slow
Stateful processing with Actors
Start
Question
Notable Changes
1: Update Dask Examples to use JupyterLab extension
Summary
Introduction
What 1.0 means to us
Combine Dask Array with CuPy
Executive Summary
Summary
Notable Changes
Summary
Executive Summary
Summary
Summary
Setup
Numba Stencils
Executive Summary
Reasons why we use Dask
Executive Summary
Executive Summary
Install
TL;DR
Notable Changes
2019 Dask User Survey Results
Executive Summary
Power Architecture
First, why would you do this?
Problem
Groupby Aggregations with Dask
Summary
Bots dominate download counts
Who came?
Summary
Video
Summary
Manual setup
How to host a distributed Dask cluster
Consistency with the Scikit-Learn API
Why should open source projects run tutorials
History
Highlights
Summary
History
Executive Summary
The problem: fixed processing chunks and a high memory/CPU ratio {#problem}
Executive Summary
Executive Summary
Executive Summary
Motivation for change
Executive Summary
Why take the survey?
In the beginning
Motivation
Executive Summary
Executive Summary
Executive Summary
Overview
Contents
Progress Overview
Summary
Executive Summary
Summary
Introduction
Executive Summary
Summary
What is meta?
What is an operator?
Slow is smooth
Visualization at Lightning Speed
Dispatching for Array Creation
What is Flyte?
What is Shuffling?
What is from_map?
What does this mean for me?
Nightly testing
Introduction
Intro
What is GroupBy.map?

GitHub

Dask Blog

GitHub

Motivation
Setup
Setup
Introduction
Introduction
Introduction
Introduction
Dask arrays work
Introduction
Evaluate dask graphs
Most Parallel Computation is Simple
Recent Parallel Work Focuses on Big Collections
Humans Repeat Stuff
Distributed Computing
Collections
GitHub Archive Data on S3
Play with Distributed Data
Setup
Identify a Problem
TL;DR.
What is grid search?
Introduction
Major Changes and Features
Biggest difference: Worker state and communication
Example: Kubernetes
Embedded Bokeh Servers in Dask Workers
Dask array without known chunk sizes
Rewriting Load Balancing
Load Balancing Cleanup
Summary
Summary
Summary
Stability enhancements and micro-release
Summary
Stability enhancements and micro-release
Summary
Summary
Dask-GLM and iterative algorithms
Summary
Summary
Arrays
Summary
Summary
Joblib
Arrays
NumPy ufuncs operate as Dask.array ufuncs
CSV is convenient, but slow
Summary
New dask-core and dask conda packages
Deploying Dask with MPI
TL;DR:
Masked Arrays
Summary
Summary
Cython
Breaking Changes
Community Communication
The Problem
This is a guest post
Deprecations
Minimal Complete Verifiable Examples
Tornado 5.0
Executive Summary
Context
History
Yarn deployment
Easy to Contribute
Dask on HPC Machines
Pickle is slow
Stateful processing with Actors
Start
Question
Notable Changes
1: Update Dask Examples to use JupyterLab extension
Summary
Introduction
What 1.0 means to us
Combine Dask Array with CuPy
Executive Summary
Summary
Notable Changes
Summary
Executive Summary
Summary
Summary
Setup
Numba Stencils
Executive Summary
Reasons why we use Dask
Executive Summary
Executive Summary
Install
TL;DR
Notable Changes
2019 Dask User Survey Results
Executive Summary
Power Architecture
First, why would you do this?
Problem
Groupby Aggregations with Dask
Summary
Bots dominate download counts
Who came?
Summary
Video
Summary
Manual setup
How to host a distributed Dask cluster
Consistency with the Scikit-Learn API
Why should open source projects run tutorials
History
Highlights
Summary
History
Executive Summary
The problem: fixed processing chunks and a high memory/CPU ratio {#problem}
Executive Summary
Executive Summary
Executive Summary
Motivation for change
Executive Summary
Why take the survey?
In the beginning
Motivation
Executive Summary
Executive Summary
Executive Summary
Overview
Contents
Progress Overview
Summary
Executive Summary
Summary
Introduction
Executive Summary
Summary
What is meta?
What is an operator?
Slow is smooth
Visualization at Lightning Speed
Dispatching for Array Creation
What is Flyte?
What is Shuffling?
What is from_map?
What does this mean for me?
Nightly testing
Introduction
Intro
What is GroupBy.map?

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