Posted in 2018

Dask Version 1.0

We are pleased to announce the release of Dask version 1.0.0!

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Dask-jobqueue

This work was done in collaboration with Matthew Rocklin (Anaconda), Jim Edwards (NCAR), Guillaume Eynard-Bontemps (CNES), and Loïc Estève (INRIA), and is supported, in part, by the US National Science Foundation Earth Cube program. The dask-jobqueue package is a spinoff of the Pangeo Project. This blogpost was previously published here

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Refactor Documentation

This work is supported by Anaconda Inc

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Dask Development Log

This work is supported by Anaconda Inc

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Dask Release 0.19.0

This work is supported by Anaconda Inc.

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High level performance of Pandas, Dask, Spark, and Arrow

This work is supported by Anaconda Inc

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Building SAGA optimization for Dask arrays

This work is supported by ETH Zurich, Anaconda Inc, and the Berkeley Institute for Data Science

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Dask Development Log

This work is supported by Anaconda Inc

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Pickle isn't slow, it's a protocol

This work is supported by Anaconda Inc

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Dask Development Log, Scipy 2018

This work is supported by Anaconda Inc

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Who uses Dask?

This work is supported by Anaconda Inc

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Dask Development Log

This work is supported by Anaconda Inc

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Dask Scaling Limits

This work is supported by Anaconda Inc.

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Dask Release 0.18.0

This work is supported by Anaconda Inc.

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Beyond Numpy Arrays in Python

Document headings start at H2, not H1 [myst.header]

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Dask Release 0.17.2

This work is supported by Anaconda Inc. and the Data Driven Discovery Initiative from the Moore Foundation.

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Craft Minimal Bug Reports

Following up on a post on supporting users in open source this post lists some suggestions on how to ask a maintainer to help you with a problem.

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Dask Release 0.17.0

This work is supported by Anaconda Inc. and the Data Driven Discovery Initiative from the Moore Foundation.

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Credit Modeling with Dask

This post explores a real-world use case calculating complex credit models in Python using Dask. It is an example of a complex parallel system that is well outside of the traditional “big data” workloads.

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Pangeo: JupyterHub, Dask, and XArray on the Cloud

This work is supported by Anaconda Inc, the NSF EarthCube program, and UC Berkeley BIDS

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