Posted in 2019

Dask Deployment Updates

  • Nov 01, 2019

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DataFrame Groupby Aggregations

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Better and faster hyperparameter optimization with Dask

Scott Sievert wrote this post. The original post lives at https://stsievert.com/blog/2019/09/27/dask-hyperparam-opt/ with better styling. This work is supported by Anaconda, Inc.

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Co-locating a Jupyter Server and Dask Scheduler

If you want, you can have Dask set up a Jupyter notebook server for you, co-located with the Dask scheduler. There are many ways to do this, but this blog post lists two.

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Dask on HPC: a case study

Dask is deployed on traditional HPC machines with increasing frequency. In the past week I’ve personally helped four different groups get set up. This is a surprisingly individual process, because every HPC machine has its own idiosyncrasies. Each machine uses a job scheduler like SLURM/PBS/SGE/LSF/…, a network file system, and fast interconnect, but each of those sub-systems have slightly different policies on a machine-by-machine basis, which is where things get tricky.

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Dask and ITK for large scale image analysis

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2019 Dask User Survey

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Dask Release 2.2.0

I’m pleased to announce the release of Dask version 2.2. This is a significant release with bug fixes and new features. The last blogged release was 2.0 on 2019-06-22. This blogpost outlines notable changes since the last post.

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Extracting fsspec from Dask

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Dask Release 2.0

Please take the Dask User Survey for 2019. Your reponse helps to prioritize future work.

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Load Large Image Data with Dask Array

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Python and GPUs: A Status Update

This blogpost was delivered in talk form at the recent PASC 2019 conference. Slides for that talk are here.

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Dask on HPC

We analyze large datasets on HPC systems with Dask, a parallel computing library that integrates well with the existing Python software ecosystem, and works comfortably with native HPC hardware.

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Experiments in High Performance Networking with UCX and DGX

This post is about experimental and rapidly changing software. Code examples in this post should not be relied upon to work in the future.

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Composing Dask Array with Numba Stencils

In this post we explore four array computing technologies, and how they work together to achieve powerful results.

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cuML and Dask hyperparameter optimization

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Dask and the __array_function__ protocol

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Building GPU Groupby-Aggregations for Dask

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Running Dask and MPI programs together

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Single-Node Multi-GPU Dataframe Joins

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Dask Release 1.1.0

I’m pleased to announce the release of Dask version 1.1.0. This is a major release with bug fixes and new features. The last release was 1.0.0 on 2018-11-29. This blogpost outlines notable changes since the last release.

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Extension Arrays in Dask DataFrame

This work is supported by Anaconda Inc

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Dask, Pandas, and GPUs: first steps

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GPU Dask Arrays, first steps

The following code creates and manipulates 2 TB of randomly generated data.

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