python-dask-doc – Minimal task scheduling abstraction documentation¶
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Dask is a flexible parallel computing library for analytics, containing two components.
Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. 2. “Big Data” collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.
This contains the documentation
Distribution |
Base version |
Our version |
Architectures |
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Debian GNU/Linux 10.0 (buster) |
1.0.0+dfsg-2 |
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Debian GNU/Linux 11.0 (bullseye) |
2021.01.0+dfsg-1 |
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Debian GNU/Linux 12.0 (bookworm) |
2022.12.1+dfsg-2 |
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Debian testing (trixie) |
2024.5.2+dfsg-1 |
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Debian unstable (sid) |
2024.5.2+dfsg-1 |
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Ubuntu 18.04 “Bionic Beaver” (bionic) |
0.16.0-1 |
0.17.5-2~nd18.04+1 |
i386, amd64, sparc, armel |
Ubuntu 20.04 “Focal Fossa” (focal) |
2.8.1+dfsg-0.4 |
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Ubuntu 22.04 “Jammy Jellyfish” (jammy) |
2022.01.0+dfsg-1ubuntu1 |
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Ubuntu 24.04 “Noble Numbat” (noble) |
2023.12.1+dfsg-2 |