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A flexible library for parallel computing in Python. Dask is composed of two parts: - Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. - “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 dynamic task schedulers. This meta package pulls in the distributed module into the dask namespace.
| Package | Summary | Distribution | Download |
| python312-dask-distributed-2024.11.1-1.1.noarch.html | Interface with the distributed task scheduler in dask | OpenSuSE Tumbleweed for noarch | python312-dask-distributed-2024.11.1-1.1.noarch.rpm |
| Interface with the distributed task scheduler in dask | python312-dask-distributed-2024.11.1-1.1.noarch.rpm | ||
| python312-dask-distributed-2024.8.2-1.1.noarch.html | Interface with the distributed task scheduler in dask | OpenSuSE Ports Tumbleweed for noarch | python312-dask-distributed-2024.8.2-1.1.noarch.rpm |
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