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A minimal task scheduling abstraction and parallel arrays. * dask is a specification to describe task dependency graphs. * dask.array is a drop-in NumPy replacement (for a subset of NumPy) that encodes blocked algorithms in dask dependency graphs. * dask.async is a shared-memory asynchronous scheduler that efficiently executes dask dependency graphs on multiple cores. This package contains the dask distributed interface. Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters.
| Package | Summary | Distribution | Download |
| python2-dask-distributed-1.1.1-lp152.2.4.noarch.html | Interface with the distributed task scheduler in dask | OpenSuSE Leap 15.2 for noarch | python2-dask-distributed-1.1.1-lp152.2.4.noarch.rpm |
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