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NumSharp's NumPy-vs-NumSharp performance harness — the op/dtype/N matrix (C# BenchmarkDotNet vs a warm NumPy process), unified Managed/OpenBLAS backend profiles, and five appended subsystems (nditer, layout, operand, cast, fusion), all in the NPY/NS…
Langue du texte source : anglais
NumSharp's differential-fuzz pipeline — the NumPy 2.4.2 "oracle" that proves every NDIter-backed op is BIT-IDENTICAL to NumPy across the input space. Use this whenever you add or change an np.* op and need fuzz coverage, regenerate the committed corpus, wire…
Langue du texte source : anglais
How to discover, validate, build, and honestly verify a SPECIALIZED path for ANY function — a cheaper route for a recognizable SUBSET of an operation's inputs, sitting behind the general implementation with a gate + fallback so the general case never…
Langue du texte source : anglais
NumSharp's release-notes authoring + large-range commit-sweep playbook - how to write a changelog in the house style AND how to work through a few-hundred-commit range without missing or mis-stating anything. Use this whenever you write or refresh a release…
Langue du texte source : anglais
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Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/SciSharp/NumSharp.Le README n’a pas pu être chargé. Vous pouvez toujours consulter les Skills du dépôt ci-dessous.