cuPyNumeric Migration Readiness

Purpose

Use this skill BEFORE the migration, not during. Answer one question: which of the user's existing NumPy APIs will scale on cuPyNumeric, and which need refactoring, before they commit engineer-weeks to porting? To answer it: read the source, classify each NumPy idiom by its expected multi-GPU scaling on the Legate/NVIDIA GPU stack, cross-reference the bundled API-support manifest, and produce a structured verdict with per-finding reasoning and recipe pointers.

This is a static, read-only assessment. Inspect the user's source with Read, Grep, and Glob. Do not execute the user's code, modify or write files, or print environment variables or secrets. The legate, and cuPyNumeric Doctor commands shown below are suggestions for the user to run — not actions this skill performs.

If this skill has never been seen before, head to references/getting-started.md first.

When to use this skill

Use when the user is about to migrate NumPy code to GPU and asks whether it will scale on cuPyNumeric / GPU, whether they should migrate, which parts will benefit, what must change before porting, or whether the port is worth it — or mentions pre-port assessment, scaling analysis, idiom analysis, GPU refactor planning, or identifying NumPy anti-patterns for GPU.

Decline and redirect when the request is not a pre-migration assessment:

• Post-migration performance / profiling ("already ported, why is it slow?") → point to legate --profile and the upstream profiling and debugging walkthrough.
• Custom CUDA / kernel authoring ("write/optimize a CUDA kernel")