cupynumeric-parallel-data-load
Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.
npx skills add NVIDIA/skills --skill cupynumeric-parallel-data-load --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Parallel sharded data -> cupynumeric load **Why this skill exists.** cupynumeric mirrors NumPy's array API, including `cupynumeric.load` for a single `.npy` file. Beyond that, file *loading* lives in Legate, not cupynumeric: | Format | Built-in loader | |---|---| | Single `.npy` | `cupynumeric.load(path)` (NumPy-API parity) | | HDF5 (single file) | `legate.io.hdf5.from_file` / `from_file_batched
What does the cupynumeric-parallel-data-load skill do?
Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.
How do I install it?
Run `npx skills add NVIDIA/skills --skill cupynumeric-parallel-data-load --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.