cupynumeric-parallel-data-load

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 Sharded multi-file (any format), Parquet/Arrow, raw binary, custom layouts No built-in loader — this skill. This skill shows the canonical way to fill the gap in the last row: write a Legate Python task that calls the third-party reader the format needs ( h5py , pyarrow , np.memmap , ...) inside the task body, and let Legate distribute the reads across GPUs / nodes. For the formats with a built-in loader, prefer it unless you need a custom in-task body (mmap-based loader, format-specific decoder, sidecar metadata, partial / sharded reads). Show more Installs 549 Repository nvidia/skills GitHub Stars 1.3K First Seen May 30, 2026 Security Audits Gen Agent Trust Hub Pass Socket Pass Snyk Pass