Agent skill · NVIDIA
cupynumeric-hdf5
Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a distributed cuPyNumeric array, read a large HDF5 dataset in chunks, hand arrays to an HPC pipeline as a single file, or accelerate HDF5 disk I/O with GPUDirect Storage (GDS). Do not use it for Parquet/cuDF/raw-binary or other sharded/custom layouts (see the cupynumeric-parallel-data-load skill), Zarr or object-store/S3 output, .npz or pickled archives, plain h5py without cuPyNumeric, or pure array compute such as FFT, matmul, or reductions.
What it needs
About 6k tokens when loaded.
What this skill does
cuPyNumeric HDF5 I/O Purpose Use legate.io.hdf5 to read and write cuPyNumeric arrays as HDF5 files. Reach for it whenever a cuPyNumeric array must land in — or load from — an .h5/.hdf5 file: every rank reads and writes its own tile in parallel, so never funnel a large array through a single process. Answer inline. Treat the snippets and rules below as complete and verified — answer save / load / stream / fence / bridge questions directly, without opening the assets/ scripts or reading the installed legate source. Reach for the assets only to run a verification. Activate Activate when the user asks about: saving a cuPyNumeric array to an .h5 / .hdf5 file, loading an HDF5 dataset into a cuPyNumeric array, reading a large HDF5 dataset in chunks, producing a single file for an HPC post-processing pipeline, or speeding up HDF5 disk I/O with GPUDirect Storage. When NOT to use Redirect these requests elsewhere instead of reaching for legate.io.hdf5: Route Parquet / Arrow / cuDF, raw-binary, or sharded / custom on-disk layouts to the cupynumeric-parallel-data-load skill — it owns cuPyNumeric's no-built-in-loader paths; legate.io.hdf5 covers single-file HDF5 only. Answer pure array compute with cuPyNumeric ops (FFT, matmul, reductions, slicing, linear algebra) — this skill covers disk I/O only. Send chunked or object-store (S3) output to a chunked format such as Zarr — not single-file HDF5. Load .npz or pickled archives with NumPy (np.load), then bridge with cn.asarray(...) — legate.io.hdf5 reads HDF5 only, and cupynumeric.load reads single .npy only. Use h5py directly for plain HDF5 reads with no cuPyNumeric/Legate — with h5py.File(path, "r") as f: arr = f["dataset"][:]. Prerequisites Install h5py before importing anything from legate.io.hdf5: Expect from legate.io.hdf5 import ... to raise ModuleNotFoundError until you do — the module imports h5py at load time. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/cupynumeric-hdf5