cupynumeric-parallel-data-load SKILL.md skillA
cupynumeric-parallel-data-load from nvidia/skills is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add nvidia/skills/cupynumeric-parallel-data-load@v1.0.0Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- nvidia/skills
- Path
skills/cupynumeric-parallel-data-load/SKILL.md- Versions recorded
- 2
- Latest version
- v1.0.0, committed Jul 31, 2026
- First version recorded
- May 30, 2026
- Size
- 18,178 bytes
- Repository stars
- 3,871
- License
- Apache-2.0
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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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). Canonical pattern: **manual partition + manual task launch, sized to the machine, not the files.** Only axis 0 is sharded; trailing axes ride along inside each tile. Per-shard row counts may differ across …
Read the whole file at its exact version.
How to install
mdr add nvidia/skills/cupynumeric-parallel-data-load@sha256:2ae4b284c285b5beThe line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_lrfmmfh5h5ofsvm7)
3 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 2 versions of this file, between May 30, 2026 and Jul 31, 2026, about one every 63 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v1.0.0 latest | 2026-07-31 | ea1d5b5 | 18,178 B | A | view · diff |
| v1.0.0 | 2026-05-30 | 942ea1e | 18,194 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (18178 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
nvidia/skills · 3,871 stars · license Apache-2.0 · pushed 2026-11-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_lrfmmfh5h5ofsvm7 GET https://markdownregistry.com/api/v1/resolve?ref=nvidia/skills/cupynumeric-parallel-data-load GET https://markdownregistry.com/api/v1/blob/2ae4b284c285b5be96f9d9fe0cd3a6e51d86f3d12da3e9867ecde75265b99708
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.