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Datasets

Versioned data for evaluations, training, and optimization — authored as a directory, published as an artifact, pinned by reference.

A Dreadnode dataset is a directory with a dataset.yaml manifest that the platform packages, versions, and serves back by reference. Author locally, publish a version, then pin that version from an evaluation, training job, or optimization study.

support-prompts/
  dataset.yaml
  splits/
    train.parquet
    validation.parquet
    test.parquet
Terminal window
dn dataset push ./support-prompts    # → acme/[email protected]

Every consumer — training job configs, the SDK pull/load path, and the CLI — resolves the same org/name@version reference.

The lifecycle

Section titled “The lifecycle”
  1. Author the directory locally: a dataset.yaml, one or more data files, splits if needed.
  2. Inspect before publishing — dn dataset inspect ./path catches schema and format problems before anything leaves your machine.
  3. Push to the registry with dn dataset push or dn.push_dataset(...).
  4. Share or pin: keep the version private to your organization, or dn dataset publish it to the public catalog.
  5. Consume from evaluations, training, optimization, or ad-hoc SDK code by pinning org/name@version.

Every step is covered on one of the pages below.

Formats and splits

Section titled “Formats and splits”

Datasets hold tabular data. Supported artifact formats are parquet, csv, arrow, feather, json, and jsonl — all within one dataset must share one format. Parquet is the default and the cheapest to ship.

Splits are optional. When dataset.yaml declares splits: {train: ..., test: ...}, consumers can ask for one (load_dataset(..., split="train"), dn dataset pull --split train). Without splits, the dataset is a flat set of rows across one or more files.

When a dataset belongs in the registry

Section titled “When a dataset belongs in the registry”

Publish a dataset when the rows need to live somewhere reproducible — benchmarks you rerun, training corpora, adversarial goal sets, regression suites. Every rerun of a pinned version loads the same bytes.

Keep rows inline when they are one-shot evaluation inputs scoped to a single config file. Evaluation manifests accept a dataset: block with per-row parameters for exactly this case — see Evaluations → Inputs. Same noun, different mechanic; the registry page is about the durable-artifact side.

Section titled “Related surfaces”
Quickstart
Authoring
Publishing
Catalog
Using in code
Manifest reference

Full CLI: dn dataset. The Hub shows the same registry visually — org and public datasets, version history, facet filters, download activity.