Validate a TAO DAFT Dataset

Validate a TAO DAFT Dataset

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

Quick start

tao-daft validate <format> --path <dataset-or-parent-dir>

<format> is a positional subcommand (e.g. metropolis-v3.0, cosmos-reason-v1.0); --path is required. Discover supported formats and per-format flags via tao-daft validate --help and the leaf --help (see "CLI conventions" below).

Preflight

python -c "import nvidia_tao_daft" 2>/​dev/​null || {
  echo "MISSING: tao-daft not installed. Run:"
  echo "  pip install nvidia-tao-daft"
  exit 1
}

Quick Start

Discover the installed validator formats before choosing a format slug, then run validation with the target passed through --path:

tao-daft --version
tao-daft validate --help
tao-daft validate <format> --help
tao-daft validate <format> --path /​path/​to/​daft-dataset

Purpose

Drive tao-daft validate against a DAFT dataset (or a tree of them). The CLI is the spec; the skill picks subcommand + flags and explains the result.

Trigger when the user mentions "TAO DAFT", "DAFT format", validating a DAFT dataset, schema/​cross-reference errors, or tao-daft validate. Do not trigger for non-DAFT layouts (COCO, YOLO, Data Factory JSONL), or for tao-daft info / tao-daft convert — those have their own skills.

If the user's opening is ambiguous, run a few --help commands first to ground yourself, then come back and confirm the task.

Prerequisites

• nvidia-tao-daft installed (pip install nvidia-tao-daft; the wheel is enough, no source repo). Confirm with tao-daft --version.
• A DAFT dataset, or a parent directory of them, on local disk.

Instructions

CLI conventions

tao-daft is nested argparse subcommands. Names and flags drift across versions, so discover the current surface from --help rather than trusting any list in this doc.

1. Format is a positional subcommand, not --format: tao-daft validate <format> [flags]. List current formats via tao-daft validate --help; slugs look like metropolis-v3.0, cosmos-reason-v1.0.
2. Target is --path PATH, not positional. It accepts a single dataset/​scene or a parent directory — the validator walks the tree.
3. Flags are per-format; run the leaf help, e.g. tao-daft validate metropolis-v3.0 --help, before choosing them. Don't assume a flag from one format exists on another.

So the loop is: tao-daft --version → tao-daft validate --help → pick format (infer if unspecified, see below) → tao-daft validate <format> --help → run → interpret.

Format inference

Use directory markers, not filenames:

• meta.json next to media/ and text/ ⇒ cosmos-reason-v1.0.
• A directory (or nested directories) containing contextual/, typically alongside raw/ and task/ ⇒ metropolis-v3.0.
• Neither marker present ⇒ ask the user; do not guess.

Reading errors

The CLI ends every run with a VALIDATION RESULTS block, then ✅ VALIDATION PASSED or ❌ VALIDATION FAILED, and exits non-zero on failure (safe to chain in scripts).

Output can be large on big trees — capture the full output to a file and read it in slices rather than scrolling inline.

Limitations

• Validates DAFT only. Non-DAFT layouts (COCO, YOLO, Data Factory JSONL, etc.) belong in the upstream converter skills.
• Supported formats are whatever tao-daft validate --help reports for the installed version; older slugs may have been retired.
• Covers validate only. Defer to the dedicated skills for tao-daft info and tao-daft convert.
• Don't reimplement validation in Python; the CLI is the spec.

Troubleshooting

• tao-daft: command not found — wheel not installed in the active env. pip install nvidia-tao-daft; verify tao-daft --version.
• error: argument --path is required — path passed positionally. Move it behind --path.
• invalid choice: '<format>' — slug isn't wired up in this version. Re-run tao-daft validate --help and pick from the list.
• Auto-detection (raw type / contextual set) is wrong — override via the format's scope-restriction flag; discover the name from the leaf --help.
• CI wants warnings to fail — add --strict.