tao-convert-dataset-format

Convert a TAO DAFT Dataset

Quick start

tao-daft convert <source-format> <target-format> --path <input> --output <output>

Source and target are positional subcommands; --path and --output are flags.
Discover the supported formats and per-pair flags from 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 CLI surface before choosing format slugs, then run the
leaf conversion command with explicit --path and --output flags:

tao-daft --version
tao-daft convert --help
tao-daft convert <source-format> --help
tao-daft convert <source-format> <target-format> --path /path/to/daft --output /path/to/converted

Purpose

Drives tao-daft convert to transform a DAFT dataset (or a tree of
them) between supported formats. The CLI does the real work; the
skill picks the right source/target pair and flags, then explains the
result.

Trigger on: converting a DAFT dataset, packaging DAFT QA /
summarization / temporal tasks for VLM training, producing a
meta.json-style training set, or the command tao-daft convert. Do
not trigger for non-DAFT → DAFT conversion (COCO, YOLO, Data
Factory JSONL) — redirect to the upstream nvidia-tao-daft repo's
converter skills.

If the user opens ambiguously, run a few --help calls first.

Prerequisites

• nvidia-tao-daft installed (wheel only, not the source repo).
  Confirm with tao-daft --version.
• A DAFT dataset, or a parent directory containing many, on local
  disk.

Instructions

CLI conventions

tao-daft is nested argparse subcommands. The conventions below are
stable across versions even when format names or flags change, so
always discover the current surface from --help rather than
relying on names this doc happens to mention.

1. Source and target are both positional subcommands, not
   --from/--to: tao-daft convert <source> <target> [flags].
   Format slugs are versioned, lowercase, dot-separated
   (metropolis-v3.0, cosmos-reason-v1.0, ...).
2. Path and output are flags — --path PATH (source),
   --output OUTPUT (destination). Both required at the leaf;
   passing positionally fails.
3. --path accepts both granularities — a single scene/dataset
   or a parent directory; the converter walks the tree.
4. Per-pair flags live at the leaf — flag sets differ between
   targets (e.g. media-handling). Always check the leaf --help.

Operating procedure:

1. tao-daft --version — confirm install, pin version in any report.
2. tao-daft convert --help — list supported source formats.
3. tao-daft convert <source> --help — list valid targets for that
   source.
4. Infer source from layout (same directory markers as the
   tao-validate-dataset-format skill's "Format inference"). If you cannot infer
   or the target is unspecified, ask.
5. tao-daft convert <source> <target> --help — pick flags for the
   user's intent (task subset, media copy vs reference, metadata).
6. Execute, then interpret (see below).

Reading output

Per-scene progress prints to stdout; non-zero exit on failure. The
converted dataset is written under --output — spot-check it with
the tao-validate-dataset-format skill before training. For large trees, capture
the full output and partial-read if huge.

Limitations

• DAFT-supported source formats only. For non-DAFT layouts use the
  upstream repo's converter skills.
• Supported pairs are whatever --help reports for the installed
  version — don't pass an unconfirmed pair.
• Source and target are positional; --path / --output are flags.
• convert only — validate and info have their own skills.
• Do not reimplement conversion in Python; the CLI is the spec.

Troubleshooting

• tao-daft: command not found — wheel not installed; pip install nvidia-tao-daft, verify with tao-daft --version.
• error: argument --path/--output is required — passed
  positionally; move behind the flag.
• invalid choice: '<format>' — slug not wired up in this
  version. Re-run the relevant --help.
• Output rejected by tao-daft validate — re-check per-pair
  flags (media handling, task subset) via leaf --help; a misset
  flag often produces a structurally valid but semantically wrong
  target.