Tao Convert Dataset Format

--- name: tao-convert-dataset-format description: Run `tao-daft convert` to convert NVIDIA TAO DAFT datasets between supported formats. Do not use for non-DAFT data. Use when the user asks to convert a DAFT dataset, change DAFT format, change a TAO dataset format, or run `tao-daft convert`. license: Apache-2.0 compatibility: Requires Python 3.10+ and the nvidia-tao-sdk package (pip install nvidia-tao-daft). metadata: author: NVIDIA Corporation version: "0.1.0" allowed-tools: Read Bash tags: - tao-daft - dataset - conversion - vlm - cosmos-reason --- # Convert 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 ```bash tao-daft convert --path --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 ```bash 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: ```bash tao-daft --version tao-daft convert --help tao-daft convert --help tao-daft convert --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 [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 --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 --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: ''`** — 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.