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name: setup-and-command-building description: "Guides OFA installation, environment checks, PYTHONPATH setup, and safe train/evaluate command construction." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

setup-and-command-building

Use this sub-skill when a user wants to install OFA, verify the environment, figure out PYTHONPATH or --user-dir, or turn a long distributed shell script into a copyable command.

Trigger phrases

• "How do I run OFA?"
• "Why does train.py import fail?"
• "What PYTHONPATH do I need?"
• "Render the caption/VQA/RefCOCO/MMSpeech command for me."
• "Which ports, ranks, or GPUs should I use?"

What this sub-skill owns

• repository installation and minimal verification,
• Fairseq fork visibility and import ordering,
• command shape for train.py and evaluate.py,
• distributed launch scaffolding and safe launch parameters,
• command rendering for the common OFA task families.

What it excludes

• dataset schemas and row validation -> data-formats,
• task-specific workflows -> the dedicated workflow sub-skill,
• model/criterion internals -> model-internals-and-extension.

Read these files

• references/command-recipes.md for copyable launch patterns and common flags.
• references/troubleshooting.md for import, CUDA, and launch failures.
• ../../scripts/check_ofa_environment.py to confirm imports and CLI help.
• ../../scripts/render_ofa_command.py to render a command without executing it.

Typical workflow

1. Check the environment.
2. Confirm the repo root and bundled Fairseq fork are visible to Python.
3. Decide which task sub-skill owns the actual workflow.
4. Render the command with explicit data, checkpoint, and selected_cols values.
5. Only launch the heavy job after the relevant input validator passes.

Notes

• Most OFA commands are Fairseq launches plus OFA-specific overrides.
• The command renderer is intentionally conservative; it does not guess dataset or checkpoint paths.
• Use CPU-only help checks only for import and parser validation. Do not treat them as proof of GPU readiness.