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name: cli-and-config description: "Use torchtune's tune CLI, recipe registry, and OmegaConf config system safely without importing recipes." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

cli-and-config

Use this sub-skill when an agent needs to discover torchtune recipes/configs, copy or inspect built-in YAML, validate config shape, build tune run commands, or explain torchtune's _component_/OmegaConf override behavior.

Route Here For

• Listing, copying, catting, validating, downloading, or running through the tune CLI.
• Building tune run [TORCHRUN-OPTIONS] <recipe> --config <config> [RECIPE-OPTIONS] commands.
• Explaining config YAML fields, _component_ dotpaths, interpolation, key=value overrides, and ~field removals.
• Inspecting built-in recipes/configs without importing the intentionally non-importable recipes package.
• Diagnosing CLI/config failures before moving on to expensive training.

Quick Workflow

1. Discover registry entries with tune ls or python scripts/inspect_tune_registry.py --format table.
2. Inspect a built-in config with tune cat <config-name> or copy it with tune cp <config-name> ./my_config.yaml --make-parents.
3. Make quick changes with recipe overrides after the --config value, using key=value and ~field only.
4. Run the bundled shape checker before training: python scripts/validate_config_shape.py ./my_config.yaml optimizer=bitsandbytes.optim.PagedAdamW8bit ~optimizer.fused.
5. Use tune validate ./my_config.yaml when optional dependencies are present and component signature validation is desired.
6. Build execution commands with torchrun flags before the recipe and recipe/config overrides after --config.

Read Next

• CLI reference for tune download, tune ls, tune cp, tune cat, tune validate, and tune run syntax.
• Config reference for config.parse, config.instantiate, _component_, interpolation, overrides, and removals.
• Troubleshooting for common CLI/config errors and safe fixes.
• post-training-recipes for choosing recipes and planning expensive runs.
• data-and-datasets for dataset field semantics and dataset-specific component choices.

Bundled Helpers

• scripts/inspect_tune_registry.py prints recipe/config registry metadata without importing or executing recipe modules.
• scripts/validate_config_shape.py loads a local or registry config, applies torchtune-style overrides, checks _component_ shape, and optionally resolves/imports components without launching training.

Guardrails

• Do not import recipes; use tune run, tune cp, tune cat, registry names, or runpy behavior through the CLI.
• Do not place torchrun/distributed flags after the recipe name; they must appear before the recipe to affect torchrun.
• Do not use unknown --flags after --config; torchtune recipe overrides are key=value or ~field tokens.
• Do not embed Hugging Face or Kaggle tokens in configs, commands, examples, or skill content.
• Keep dataset schemas, recipe selection depth, and model/module API catalogs in their sibling sub-skills.