---

name: cli-config description: "Operate AutoTrain Advanced top-level CLI, setup command, config parser, and task routing." disable-model-invocation: true metadata: disco-role: operating parent-skill: autotrain-advanced license: Apache 2.0

AutoTrain CLI and config routing

Use this sub-skill for command discovery, package-level checks, autotrain --config, autotrain setup, and deciding which task-specific sub-skill should own a request.

Safe entry points

• autotrain --help
• autotrain --version
• autotrain <subcommand> --help
• autotrain --config path/to/config.yml
• autotrain setup --help

Bundled helpers from the root skill:

• ../../scripts/check_install.py — import/version check.
• ../../scripts/inspect_cli.py — run CLI help through the current interpreter.
• ../../scripts/validate_config.py — parse a YAML config without launching training.

Routing rules

1. Start with the top-level help or config parser if the user is not sure which task family they need.
2. If the request is autotrain --config, validate the file first, then route by parsed task and backend.
3. If the task is LLM-like (llm, YAML aliases such as llm-sft/llm-dpo, or app/API keys such as llm:sft/llm:dpo), go to ../llm-training/.
4. If the task is text, token, seq2seq, extractive QA, sentence-transformers, or tabular, go to ../text-and-tabular/.
5. If the task is image classification/regression, object detection, or VLM, go to ../vision-multimodal/.
6. If the user asks about app, api, spacerunner, jobs, logs, or hosted backends, go to ../app-backends/.
7. If the user asks about autotrain tools, go to ../model-tools/.

Important exception: VLM is supported in the task registry/app/config path but is not a registered top-level autotrain vlm command in this checkout.

Config workflow

For a YAML config:

python skills/disco/autotrain-advanced/scripts/validate_config.py path/to/config.yml

Then check:

• task: the owning trainer family.
• backend: local, local-cli, local-ui, spaces-*, ep-*, ngc-*, or nvcf-*.
• params: task-specific fields and column mappings.

Do not call AutoTrainConfigParser(...).run() until the user explicitly wants to launch a job.

Setup command caution

autotrain setup is not a passive inspection command. It mutates the environment by changing xformers; with --update-torch it installs CUDA 12.1 PyTorch wheels. Use it only when the user explicitly wants this environment change.

References

• references/workflows.md — CLI inventory and config decision flow.
• references/troubleshooting.md — invalid subcommands, parser errors, and setup/environment problems.