---

name: core-api description: "Routes LibMTL's Trainer, shared configuration, and built-in API surface." disable-model-invocation: true metadata: disco-role: operating license: MIT

core-api

Use this sub-skill when the task is about the shared LibMTL API rather than a specific benchmark dataset.

Covers

• LibMTL.Trainer construction and runtime behavior.
• LibMTL.config.LibMTL_args and prepare_args.
• Built-in losses, metrics, architectures, weightings, and backbone builders.
• The shared utility helpers in LibMTL.utils.
• Direct inspection of the API surface before writing a custom benchmark or a new method.

Does not cover

• Dataset-specific file layouts or benchmark commands.
• New-task adaptation recipes; use customization for that.
• NYUv2/Cityscapes, Office-31/Office-Home, QM9, or PAWS-X details.

When to use this sub-skill

Choose this route when the user asks things like:

• "How do I instantiate Trainer correctly?"
• "Which weighting and architecture names are supported?"
• "What does prepare_args return?"
• "How do the built-in backbones and utilities fit together?"
• "Why did a direct Trainer call fail even though the docs looked correct?"

Read next

• ../../references/api-reference.md for the verified signature and exported classes.
• ../../references/configuration.md for shared CLI and method flags.
• ../../references/troubleshooting.md for configuration and runtime failures.

Workflow

1. Confirm whether the user is passing string names or class objects.
2. Confirm whether prepare_args is being used. If not, check that both weight_args and arch_args are supplied.
3. Confirm the runtime has CUDA. The trainer's default device is cuda:0.
4. Check the requested weighting and architecture names against the exported module lists.
5. If the request is to validate the install rather than solve a dataset task, run scripts/check_core_api.py.

Common gotchas

• Trainer resolves weighting and architecture names internally. Pass strings such as "EW" and "HPS".
• The supported optimizer/scheduler combinations in the current code are narrower than the parser help suggests; adam/sgd and step are the safe choices.
• resnet18 and resnet_dilated return feature maps. They are not end-to-end classifiers.
• MOML, FORUM, and AutoLambda are treated as bilevel methods and use the trainer's special path.

Exit criteria

Leave this sub-skill when you can answer, from the bundled files alone:

• which object to import,
• which string names to pass,
• which kwargs must be present,
• and what failures mean the configuration is wrong versus the environment is incomplete.