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name: customization description: "Routes LibMTL dataset adaptation, custom methods, and extension workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

customization

Use this sub-skill when the task is to adapt LibMTL to a new dataset or extend its method set with custom losses, metrics, architectures, or weighting strategies.

Covers

• Building a new task_dict.
• Defining new losses and metrics from AbsLoss and AbsMetric.
• Defining new architectures from AbsArchitecture.
• Defining new weighting strategies from AbsWeighting.
• Writing a custom encoder/decoder pair and, when needed, a Trainer subclass.
• Choosing between single-input and multi-input task layouts.

Does not cover

• The benchmark-specific file layouts and commands for NYUv2, Office-31, Office-Home, QM9, or PAWS-X.
• Package installation or API signature lookup; use core-api for that.

When to use this sub-skill

Choose this route when the user asks things like:

• "How do I port LibMTL to a new dataset?"
• "How do I write a custom metric or loss?"
• "How do I add a new weighting strategy?"
• "How should I subclass Trainer or AbsArchitecture?"
• "What shape should my dataloaders return?"

Read next

• ../../references/api-reference.md for the base classes and verified signatures.
• ../../references/configuration.md for shared flags and method-specific options.
• references/workflows.md for the adaptation recipe.
• references/troubleshooting.md for common extension errors.

Workflow

1. Decide whether the task is single-input or multi-input.
2. Define the task dictionary first.
3. Confirm the encoder output shape and the decoder head shapes.
4. Only then wire the trainer and check the CUDA runtime.
5. If the request is about a new weighting or architecture, start from the relevant abstract base class and preserve the expected gradient plumbing.

Critical constraints

• MTAN expects a ResNet-based encoder with a resnet_network attribute.
• PLE only supports multi_input=False.
• CGC, PLE, MMoE, and DSelect_k need img_size and expert counts.
• Trainer still expects string method names and weight_args / arch_args dictionaries.

Exit criteria

Leave this sub-skill when the new dataset or method can be described as a repeatable recipe with explicit task format, encoder/decoder contract, configuration knobs, and failure modes.