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name: training-engine description: "Assemble TorchDrug models and tasks into core.Engine training, evaluation, prediction, checkpoint, config serialization, logging, CPU/GPU/distributed settings, and safe smoke tests." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training Engine

Use this sub-skill when a user needs to turn an already chosen TorchDrug dataset, model, and task into an executable training or inference loop with torchdrug.core.Engine.

When To Use

• Build a core.Engine(task, train_set, valid_set, test_set, optimizer, ...) training harness.
• Explain train(), evaluate(), direct task.predict(), checkpoint save/load, and JSON config round-trips.
• Choose CPU, single-GPU, distributed GPU, batch_size, num_worker, gradient_interval, logger, optimizer, or scheduler settings.
• Debug Engine, Task, Configurable, registry, optimizer, checkpoint, device, or logger failures.
• Add a no-dataset smoke check for core.Configurable serialization.

Route Elsewhere

• Molecular property, pretraining, generation, retrosynthesis, and molecule dataset recipes belong in the molecular-workflows sub-skill.
• Knowledge graph completion datasets, negative sampling, filtered ranking, and KGE models belong in the knowledge-graphs sub-skill.
• Protein contact, structure, and sequence workflows belong in the protein-workflows sub-skill.
• Custom layer/model internals, representation model outputs, and extension patterns belong in the layers-and-extensions sub-skill.
• Graph, molecule, packed batch, or data container manipulation belongs in the data-focused sub-skills.

References

• Engine workflows covers Engine construction, training/evaluation/prediction, CPU/GPU/distributed choices, logging, scheduler, checkpoint, and config patterns.
• Task contract covers the Task lifecycle, predict, target, forward, evaluate, preprocess, and custom integration points.
• Troubleshooting covers split shapes, missing optimizer params, device and gpus errors, W&B setup, registry/config failures, and checkpoint mismatch recovery.

Bundled Smoke Test

Run scripts/smoke_config_roundtrip.py from any environment that can import TorchDrug to verify that registered configurable models round-trip without datasets, network access, or training.