---

name: training-and-models description: "Use Towhee's optional PyTorch Trainer, TrainingConfig YAML, NNOperator training bridge, and towhee.models package boundaries safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Towhee training and models

Use this sub-skill when the task involves Towhee's optional PyTorch training layer: TrainingConfig, Trainer, NNOperator.train(...), NNOperator.setup_trainer(...), checkpoint/model-card behavior, or deciding whether to install towhee.models instead of using Hub operators.

Fast routing

• For TrainingConfig fields, YAML save/load shape, device choices, loss/optimizer/scheduler names, Trainer, and NNOperator training, use Training configuration and Trainer.
• For the towhee.models package split, create_model(...) pattern, pretrained-download risk, and Hub-versus-model-zoo decisions, use Model-zoo boundaries.
• For optional PyTorch/TorchVision/TorchMetrics installs, Towhee auto-install side effects, missing trainable model attributes, CUDA/CPU device mistakes, and model-download failures, use Troubleshooting.
• To produce a CPU-safe config stub without importing Towhee or torch, run training_config_template.py. Add --check-imports only in an isolated environment where importing the optional trainer stack is acceptable.

Boundaries and handoff

• Stay here for high-level training configuration, Trainer setup, NNOperator training hooks, checkpoint/model-card expectations, and model-zoo installation boundaries.
• Route custom pipeline operators, ops, register, Hub revisions, and towhee init to operator-hub-and-cli.
• Route DataCollection, DataLoader, image/audio/video wrappers, and non-training data serialization to data-utilities.
• Route HTTP/GRPC services, towhee server, Docker, Triton, and deployment packaging to serving-and-triton.
• Do not use this sub-skill as an exhaustive per-model API catalog. Treat large model tests, pretrained downloads, and full fine-tuning runs as explicit user-approved optional work.

Default safe workflow

python scripts/training_config_template.py --format yaml --output training_config.yaml

Start with device_str: cpu, dataloader_num_workers: 0, tensorboard: null, and pretrained=False model choices when the user only needs a portable template or diagnosis. Move to actual training only after the active environment has the intended PyTorch stack and any model/data downloads are approved.