---

name: training-configs description: "Train, evaluate, resume, and inspect PaddleGAN models from YAML configs, checkpoints, AMP, distributed launch, VisualDL, and registry-backed builders." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training-configs

Use this sub-skill for PaddleGAN training and evaluation command planning.

Route here when you need to

• start training from a YAML config
• resume from a checkpoint or load weights for evaluation or fine-tuning
• override config values with dotted key=value paths
• inspect output/checkpoint layout, AMP, distributed launch, or VisualDL
• resolve model, dataset, metric, optimizer, or scheduler names from config blocks

Do not use this sub-skill for

• dataset download or preprocessing details
• export / deployment / static inference
• image/video application inference recipes
• full native training or evaluation runs

Bundled entry point

• scripts/train_eval.py

Reference map

• references/training-workflows.md — command shapes, lifecycle decisions, output layout
• references/config-reference.md — config schema, override syntax, registry names
• references/troubleshooting.md — common parse, dataset, resume/load, AMP, VisualDL, and distributed failures

Operating rules

• Prefer --resume for continuing an exact training run; use --load for weights only.
• --evaluate-only still builds the trainer, so train-side config blocks must remain valid.
• Use --show-config for safe parse / override checks before any training.
• Keep the repo's current field spellings, including visiual_interval.
• The generic trainer writes under output_dir/<config-stem>-<timestamp>/, not the model class name.