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name: training-and-configuration description: "Choose, validate, and build safe StudioGAN training configurations, datasets, HDF5 flows, checkpoint resumes, DDP, mixed precision, and logging commands." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

StudioGAN training and configuration router

Use this sub-skill when the task is to choose or edit a StudioGAN YAML config, validate a training dataset layout, plan HDF5 or in-memory data loading, resume from checkpoints, freeze discriminator blocks, or build a safe python src/main.py training command for a separate StudioGAN checkout.

Do not use this sub-skill for standalone image-folder metrics or checkpoint visualization/latent analysis. Route those to sibling skills:

• Standalone IS/FID/PRDC/iFID/CAS folder metrics: evaluation metrics.
• Checkpoint image saving, KNN, interpolation, frequency, TSNE, SeFa, CAS commands after training: sampling and analysis.

Operating assumptions

• StudioGAN is script-first: run commands from a StudioGAN checkout with python src/main.py; do not expect package metadata or an installed console entry point.
• Training is a CUDA-oriented PyTorch workflow. CPU-only environments can validate configs and build commands, but practical training/evaluation requires CUDA-capable PyTorch and compatible dependent packages.
• W&B logging is imported by the training path. Decide before running whether to login with an API key, set offline mode, or redirect logs according to the execution environment policy.
• Use generic placeholders such as /path/to/PyTorch-StudioGAN, /path/to/data, /path/to/save, and /path/to/checkpoint in plans and commands.

Start here

1. Identify the training scenario:
   • New training from an existing YAML config.
   • Custom ImageFolder adaptation.
   • Resume from -ckpt, optionally -best.
   • Transfer/freezeD training with --freezeD and a source checkpoint.
   • Single GPU, DataParallel, DistributedDataParallel, mixed precision, sync-BN, HDF5, or in-memory loading.
2. Read training workflows for command patterns and flag combinations.
3. Read configuration reference before editing YAML; then run the config validator.
4. Read data and HDF5 before changing DATA.name, DATA.num_classes, image size, HDF5, or custom folders.
5. For StyleGAN2/3 or ADA/APA configs, read StyleGAN ADA guide.
6. If a command fails, map the message through troubleshooting before retrying.

Bundled safe helpers

All helpers are dry-run/validation tools. They do not train, download datasets intentionally, log into services, or modify checkpoints.

python sub-skills/training-and-configuration/scripts/validate_studiogan_config.py \
  --repo-root /path/to/PyTorch-StudioGAN \
  --cfg /path/to/PyTorch-StudioGAN/src/configs/CIFAR10/ContraGAN.yaml \
  --data-dir /path/to/data --save-dir /path/to/save --train --gpus 1

python sub-skills/training-and-configuration/scripts/check_studiogan_dataset.py \
  --data-dir /path/to/imagefolder --require-valid --min-classes 2 --min-images-per-class 1

python sub-skills/training-and-configuration/scripts/build_studiogan_train_command.py \
  --repo-root /path/to/PyTorch-StudioGAN \
  --cfg /path/to/PyTorch-StudioGAN/src/configs/CIFAR10/ContraGAN.yaml \
  --data-dir /path/to/data --save-dir /path/to/save \
  --gpus 0 --metrics is fid prdc --mixed-precision --hdf5 --load-in-memory

Required checks before giving a run command

• src/main.py exists under the requested --repo-root.
• The YAML file is accepted by config.Configurations and check_compatability with parser-equivalent RUN defaults.
• DATA.num_classes, DATA.img_size, and MODEL.backbone match the dataset and intended command mode.
• Custom datasets have train/<class>/... and, for validation/reference evaluation, valid/<class>/....
• OPTIMIZATION.batch_size is divisible by GPU world size; for analysis/CAS paths, keep it divisible by 8.
• -l is used only with -hdf5; iFID with HDF5 also requires in-memory loading.
• DDP is not combined with visualization, KNN, interpolation, frequency, TSNE, SeFa, Langevin/DDLS, or CAS flags.
• --freezeD includes -ckpt and uses a compatible source checkpoint.