---

name: training description: "Construct safe MUNIT and UNIT training commands and diagnose outputs, checkpoints, resume, and config tuning." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

MUNIT training sub-skill

Use this sub-skill when the user needs to plan or run a MUNIT repository training job, choose between the MUNIT and UNIT trainers, understand output/checkpoint layout, resume a stopped run, or tune training/config/loss surfaces before launching a long GPU job.

This sub-skill is self-contained operating knowledge distilled from the repository training entrypoint, trainer classes, utility helpers, configs, tutorial/manual training sections, and demo shell workflows. It intentionally does not depend on reopening source files.

Boundaries

Use this sub-skill for:

• Building a safe dry-run training command.
• Explaining the train.py CLI and the infinite-until-max_iter training loop.
• Selecting MUNIT versus UNIT and checking trainer-specific config keys.
• Understanding where logs, generated image grids, HTML, config copies, and checkpoints are written.
• Resuming from gen_*.pt, dis_*.pt, and optimizer.pt checkpoint directories.
• Troubleshooting CUDA-only paths, resume folders, VGG side effects, tensorboardX import failures, old PyTorch/PyYAML warnings, and display sampling errors.

Reroute instead:

• Dataset schema, folder/list validation, and data-loader repair: ../data-and-configuration/.
• Dependency installation, legacy Docker/conda setup, CUDA compatibility, and import smoke checks: ../environment-and-setup/.
• Generator/discriminator architecture edits or porting internals: ../model-internals/.
• Post-training checkpointed inference, batch translation, or metrics: ../inference-and-evaluation/.

Critical operating facts

• Training is CUDA-oriented. The training path calls .cuda() on the trainer, sampled display tensors, each input batch, MUNIT style tensors, VAE noise tensors, and VGG preprocessing tensors. There is no CLI flag for CPU-only training.
• The only exposed training CLI flags are --config, --output_path, --resume, and --trainer (MUNIT|UNIT).
• The default trainer is MUNIT. UNIT is selectable by CLI but needs UNIT-specific KL loss weights (recon_kl_w, recon_kl_cyc_w) that are not present in the bundled MUNIT demo configs.
• The model name is the config filename stem. Training writes under <output_path>/logs/<model_name> and <output_path>/outputs/<model_name>.
• Full training is long-running and should be launched only after environment and data/config checks pass, GPU availability is intentional, and the user has authorized a long job.

Safe command construction

The bundled helper performs static checks and prints a command without executing training:

python scripts/munit_train_command.py --help
python scripts/munit_train_command.py \
  --repo-root /path/to/user/munit-checkout \
  --config configs/demo_edges2handbags_folder.yaml \
  --output-path runs/demo_edges2handbags \
  --trainer MUNIT

Only after the user explicitly authorizes a real run, use the printed command from the repository root in a suitable legacy CUDA runtime. Do not treat the helper as a launcher; it never downloads data, starts CUDA, or imports the repository package.

Reference map

• references/workflows.md - staged training and resume workflows, including the distilled training loop and demo-script adaptation notes.
• references/cli-reference.md - training CLI arguments, trainer selection, trainer update methods, and config/loss tuning surfaces.
• references/checkpoints-and-outputs.md - output tree, image/log/checkpoint filenames, resume behavior, and monitoring expectations.
• references/troubleshooting.md - failure-mode lookup for CUDA, config, display sampling, resume, VGG, logging, tensorboardX, and legacy warnings.
• scripts/munit_train_command.py - self-contained dry-run command builder and static validator.