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training-and-distribution

training-and-distribution

Guides MMGeneration training, resume, distributed launch, and launcher-specific workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: "training-and-distribution" description: "Guides MMGeneration training, resume, distributed launch, and launcher-specific workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Distribution

Use this sub-skill when the user wants to train a model, resume from a checkpoint, or understand the repo's distributed training and validation flow.

Typical triggers

  • "How do I train this config?"
  • "How do I resume a run?"
  • "How do I use DDP or Slurm?"
  • "Can I train on CPU for debugging?"
  • "Why does the training hook not behave as expected?"

Include here

  • tools/train.py
  • tools/dist_train.sh
  • tools/slurm_train.sh
  • mmgen.apis.train_model and set_random_seed
  • DynamicIterBasedRunner
  • DDP wrapper and dynamic DDP notes
  • EMA, visualization, checkpoint, and validation hook behavior
  • CPU training guidance for debug-only use

Exclude here

  • Sampling and demo workflows -> inference-and-sampling
  • Metrics and validation scripts -> evaluation-and-metrics
  • Config syntax and registry extension -> configuration-and-extension
  • Latent editing and deployment helpers -> applications-and-deployment

Read these files first

  • references/workflows.md
  • references/troubleshooting.md
  • ../../references/api-reference.md
  • ../../references/cli-reference.md
  • ../../references/model-overview.md

What good guidance looks like

A future agent should be able to:

  1. Pick the right launcher: none, PyTorch DDP, or Slurm.
  2. Explain how work_dir, resume_from, and load_from interact.
  3. Distinguish static and dynamic GAN behavior in distributed mode.
  4. Tell the user when CPU training is acceptable only as a debugging aid.
  5. Route validation and evaluation questions to the evaluation sub-skill instead of mixing them into training advice.

Common failure modes

  • DDP settings that work for static GANs but fail for dynamic architectures.
  • apex_amp being used outside the distributed-only path.
  • Missing or incorrect work_dir, cfg_options, or checkpoint resume path.
  • A custom hook or runtime block not being registered before the run starts.

For concrete recovery steps, read references/troubleshooting.md.

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