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name: training-and-evaluation description: "Inspect MMPreTrain configs, plan train/test commands, and choose safe single-machine, distributed, Slurm, resume, AMP, auto-scale-lr, TTA, result-dump, CPU fallback, and K-fold options." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Evaluation

Use this route when the user needs to prepare or modify a training or evaluation config, choose a launch pattern, or turn a model checkpoint into a reproducible train/test command.

Covers

• Inspecting the merged config before launch.
• Understanding _base_ inheritance, _delete_, and --cfg-options.
• Planning package-level mim train mmpretrain and mim test mmpretrain runs.
• Choosing between local, distributed, and Slurm launch patterns.
• Deciding when to use --resume, --amp, --auto-scale-lr, --tta, --out, and --out-item.
• Forcing CPU-only execution with CUDA_VISIBLE_DEVICES=-1.
• Planning K-fold cross-validation commands.

Start here

• Read references/configuration.md when the question is about config inheritance, field placement, or --cfg-options syntax.
• Read references/training-testing-cli.md when the question is about train/test command shape, launcher choices, distributed environment variables, result outputs, or K-fold.
• Run scripts/print_config.py to expand a config and apply safe overrides before launching.
• Run scripts/build_train_test_command.py to print a reviewed command for train/test/dist/Slurm/K-fold without executing anything.
• Read references/troubleshooting.md for config merge, resume, AMP/TTA, distributed port, NCCL, or CPU fallback failures.

Route elsewhere

• Dataset annotation, label files, registry-based dataset design, and data-format authoring belong in ../datasets-and-customization/SKILL.md.
• Model zoo discovery, get_model, inference_model, inferencers, and feature extraction belong in ../model-zoo-inference/SKILL.md.
• Log/result analysis, confusion matrices, FLOPs, CAM, t-SNE, visualization, checkpoint publishing, and serving belong in ../tools-analysis-and-deployment/SKILL.md.

Common triggers

• "How do I change the learning rate schedule?"
• "How do I inspect the final config after inheritance?"
• "What do I pass for resume versus load_from?"
• "How do I run on CPU only?"
• "How do I launch 8 GPUs or Slurm?"
• "How do I evaluate with TTA and dump predictions?"
• "How do I use K-fold cross validation?"

Command-planning flow

1. Inspect the config with scripts/print_config.py.
2. Decide whether the run is direct, distributed, or Slurm.
3. Use scripts/build_train_test_command.py to print the command.
4. Apply the generated command in the shell only after the config and launcher choices are settled.