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name: training description: "Routes CIFAR-100 training command construction, warmup, TensorBoard, checkpoint resume, and LR-finder constraints." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

Training

Use this sub-skill when you need to run, resume, or adapt train.py for CIFAR-100.

Start here

1. Read references/training-workflows.md for the end-to-end training flow, checkpoint lifecycle, resume behavior, and TensorBoard side effects.
2. Read references/data-and-config.md for the CIFAR-100 layout, normalization constants, and legacy dataset.py caveats.
3. Read references/troubleshooting.md when a command fails, resume cannot find weights, or CUDA/logging/data setup is unclear.
4. Use scripts/build_train_command.py to validate a proposed train.py command before launching a long run.
5. Read references/lr-finder.md only when you intentionally want the optional learning-rate scan.

Core command

python train.py -net <name> [-gpu] [-b B] [-warm WARM] [-lr LR] [-resume]

Defaults:

• -b 128
• -warm 1
• -lr 0.1
• -gpu off
• -resume off

Scope

This sub-skill covers:

• CIFAR-100 training command construction
• warmup, SGD, MultiStepLR, TensorBoard, and checkpoint side effects
• resume semantics and safe command planning
• optional LR-finder constraints as a reference-only add-on

This sub-skill does not cover:

• architecture internals beyond choosing -net
• final checkpoint metric interpretation
• standalone evaluation or test.py usage