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name: training-evaluation description: "Build safe dataset-aware training and evaluation commands for the verified Gluon and PyTorch classification workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training and evaluation

Use this sub-skill when the request concerns ImageNet1K, CIFAR10, CIFAR100, SVHN, or CUB200_2011 data layout, train_gl.py/eval_gl.py, train_pt.py/eval_pt.py, metrics, batch/device sizing, or checkpoint resume behavior. This sub-skill documents classification workflows only; do not turn it into a full training run.

Route first

• Use model-inference to choose and construct a model, select resnet18-style names, or diagnose model/checkpoint shape and device loading.
• Use framework-compatibility for TensorFlow, Keras, Chainer, legacy Tensorpack, or other optional backend claims. CPU Gluon and CPU PyTorch/pytorchcv are the verified core; do not infer CUDA or optional-backend support from this sub-skill.

Safe operating procedure

1. Select the exact dataset name and layout. ImageNet1K means folder layout; ImageNet1K_rec is the Gluon-only MXNet record layout. Read datasets and layouts.

2. Run the no-network preflight before a real command:

   python scripts/check_dataset_layout.py --dataset ImageNet1K --data-dir /data/imagenet --backend pytorch
   

   Replace /data/imagenet with the user's local data root. The checker only inspects filesystem structure; it never downloads data or imports a framework. A nonzero result means stop and fix the root/layout rather than launching a CLI.

3. Construct the command from the CLI reference. Start with --num-gpus=0, --num-data-workers 0, and a conservative --batch-size on CPU. Omit --use-pretrained, --all, and any other network-dependent option for offline work. For CIFAR10, CIFAR100, and SVHN, an indeterminate_native_cache result means only that a local cache exists; the selected framework must still verify its cache with downloads disabled.

4. Run --help first. Help parsing does not construct a model, open a data loader, download weights, or start training. Do not use help output as proof that a dataset or checkpoint is valid.

5. For evaluation, prefer --data-subset val for these classification metainfo objects. The scripts use the held-out split for val; several listed metainfo objects do not define separate test_metric_names.

6. Record the selected dataset, root, framework, checkpoint type, device, effective batch size, metric names, and validation result. Do not claim an accuracy value without running the evaluation against a real local dataset.

Checkpoint and command boundaries

• Gluon training separates model parameters (--resume, normally a .params file) from optimizer state (--resume-state, normally a .states file). PyTorch evaluation loads a model checkpoint through --resume, normally .pth; PyTorch training uses --resume-state for a state object containing epoch, state_dict, and optimizer.
• --start-epoch is an explicit 1-based control in both training CLIs. Passing a state file does not remove the need to choose the intended start epoch.
• A local --resume is distinct from --use-pretrained: the latter can ask a model provider to obtain weights. For no-network operation, use a local checkpoint and omit --use-pretrained.
• Use troubleshooting when the layout, metric, checkpoint, or CPU/device assumptions fail.

Output contract

Return a command plan, not a long-running experiment: dataset/layout decision, preflight result, exact CLI flags, expected metric/checkpoint behavior, and the next bounded check. Link model construction and optional backend questions as specified above.