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name: training-evaluation-scripts description: "Construct safe GluonCV training, evaluation, demo, and benchmark flag templates from the script zoo without executing source checkout scripts." disable-model-invocation: true metadata: disco-role: operating parent-skill: gluon-cv package: gluoncv license: Apache 2.0

GluonCV training and evaluation script workflows

Use this sub-skill when a task asks for GluonCV training, evaluation, demo, feature extraction, benchmarking, DDP/Horovod/DALI usage, or command-line flags for the script zoo: CIFAR/ImageNet classification, SSD/YOLO/Faster R-CNN/CenterNet/Mask R-CNN detection, segmentation, pose, action recognition, Monodepth2, SiamRPN/SMOT tracking, GAN, Re-ID, dataset preparation, or AutoGluon example commands.

This sub-skill turns the repository's script families into self-contained, non-executable flag templates and checklists. Treat original programs as evidence for flag shape, not as runtime files this skill depends on.

Route first

Stay here for:

• Choosing a script family for training/evaluation/demo/benchmarking.
• Building a flag template and identifying required dataset/model/backend inputs.
• Explaining common flags such as --model, --network, --dataset, --dataset-root, --data-dir, --gpus, --num-gpus, --batch-size, --num-workers, --epochs, --resume, --pretrained, --deploy, --quantized, and --config-file.
• Deciding whether a workflow is safe to dry-run, help-only, reference-only, or requires user approval because it downloads, trains, benchmarks, or needs GPUs.

Route elsewhere when the user needs deeper API or data details:

• MXNet model names, get_model, reset_class, and inference API details: ../mxnet-model-zoo/
• Dataset layouts, transforms, loaders, and annotation validation: ../data-transforms-datasets/
• PyTorch action-recognition models, configs, tensor shapes, and DDP model smokes: ../torch-video-workflows/
• AutoGluon tasks, export, ONNX, TVM, quantized deployment: ../automl-deployment-export/

Safe workflow

1. Identify the task family and backend: MXNet script-style workflow, Torch action-recognition config workflow, AutoGluon example, deployment/export, or dataset preparation.

2. Read references/script-catalog.md to classify source families as adapted, reference-only, or excluded.

3. Use scripts/build_training_command.py to generate a no-side-effect flag template:

   python scripts/build_training_command.py detection-yolo \
     --dataset voc --dataset-root /data/VOCdevkit --gpus "" --batch-size 2
   

4. Replace placeholders with user-provided values and verify prerequisites before any real execution in a user-provided checkout or project: dataset root, annotations, image/video files, pretrained cache/network policy, backend framework, optional dependencies, and GPU availability.

5. For CPU-safe exploration, prefer dry model/data smoke checks from sibling sub-skills before launching long jobs.

6. For real training, explicitly confirm expected runtime, write locations, and resource usage. Long workflows can create checkpoints/logs, download weights/data, and consume GPUs for hours.

References and helper

• references/training-and-evaluation-scripts.md — task-family flag anatomy and adaptation patterns.
• references/script-catalog.md — source script family inventory, selected bundled replacement, and skip/risk decisions.
• references/troubleshooting.md — dataset, backend, optional dependency, OOM, resume, distributed, and cache failures.
• scripts/build_training_command.py — safe flag-template builder; it prints pseudo-command families and warnings but never starts training.

Safety defaults

• Use empty --gpus "" or --num-gpus 0 for CPU templates when the underlying workflow supports it.
• Do not run benchmark, DDP, Horovod, DALI, video decoding, dataset download, export, or training commands until the user confirms hardware/data/network/write side effects.
• Treat pretrained downloads as network/cache side effects. Prefer pretrained=False in API smoke checks and pass explicit cache/write locations for real runs when possible.
• When a source family defaults to GPU, make the GPU requirement visible and change the template only when CPU behavior is supported by that workflow.