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name: cnn-architectures description: "Plan and troubleshoot convolutional DARTS workflows for CIFAR-10 and ImageNet." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

cnn-architectures

Use this sub-skill for convolutional DARTS questions: CIFAR-10 architecture search, CIFAR-10 training/evaluation from a genotype, ImageNet training/evaluation, pretrained CNN checkpoint evaluation, CNN cell/model API behavior, and CNN-specific runtime failures.

Read order

• For commands, flags, outputs, expected metrics, and safe smoke checks, read workflows.
• For source-backed architecture/API facts about search networks, fixed-genotype networks, operations, transforms, and checkpoint formats, read api-reference.
• For failure triage, legacy CUDA/PyTorch issues, data/checkpoint problems, OOM, nondeterminism, and augmentation/drop-path confusion, read troubleshooting.

Routing boundaries

• Genotype catalogs, genotype schema editing, and DOT/Graphviz rendering details route to ../genotypes-and-visualization/.
• Cross-cutting dataset, checkpoint download, and legacy runtime setup details route to the root references once available, especially ../../references/data-and-checkpoints.md, ../../references/legacy-runtime.md, and ../../references/troubleshooting.md.
• Prefer the root command planner ../../scripts/darts_command_builder.py when it is present. It should generate commands and prerequisites; do not copy or recreate long runner scripts in this sub-skill.

Operating constraints

• Treat all CNN runner scripts as reference-only evidence. The DARTS project is script-style, not an installable Python package.
• Native CNN workflows are legacy CUDA workflows. Do not promise successful execution on modern Python/PyTorch or CPU-only hosts.
• Do not reopen the source tree for routine answers; the references in this sub-skill are intended to be sufficient for novice and expert CNN DARTS planning.
• When advising on full training or evaluation, distinguish architecture search validation from final architecture evaluation. Search validation accuracy is not the paper result.

Evidence basis

This sub-skill distills the README CNN sections and the CNN source scripts/modules covering CIFAR/ImageNet runners, search and fixed-genotype networks, operations, architect updates, utilities, and genotype consumption. It intentionally excludes detailed genotype catalogs and visualization internals, which belong to the genotype/visualization sub-skill.