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name: quark0-darts description: "Use the original DARTS research code for differentiable architecture search, CNN/RNN genotypes, CIFAR/ImageNet/PTB/WT2 workflows, and legacy PyTorch troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DARTS

Use this repo skill when a task is about the original DARTS: Differentiable Architecture Search codebase: convolutional architecture search/evaluation, recurrent language-modeling search/evaluation, genotype schemas, visualization, dataset/checkpoint layout, or legacy PyTorch/CUDA failures.

This is a source-backed operating skill for a script-style research repository. It does not assume the original checkout is available, does not install a package, and does not claim that paper-scale native runs were executed during skill production.

Start here

1. Read legacy runtime before promising native execution. The original code targets Python 3.5-era PyTorch 0.3.1/torchvision 0.2.0 and CUDA.
2. Read data and checkpoints when a workflow needs CIFAR-10, ImageNet, PTB, WikiText-2, or pretrained model files.
3. Read troubleshooting for cross-cutting failures such as async=True syntax errors, missing CUDA, dataset assertions, checkpoint-format mismatches, and Graphviz issues.
4. Check repo provenance before treating this skill as current for a different DARTS checkout.

Route by task

User task	Read
CIFAR-10 DARTS architecture search, CIFAR-10 training/test, ImageNet training/test, CNN model/cell/operation facts, or CNN CUDA/OOM/nondeterminism failures	cnn-architectures
Penn Treebank or WikiText-2 recurrent DARTS search/training/test, corpus layout, checkpoint behavior, ASGD schedule, perplexity interpretation, or RNN CUDA/hidden-size/data failures	rnn-language-modeling
CNN/RNN genotype schemas, built-in DARTS/NASNet/AmoebaNet/ENAS genotypes, converting search outputs, validating custom genotypes, or DOT visualization	genotypes-and-visualization
Construct a native command without launching a job	darts_command_builder.py
Inspect a local DARTS-style source tree before running or porting it	darts_static_inspector.py

Safe helper scripts

• Run python scripts/darts_command_builder.py list to see workflow ids.
• Run python scripts/darts_command_builder.py build cnn-search --smoke --gpu 0 to print a wiring-check command and prerequisites. The helper does not run training or download data.
• Run python scripts/darts_static_inspector.py --repo-root <path> to statically check an external DARTS source tree. The helper does not import the repo.
• Run python sub-skills/genotypes-and-visualization/scripts/darts_genotype_tools.py list to inspect bundled genotype catalogs and emit DOT without Graphviz.

Operating rules

• Distinguish architecture search from final evaluation. Search validation accuracy/perplexity is not the paper result.
• Treat full native workflows as legacy CUDA jobs. If the user is on modern Python/PyTorch, route to porting guidance instead of implying direct compatibility.
• Never report smoke-mode output as paper accuracy or perplexity.
• Keep CNN and RNN genotype schemas separate; both define DARTS, but the tuple fields and valid edges differ.
• Do not tell future agents to open original repo scripts or README pages for routine operation. Use the bundled references and scripts in this skill.
• If a requested dataset, pretrained checkpoint, CUDA runtime, or legacy PyTorch build is missing, report the missing prerequisite clearly and stop before inventing a result.