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name: pytorch-examples description: "Routes PyTorch SIGNS and NER example requests to the correct preprocessing, training, evaluation, hyperparameter search, and results workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PyTorch Examples

Use this sub-skill for every PyTorch workflow in the repository:

• vision on the SIGNS image dataset;
• NLP on the named-entity recognition dataset;
• preprocessing, training, evaluation, hyperparameter search, and result synthesis for either workflow.

The helper scripts in this sub-skill are safe wrappers around the repository's PyTorch example commands. Future agents should use them instead of trying to reconstruct the working directory and flags from memory.

Read when

• the user mentions PyTorch, torch, torchvision, SIGNS, hand signs, or image classification;
• the user mentions PyTorch NER, vocab building, Kaggle CSV splitting, or sentence tagging;
• the user wants a train/evaluate/search/synthesize command for a PyTorch example;
• the user needs PyTorch-specific troubleshooting or data-layout guidance.

What this sub-skill covers

Vision route

• resizing the SIGNS dataset to 64x64;
• training the convolutional classifier;
• evaluating a saved checkpoint;
• launching the built-in learning-rate search;
• aggregating experiment metrics.

NLP route

• converting the Kaggle CSV into train/, val/, and test/ text splits;
• building word and tag vocabularies;
• training the BiLSTM-style NER model;
• evaluating a saved checkpoint;
• launching the built-in learning-rate search;
• aggregating experiment metrics.

What is excluded

• TensorFlow workflows.
• Any workflow that needs the TensorFlow 1.15 graph/session API.
• Repository-wide provenance or framework selection questions; those belong in the root skill.

Key bundled files

• references/workflows.md for copyable command sequences and data layouts.
• references/api-reference.md for verified local module signatures and helper behavior.
• references/troubleshooting.md for common PyTorch setup and data issues.
• scripts/run_workflow.py for a safe command wrapper with --repo-root and --execute support.

Installation guidance

• Install the packages listed in ../../pytorch/vision/requirements.txt when you need the vision workflow.
• Install the packages listed in ../../pytorch/nlp/requirements.txt when you need the NER workflow.
• A single environment that satisfies both files is usually enough for the full PyTorch sub-skill.
• Keep the environment isolated so the example scripts do not accidentally pick up unrelated local imports.

Example install commands:

python -m pip install -r ../../pytorch/vision/requirements.txt
python -m pip install -r ../../pytorch/nlp/requirements.txt

Minimal check

Run the shared environment helper first:

python ../../scripts/check_env.py --frameworks pytorch

If you need to confirm the local checkout as well, add --repo-root <repo-root>.

Workflow selection

• Use the vision route when the task mentions images, signs, resizing, or 64x64 SIGNS preprocessing.
• Use the NLP route when the task mentions sentences, tokens, tags, words, Kaggle CSV splitting, or vocabulary building.

If the user only asks for a command, read references/workflows.md first. If an import or runtime error appears, jump to references/troubleshooting.md.