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name: training-data description: "Prepare, validate, and train SketchCode paired PNG/GUI datasets, including vocabulary, preprocessing, model architecture, and legacy training troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

SketchCode training-data

Use this sub-skill when the task involves preparing paired SketchCode training data, validating .png/.gui layouts, understanding vocabulary.vocab, running or adapting legacy training, fine-tuning from model JSON/weights, inspecting image preprocessing, or explaining the CNN-plus-GRU model structure.

Route here for

• Training SketchCode from scratch or from existing model_json.json and weights.
• Checking a flat dataset of paired sample_id.png and sample_id.gui files before training.
• Debugging training_set, validation_set, .npz feature outputs, augmentation, or validation split surprises.
• Explaining vocabulary tokens, GUI DSL tokenization, sequence generation, and model save files.

Route elsewhere

• Converting wireframe PNGs to .gui or HTML belongs to conversion-inference.
• BLEU scoring or comparing predicted/original .gui files belongs to evaluation.
• Downloading public data or pretrained model assets belongs to the root SketchCode asset workflow, not this training sub-skill.

Operating sequence

1. Read references/data-formats.md to confirm the dataset shape, vocabulary, tokenization, and preprocessing outputs.

2. Run the bundled validator before expensive or destructive training:

   python sub-skills/training-data/scripts/validate_training_dataset.py DATASET_DIR
   

3. Use the guarded bundled training wrapper when a real run is requested; it dry-runs by default and requires explicit destructive-split acknowledgement:

   python sub-skills/training-data/scripts/run_training.py --help
   

4. Read references/training-workflow.md before launching training; it explains command templates, flags/defaults, fine-tuning inputs, and the destructive split directory behavior.

5. Read references/model-overview.md when the task asks about architecture, optimizer, callbacks, saved files, sequence lengths, or memory expectations.

6. Use references/api-reference.md for a concise source-level API and CLI map.

7. If anything fails, start with references/troubleshooting.md, especially for missing pairs, duplicate GUI layouts, legacy TensorFlow/Keras/OpenCV imports, and the VOCAB_FILE relative-path issue.

Safety defaults

• Validate first; full training is long-running and mutates filesystem state.
• Stage data in a scratch parent directory. The legacy split code deletes and recreates sibling training_set and validation_set directories under the parent of --data_input_path.
• Do not fine-tune in-place inside a directory that contains important training_set or validation_set folders.
• Use the bundled wrapper's dry-run output to inspect the split directories and model output location before passing --run --allow-destructive-split.