Pangolinn: Catching Bugs in Neural Networks Like a Pangolin

Pangolinn is an open-source tool library developed by the HLT-MT research team, dedicated to detecting and diagnosing defects in neural network models. Inspired by the pangolin (a creature skilled at catching insects), it aims to help developers find hidden errors and abnormal behaviors early in the model development process. This tool addresses the unique challenges of neural network debugging through systematic diagnostic tools covering data, model, training, and evaluation layers.

Neural network debugging faces distinct difficulties compared to traditional software:

1. Black box nature: Millions of parameters make it hard to understand internal decision processes.
2. Hidden errors: Models may run and converge normally but fail in specific scenarios, easily overlooked in standard evaluations.
3. Root cause tracing: Issues can stem from data, architecture, hyperparameters, or annotations, requiring time-consuming elimination.
4. Reproducibility: Randomness in training (weight initialization, data shuffling, dropout) makes some bugs hard to replicate.

Pangolinn provides a set of combinable diagnostic tools across key stages:

• Data layer: Checks for label inconsistencies, data leakage, extreme class imbalance, and outliers.
• Model layer: Validates architecture issues like dimension mismatches, unreasonable layer configurations, and gradient flow breaks.
• Training layer: Monitors loss curves, early signs of gradient explosion/vanishing, learning rate-convergence mismatch, and overfitting signals.
• Evaluation layer: Conducts fine-grained error analysis to identify systematic failure patterns.

Pangolinn's name draws inspiration from the pangolin, a creature known for its ability to catch hidden insects—mirroring the tool's mission to find deep-seated neural network bugs. The extra 'n' in the name hints at 'neural network', linking the natural world's pangolin to the AI domain. This metaphor emphasizes the patience, systematicity, and precision needed for effective neural network debugging.

Pangolinn complements existing tools:

• Unlike TensorBoard (which visualizes training but doesn't actively diagnose issues) or model explanation tools like SHAP/LIME (focused on decision understanding), Pangolinn specializes in active defect detection.
• Developers can use it alongside these tools: TensorBoard for overall training monitoring, SHAP/LIME for decision logic understanding, and Pangolinn for systematic defect troubleshooting.

Pangolinn reflects the growing maturity of AI engineering—shifting from 'running models' to building reliable systems. It is particularly valuable for team collaboration, as standardized diagnostic tools reduce communication gaps and debugging time. In summary, Pangolinn fills a critical gap in systematic neural network debugging, becoming increasingly essential as deep learning is deployed in more critical fields.