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TQNN Tennessee Eastman Benchmark
Official benchmark notebook for evaluating the TQNN Fault-Tolerant Inference Platform on the Tennessee Eastman Process (TEP).
Overview
This repository demonstrates how to:
- Connect to the public TQNN API
- Authenticate using an API key
- Run fault-tolerant inference
- Evaluate prediction confidence
- Inspect data integrity
- Review runtime diagnostics
The notebook is intended as a reproducible example for developers, researchers, and engineers exploring confidence-aware inference on industrial process data.
Repository Contents
- 📓 Benchmark notebook
- 🖼️ TQNNLabs banner
- 📖 Documentation
- 📊 Example outputs
Requirements
- Python 3.9+
- TQNN Python SDK
- TQNN API key
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