Optimized Xtream AI Diamond Valuation System Performance

📅 2024-04-18 — Session: Optimized Xtream AI Diamond Valuation System Performance

🕒 17:55–20:15
🏷️ Labels: Performance, Optimization, Flask, Mlflow, API, Profiling
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The session aimed to optimize the performance of the Xtream AI Diamond Valuation System through a series of structured tasks focusing on benchmarking, code optimization, and performance monitoring.

Key Activities:

• Developed a performance and optimization checklist for the Xtream AI system, covering benchmarking, code optimization, and load handling.
• Conducted a performance review and benchmarking to identify KPIs and establish baseline measurements.
• Set up and monitored KPIs such as response time, throughput, CPU, and memory usage.
• Integrated performance monitoring features into the application UI using Flask and JavaScript.
• Implemented API performance testing for backend and frontend using Flask and JavaScript.
• Diagnosed and resolved 404 errors in Flask applications, focusing on endpoint path correctness and request method handling.
• Profiled and optimized the /retrain endpoint using Python’s cProfile and line_profiler.
• Enhanced MLflow model logging performance by optimizing subprocess communication and caching.

Achievements:

• Successfully integrated performance monitoring into the application UI.
• Implemented API performance testing and resolved 404 errors in Flask endpoints.
• Optimized model retraining processes and MLflow model logging.

Pending Tasks:

• Further optimization of MLflow’s log_model function to reduce execution time.
• Analysis of diamond ratios in histograms for better understanding of gemology standards.