EP-028: Interactive Energy Phase Visualization for AI Inference
Building an interactive visualization tool for analyzing energy consumption phases during AI inference — from tokenization through generation.
Today’s Engineering Journey
What I Built
Today, I extended the SDB Energy Profiler with a critical visualization component: an interactive Dash-powered dashboard for analyzing power consumption across different inference pipeline phases. The key breakthrough is our ability to dynamically filter and visualize power consumption data with granular phase-level insights.
Key Technical Implementations
- Created
phase_energy_viz.pyusing Dash and Plotly - Integrated with existing power sampling infrastructure
- Developed interactive visualizations:
- Scatter plot showing power consumption over time
- Boxplot revealing power distribution across inference phases
Challenges & Insights
The most interesting challenge was designing a visualization that doesn’t just display data, but tells a story about energy consumption. By color-coding different inference phases and allowing dynamic filtering, we’ve created a tool that transforms raw power samples into meaningful insights.
What I Missed
- Didn’t implement confidence-based filtering in the visualization
- Need to enhance error handling for potential data inconsistencies
- Requires more comprehensive test coverage
Improvement Vectors
- Add machine learning-based anomaly detection for power consumption
- Implement more sophisticated phase detection algorithms
- Create comparative views across different hardware configurations
Tomorrow’s Horizon
- Integrate ML-based phase confidence scoring
- Expand visualization to support multi-model comparisons
- Begin work on quantization energy impact analysis
Reflection
Another day of incremental progress. The Energy Profiler isn’t just a tool—it’s our lens into understanding the environmental and computational complexity of AI systems.
Tracking our computational footprint, one sample at a time.