Enhanced Model Performance Visualization with Violin Plots

• Day: 2025-12-10
• Time: 21:45 to 22:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Data Visualization, Python, Matplotlib, Model Performance, Data Diagnostics

Description

Session Goal: The session aimed to enhance the visualization of model performance metrics using Python, focusing on creating violin plots to better understand performance distributions across different models and accelerators.

Key Activities:

1. Developed Python scripts to generate violin plots for visualizing model performance in tokens per second, incorporating data loading, filtering, and diagnostics.
2. Implemented robust error handling for file loading and ensured models with sufficient data were considered for plotting.
3. Enhanced plotting scripts by adjusting tick labels, color schemes, and annotations for improved clarity.
4. Conducted data diagnostics and recommended normalization schemas for datasets, providing practical Python code for diagnostics and plotting.
5. Created a horizontal bar chart to visualize energy efficiency metrics, detailing the perf_per_watt calculation using Seaborn and Matplotlib.

Achievements: Successfully created and refined plotting scripts for model performance analysis, ensuring robust data handling and visualization clarity. Developed a reusable plotting cell for future performance analysis tasks.

Pending Tasks: Further improvements to the plotting scripts and exploration of additional visualization techniques for enhanced insights.

Evidence

• source_file=2025-12-10.sessions.jsonl, line_number=0, event_count=0, session_id=9208f8d2f4c86a5f59078c38843c474963ba565d3f3cebbc74b3b244e662393b
• event_ids: []