Principle:EvolvingLMMs Lab Lmms eval Experiment Tracking
| Knowledge Sources | |
|---|---|
| Domains | Machine Learning Operations, Reproducibility |
| Last Updated | 2026-02-14 00:00 GMT |
Overview
Experiment tracking systematically logs evaluation runs, metrics, and artifacts to enable reproducibility and comparison across experiments.
Description
Experiment tracking provides a structured approach to recording model evaluation experiments. It captures configuration parameters, evaluation metrics, model predictions, and metadata in a centralized platform (like Weights & Biases). This enables researchers to compare results across different models, hyperparameters, and evaluation setups, reproduce past experiments, and visualize performance trends over time. Key components include run initialization with unique identifiers, automatic config logging, metric aggregation, artifact versioning, and interactive visualization dashboards.
Usage
Apply this principle when conducting systematic model evaluations across multiple configurations, comparing baseline and improved models, debugging unexpected performance issues by examining individual predictions, or archiving evaluation results for future reference and reproducibility.
Theoretical Basis
Core Components
- Run Initialization: Creates unique experiment identifier with metadata (project, tags, name)
- Config Logging: Records all configuration parameters (model args, task settings, evaluation settings)
- Metric Logging: Captures scalar metrics (accuracy, perplexity, etc.) and associates them with runs
- Artifact Storage: Versions and stores complete results, predictions, and model outputs
- Visualization: Generates tables, charts, and dashboards for interactive exploration
Data Organization
- Hierarchical Metrics: Organized as task_name/metric_name for clear attribution
- String vs Numeric: String metrics stored in summary, numeric metrics in logs for plotting
- Grouped Tasks: Tasks belonging to groups are aggregated and displayed together
- Sample-Level Data: Individual predictions stored with inputs, targets, and computed metrics
Best Practices
- Naming Convention: Use descriptive run names that include model, task, and key parameters
- Project Organization: Group related experiments in the same project
- Tag Usage: Add tags for filtering and organizing runs (e.g., "baseline", "ablation")
- Offline Mode: Use offline mode for debugging, then sync later
- Artifact Versioning: Archive complete results as JSON artifacts for long-term storage
Retry and Robustness
- Implement exponential backoff for network failures
- Fall back to offline mode if initialization fails
- Handle non-serializable objects gracefully
- Validate data before logging to prevent partial uploads