Core Concepts

Overview

ORruns is an experiment tracking framework designed for optimization and operations research. It provides:

Experiment Management: Track and compare multiple optimization runs
Parameter Control: Manage and version experimental parameters
Results Tracking: Record metrics, artifacts and performance data
Reproducibility: Ensure experiments can be replicated
Analysis Tools: Visualize and analyze experimental results

Key Components

1. Experiments

An experiment represents a complete research study or optimization task. It: - Groups related runs together - Maintains consistent parameter spaces - Tracks overall progress and results - Provides comparative analysis

2. Runs

A run is a single execution of an experiment. Each run: - Has unique parameters and configurations - Records performance metrics - Stores generated artifacts - Maintains its own execution context

3. Parameters

Parameters define the configuration of a run: - Solver settings - Algorithm parameters - Problem configurations - Environmental variables

4. Metrics

Metrics capture the performance and results: - Objective values - Solution quality - Computational time - Resource usage - Convergence data

5. Artifacts

Artifacts are files generated during a run: - Visualization plots - Solution data - Log files - Model checkpoints

Data Organization

Each experiment follows a structured organization:

experiment_name/
├── run_[timestamp]/
│   ├── params/          # Configuration parameters
│   ├── metrics/         # Performance metrics
│   ├── artifacts/       # Generated files
│   │   ├── figures/     # Plots and visualizations
│   │   └── data/       # Data files
│   └── metadata.json    # Run information
└── summary.json         # Experiment overview

Core Features

1. Experiment Lifecycle Management

Experiment creation and configuration
Run initialization and execution
Resource cleanup and management
Version control and tracking

2. Data Collection

Automated metric logging
Parameter versioning
Artifact management
Environment tracking

3. Analysis Capabilities

Cross-run comparisons
Parameter sensitivity analysis
Performance visualization
Statistical analysis

4. Integration Support

API access
Dashboard visualization
External tool integration
Custom extensions

Best Practices

1. Experiment Organization

Use descriptive experiment names
Group related runs logically
Maintain consistent structure
Document experiment purpose

2. Parameter Management

Version all parameters
Use consistent naming
Document parameter meanings
Track parameter relationships

3. Results Tracking

Log comprehensive metrics
Save intermediate results
Include visualization artifacts
Maintain raw data

4. Reproducibility

Set random seeds
Document dependencies
Track environment details
Version control code

Advanced Concepts

1. Parallel Execution

Multiple run management
Resource allocation
Result synchronization
Error handling

2. Data Analysis

Comparative analysis
Statistical testing
Performance profiling
Result visualization

3. Integration

External tool support
Custom metric logging
Artifact management
API extensions