Model Benchmarking¶
This section provides comprehensive tutorials and guides for benchmarking DNA language models using DNALLM. Benchmarking allows you to compare model performance across different tasks, datasets, and evaluation metrics.
What You'll Learn¶
- Basic Benchmarking: Get started with simple model comparisons
- Advanced Techniques: Cross-validation, custom metrics, and performance profiling
- Real-world Examples: Practical applications and use cases
- Best Practices: Optimization strategies and troubleshooting
Quick Navigation¶
| Topic | Description | Difficulty |
|---|---|---|
| Getting Started | Basic benchmarking setup and configuration | Beginner |
| Advanced Techniques | Cross-validation, custom metrics, profiling | Intermediate |
| Configuration Guide | Detailed configuration options and examples | Intermediate |
| Examples and Use Cases | Real-world benchmarking scenarios | All Levels |
| Troubleshooting | Common issues and solutions | All Levels |
Prerequisites¶
Before diving into benchmarking, ensure you have:
- ✅ DNALLM installed and configured
- ✅ Access to DNA language models
- ✅ Test datasets in appropriate formats
- ✅ Sufficient computational resources
Quick Start¶
from dnallm import load_config, Benchmark
# Load configuration
config = load_config("benchmark_config.yaml")
# Initialize and run benchmark
benchmark = Benchmark(config=config)
results = benchmark.run()
Key Features¶
- Multi-Model Comparison: Evaluate multiple models simultaneously
- Comprehensive Metrics: Accuracy, F1, precision, recall, ROC-AUC, and more
- Performance Profiling: Memory usage, inference time, and resource monitoring
- Flexible Output: HTML reports, CSV exports, and interactive visualizations
- Cross-Validation: Robust evaluation with k-fold validation
Next Steps¶
Choose your path:
- New to benchmarking? Start with Getting Started
- Want advanced features? Jump to Advanced Techniques
- Need configuration help? Check Configuration Guide
- Looking for examples? Explore Examples and Use Cases