ML/DL Optimization

Overview

This section covers critical optimization strategies for machine learning and deep learning workloads. Understanding these concepts can significantly improve training efficiency, reduce costs, and help you get the most out of your GPU hardware.

Topics Covered

Performance Optimization

• Batch Size Selection - Understanding how batch size affects training speed and GPU utilization
• Learning Rate Tuning - Finding optimal learning rates for faster convergence
• Data Loading - Eliminating data bottlenecks in your training pipeline

Resource Management

• GPU Memory Management - Maximizing GPU memory usage and handling OOM errors
• Mixed-precision training techniques
• Gradient accumulation strategies

Common Pitfalls

Caution

Many optimization techniques have trade-offs that aren’t immediately obvious:

• Larger batch sizes don’t always mean faster training
• Higher learning rates can lead to unstable training
• GPU utilization at 100% doesn’t guarantee optimal performance

Best Practices

1. Profile First - Use tools like nvidia-smi, nvtop, or PyTorch Profiler to identify bottlenecks
2. Monitor Metrics - Track GPU utilization, memory usage, and data loading times
3. Iterate Gradually - Change one parameter at a time to understand its impact
4. Document Changes - Keep track of what works and what doesn’t for your specific use case

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These guides provide practical, tested solutions for common optimization challenges in deep learning workflows.