15. Profiling

15. Profiling#

Module Overview

Performance detective work - discover why your models are slow and identify optimization opportunities.

What You’ll Build#

In this module, you’ll become a performance detective and build the tools to understand exactly what’s happening inside your ML systems:

  • Professional timing systems with statistical rigor for accurate performance measurement

  • Memory profilers that track allocation patterns and identify memory bottlenecks

  • FLOP counters that reveal the computational complexity of different operations

  • Architecture comparisons that show why transformers are slow but powerful

Learning Objectives#

Eye-Opening Discovery

You might be shocked to discover that your “simple” attention mechanism consumes 73% of your compute time!

By completing this module, you will be able to:

  1. Build professional profilers including timing, memory, and FLOP counting systems

  2. Identify performance bottlenecks and understand which operations are consuming your resources

  3. Compare architectures scientifically using data-driven analysis of MLPs, CNNs, and Transformers

  4. Guide optimization decisions with quantitative measurements and statistical analysis

  5. Understand scaling behavior and predict performance at different model sizes

Systems Concepts#

This module covers critical ML systems concepts:

  • Statistical performance measurement with confidence intervals and variance analysis

  • Memory profiling techniques for identifying allocation patterns and leaks

  • Computational complexity analysis through FLOP counting and operation decomposition

  • Performance bottleneck identification using systematic measurement methodology

  • Architecture comparison frameworks for data-driven optimization decisions

Prerequisites#

  • Modules 1-14: Complete neural network implementations to profile and analyze

  • Understanding of basic statistics for performance measurement interpretation

Time Estimate#

5-6 hours - Comprehensive profiling implementation with detailed analysis across all architectures

Getting Started#

Open the profiling module and begin your performance detective work:

# Navigate to the module
cd modules/15_profiling

# Open the development notebook
tito module view 15_profiling

# Complete the module
tito module complete 15_profiling

What You’ll Discover#

Performance Revelations

Prepare for some surprising discoveries about where your compute time actually goes!

Your profiling investigation will reveal:

  • Which operations are eating your CPU cycles (spoiler: it’s probably not what you think)

  • Where memory disappears and how different architectures use RAM

  • The shocking differences between MLP, CNN, and Transformer computational patterns

  • Why PyTorch is 100x faster than your implementations (and what you can do about it)

Next Steps#

After completing profiling, you’ll be ready for:

  • Module 16 (Acceleration): Use your profiling data to actually fix the performance problems you discovered

Production Context#

Real-World Impact

The profiling techniques you learn here are the same ones used to optimize production ML systems at scale.

Professional ML systems engineering requires systematic performance analysis:

  • Identifying bottlenecks in training pipelines that process petabytes of data

  • Optimizing inference latency for real-time applications serving millions of users

  • Memory optimization for deploying large models on resource-constrained hardware

  • Cost optimization in cloud environments where compute efficiency directly impacts expenses

The profiling skills you develop here are essential for building efficient, scalable ML systems in production environments.

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