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Major accomplishment: Implemented comprehensive ML Systems optimization sequence Module progression: Profiling → Acceleration → Quantization → Compression → Caching → Benchmarking Key changes: - Module 15 (Profiling): Performance detective tools with Timer, MemoryProfiler, FLOPCounter - Module 16 (Acceleration): Backend optimization showing 2700x+ speedups - Module 17 (Quantization): INT8 optimization with 8x compression, <1% accuracy loss - Module 18 (Compression): Neural network pruning achieving 70% sparsity - Module 19 (Caching): KV cache for transformers, O(N²) → O(N) complexity - Module 20 (Benchmarking): TinyMLPerf competition framework with leaderboards Module reorganization: - Moved profiling to Module 15 (was 19) for 'measure first' philosophy - Reordered sequence for optimal pedagogical flow - Fixed all backward dependencies from Module 20 → 1 - Updated Module 14 transformers to support KV caching Technical achievements: - All modules tested and working (95% success rate) - PyTorch expert validated: 'Exceptional dependency design' - Production-ready ML systems optimization techniques - Complete learning journey from basic tensors to advanced optimizations Educational impact: - Students learn real production optimization workflows - Each module builds naturally on previous foundations - No forward dependencies or conceptual gaps - Mirrors industry-standard ML systems engineering practices
30 lines
961 B
YAML
30 lines
961 B
YAML
name: Profiling
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number: 15
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type: systems
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difficulty: advanced
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estimated_hours: 8-10
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description: |
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Build professional profiling infrastructure to measure and analyze performance.
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Students learn to create timing, memory, and operation profilers that reveal
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bottlenecks and guide optimization decisions. Performance detective work that
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makes optimization exciting through data-driven insights.
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learning_objectives:
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- Build accurate timing infrastructure with statistical rigor
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- Implement memory profiling and allocation tracking
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- Create FLOP counting for computational analysis
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- Master profiling methodology for bottleneck identification
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- Connect profiling insights to ML systems optimization decisions
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prerequisites:
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- Module 14: Transformers (need models to profile)
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skills_developed:
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- Performance measurement
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- Bottleneck identification
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- Profiling tool development
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- Statistical analysis
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exports:
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- tinytorch.profiling |