Update development files: streamline benchmarking and capstone dev modules

- Clean up benchmarking_dev.py implementation
- Refine capstone_dev.py development workflow

modules/19_benchmarking/benchmarking_dev.py

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.18.1
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -1760,7 +1760,6 @@ def test_module():
     test_unit_benchmark_suite()
     test_unit_tinymlperf()
     test_unit_optimization_comparison()
-    test_unit_normalized_scoring()
 
     print("\nRunning integration scenarios...")
 

modules/20_capstone/capstone_dev.py

@@ -5,7 +5,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.18.1
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -824,191 +824,7 @@ Your competition workflow demonstrates:
 
 You now understand how ML competitions work - from measurement to submission. The benchmarking tools you built in Module 19 and the optimization techniques from Modules 14-18 come together in Module 20's competition workflow.
 
-**What's Next:** 
+**What's Next:**
-- Build TinyGPT in Milestone 05 (historical achievement)
-- Compete in Torch Olympics (Milestone 06) using this workflow
-- Use `tito olympics submit` to generate your competition entry!
-"""
-
-# %% [markdown]
-"""
-## 🎯 Module Integration Test
-
-Final comprehensive test validating all components work together correctly.
-"""
-
-# %% nbgrader={"grade": true, "grade_id": "test_module", "locked": true, "points": 20}
-def test_module():
-    """
-    Comprehensive test of entire competition module functionality.
-
-    This final test runs before module summary to ensure:
-    - OlympicEvent enum works correctly
-    - calculate_normalized_scores computes correctly
-    - generate_submission creates valid submissions
-    - validate_submission checks requirements properly
-    - Complete workflow demonstration executes
-    """
-    print("🧪 RUNNING MODULE INTEGRATION TEST")
-    print("=" * 60)
-
-    # Test 1: OlympicEvent enum
-    print("🔬 Testing OlympicEvent enum...")
-    assert OlympicEvent.LATENCY_SPRINT.value == "latency_sprint"
-    assert OlympicEvent.MEMORY_CHALLENGE.value == "memory_challenge"
-    assert OlympicEvent.ALL_AROUND.value == "all_around"
-    print("  ✅ OlympicEvent enum works")
-
-    # Test 2: Normalized scoring
-    print("\n🔬 Testing normalized scoring...")
-    baseline = {'latency': 100.0, 'memory': 12.0, 'accuracy': 0.85}
-    optimized = {'latency': 40.0, 'memory': 3.0, 'accuracy': 0.83}
-    scores = calculate_normalized_scores(baseline, optimized)
-    assert abs(scores['speedup'] - 2.5) < 0.01
-    assert abs(scores['compression_ratio'] - 4.0) < 0.01
-    print("  ✅ Normalized scoring works")
-
-    # Test 3: Submission generation
-    print("\n🔬 Testing submission generation...")
-    submission = generate_submission(
-        baseline_results=baseline,
-        optimized_results=optimized,
-        event=OlympicEvent.LATENCY_SPRINT,
-        athlete_name="TestUser"
-    )
-    assert submission['event'] == 'latency_sprint'
-    assert 'normalized_scores' in submission
-    assert 'system_info' in submission
-    print("  ✅ Submission generation works")
-
-    # Test 4: Submission validation
-    print("\n🔬 Testing submission validation...")
-    validation = validate_submission(submission)
-    assert validation['valid'] == True
-    assert len(validation['checks']) > 0
-    print("  ✅ Submission validation works")
-
-    # Test 5: Complete workflow
-    print("\n🔬 Testing complete workflow...")
-    demonstrate_competition_workflow()
-    print("  ✅ Complete workflow works")
-
-    print("\n" + "=" * 60)
-    print("🎉 ALL COMPETITION MODULE TESTS PASSED!")
-    print("✅ Competition workflow fully functional!")
-    print("📊 Ready to generate submissions!")
-    print("\nRun: tito module complete 20")
-
-# Call the comprehensive test
-test_module()
-
-# %% nbgrader={"grade": false, "grade_id": "main_execution", "solution": false}
-if __name__ == "__main__":
-    print("🚀 Running TinyTorch Olympics Competition module...")
-
-    # Run the comprehensive test
-    test_module()
-
-    print("\n✅ Competition module ready!")
-    print("📤 Use generate_submission() to create your competition entry!")
-
-# %% [markdown]
-"""
-## 🤔 ML Systems Thinking: Competition Workflow Reflection
-
-This capstone teaches the workflow of professional ML competitions. Let's reflect on the systems thinking behind competition participation.
-
-### Question 1: Statistical Confidence
-You use Module 19's Benchmark harness which runs multiple trials and reports confidence intervals.
-If baseline latency is 50ms ± 5ms and optimized is 25ms ± 3ms, can you confidently claim improvement?
-
-**Answer:** [Yes/No] _______
-
-**Reasoning:** Consider whether confidence intervals overlap and what that means for statistical significance.
-
-### Question 2: Event Selection Strategy
-Different Olympic events have different constraints (Latency Sprint: accuracy ≥ 85%, Extreme Push: accuracy ≥ 80%).
-If your optimization reduces accuracy from 87% to 82%, which events can you still compete in?
-
-**Answer:** _______
-
-**Reasoning:** Check which events' accuracy constraints you still meet.
-
-### Question 3: Normalized Scoring
-Normalized scores enable fair comparison across hardware. If Baseline A runs on fast GPU (10ms) and Baseline B runs on slow CPU (100ms), both optimized to 5ms:
-- Which has better absolute time? _______
-- Which has better speedup? _______
-- Why does normalized scoring matter? _______
-
-### Question 4: Submission Validation
-Your validate_submission() function checks event constraints and flags unrealistic improvements.
-If someone claims 100× speedup, what should the validation do?
-
-**Answer:** _______
-
-**Reasoning:** Consider how to balance catching errors vs allowing legitimate breakthroughs.
-
-### Question 5: Workflow Integration
-Module 20 uses Benchmark from Module 19 and optimization techniques from Modules 14-18.
-What's the key insight about how these modules work together?
-
-a) Each module is independent
-b) Module 20 provides workflow that uses tools from other modules
-c) You need to rebuild everything in Module 20
-d) Competition is separate from benchmarking
-
-**Answer:** _______
-
-**Explanation:** Module 20 teaches workflow and packaging - you use existing tools, not rebuild them.
-"""
-
-# %% [markdown]
-"""
-## 🎯 MODULE SUMMARY: TinyTorch Olympics - Competition & Submission
-
-Congratulations! You've completed the capstone module - learning how to participate in professional ML competitions!
-
-### Key Accomplishments
-- **Understood competition events** and how to choose the right event for your optimization goals
-- **Used Benchmark harness** from Module 19 to measure performance with statistical rigor
-- **Generated standardized submissions** following MLPerf-style format
-- **Validated submissions** meet competition requirements
-- **Demonstrated complete workflow** from measurement to submission
-- All tests pass ✅ (validated by `test_module()`)
-
-### Systems Insights Gained
-- **Competition workflow**: How professional ML competitions are structured and participated in
-- **Submission packaging**: How to format results for fair comparison and validation
-- **Event constraints**: How different events require different optimization strategies
-- **Workflow integration**: How to use benchmarking tools (Module 19) + optimization techniques (Modules 14-18)
-
-### The Complete Journey
-```
-Module 01-18: Build ML Framework
-    ↓
-Module 19: Learn Benchmarking Methodology
-    ↓
-Module 20: Learn Competition Workflow
-    ↓
-Milestone 05: Build TinyGPT (Historical Achievement)
-    ↓
-Milestone 06: Torch Olympics (Optimization Competition)
-```
-
-### Ready for Competition
-Your competition workflow demonstrates:
-- **Professional submission format** following industry standards (MLPerf-style)
-- **Statistical rigor** using Benchmark harness from Module 19
-- **Event understanding** knowing which optimizations fit which events
-- **Validation mindset** ensuring submissions meet requirements before submitting
-
-**Export with:** `tito module complete 20`
-
-**Achievement Unlocked:** 🏅 **Competition Ready** - You know how to participate in professional ML competitions!
-
-You now understand how ML competitions work - from measurement to submission. The benchmarking tools you built in Module 19 and the optimization techniques from Modules 14-18 come together in Module 20's competition workflow.
-
-**What's Next:** 
 - Build TinyGPT in Milestone 05 (historical achievement)
 - Compete in Torch Olympics (Milestone 06) using this workflow
 - Use `tito olympics submit` to generate your competition entry!