From fece8d0b52d5ab345010aa97bdad0db4ba5f2c9d Mon Sep 17 00:00:00 2001
From: Vijay Janapa Reddi <vj@eecs.harvard.edu>
Date: Wed, 19 Nov 2025 22:06:29 -0500
Subject: [PATCH] Remove archived and unnecessary files from git tracking

- Remove COMMIT_LOG.txt (already in .gitignore)
- Remove archived competition module (20_competition_ARCHIVED)
- Remove missing text files (ISSUES_DIAGRAM.txt, REVIEW_SUMMARY.txt)
---
 COMMIT_LOG.txt                                | 141 ---
 modules/16_compression/ISSUES_DIAGRAM.txt     | 220 ----
 modules/16_compression/REVIEW_SUMMARY.txt     | 191 ----
 .../20_competition_ARCHIVED/competition.py    | 891 ----------------
 .../competition_dev.py                        | 977 ------------------
 modules/20_competition_ARCHIVED/module.yaml   |  59 --
 6 files changed, 2479 deletions(-)
 delete mode 100644 COMMIT_LOG.txt
 delete mode 100644 modules/16_compression/ISSUES_DIAGRAM.txt
 delete mode 100644 modules/16_compression/REVIEW_SUMMARY.txt
 delete mode 100644 modules/20_competition_ARCHIVED/competition.py
 delete mode 100644 modules/20_competition_ARCHIVED/competition_dev.py
 delete mode 100644 modules/20_competition_ARCHIVED/module.yaml

diff --git a/COMMIT_LOG.txt b/COMMIT_LOG.txt
deleted file mode 100644
index 85be920e..00000000
--- a/COMMIT_LOG.txt
+++ /dev/null
@@ -1,141 +0,0 @@
-2961f55 Add work completion summary for user review
-78dc030 Add comprehensive user feedback and review document
-c0d3595 Update TOC with tier overview pages and improved structure
-d9e33b2 Add Intelligence and Performance Tier overview pages
-27458d3 Update Module 07 Training - Complete Foundation Tier
-7dfab41 Update Module 06 Optimizers with professional template
-fdc8e3b Update Module 05 Autograd with professional template
-a1d60ef Fix Module 04 content - change from Networks to Losses
-ece7552 Update Module 03 Layers with professional template
-84f280d Update Module 02 Activations with professional template
-b9b525f Add website content improvements implementation guide
-3003108 Remove tito module and tito notebooks commands from CLI
-c8fb034 Fix duplicate submit commands by renaming community submit to share
-8e99df1 Add tito submit command and rename leaderboard to community
-863cde8 Add validation and normalized scoring to Module 20 competition submissions
-26fafbc Add normalized scoring to Module 19 for fair competition comparison
-7c41e2d Add MLPerf methodology to Module 19 and rebrand Module 20 as TinyMLPerf
-4a9919e Refactor Module 19 to TorchPerf Olympics framework
-80601c0 Add Profiler demo to Module 18 Compression
-6118f1e Add Profiler demo to Module 17 Quantization
-4ef3cb9 Rename ProfilerComplete to Profiler for cleaner API
-96d0fc5 Refactor Module 19 Benchmark to use ProfilerComplete from Module 15
-f670260 Fix Module 16 test to remove mixed precision trainer references
-9ad19a1 Streamline Module 18 Compression (Option 2: Moderate cleanup)
-ac75584 Streamline Module 17 Quantization by removing analysis functions
-1d663bb Remove mixed precision content from Module 16 Acceleration
-190dd29 Update project status: Module 17 Quantization complete
-e7b1337 Module 17: Export QuantizationComplete for INT8 quantization
-0fd500b Format matrix diagram in acceleration module for better readability
-8013f5d Add Module 14-15 connection section to profiling documentation
-1aea3ec Update project status: Module 15 Profiling complete
-6ae3505 Module 15: Export ProfilerComplete and create KV cache profiling demo
-45fd873 Add comprehensive documentation for KV cache path selection
-13c894f Implement REAL KV caching with 6x speedup
-fff23ef Fix enable_kv_cache to handle mask parameter and add integration test
-7b057a9 Add jupytext to requirements and export Module 14
-515384f Complete Module 14 KV caching implementation
-50176f7 Implement non-invasive KV cache integration (enable_kv_cache)
-adbc96a Add KV caching support to chatbot milestone
-d9e9e6b Consolidate environment setup to ONE canonical path
-98f0c96 Update PROJECT_STATUS: Module 14 complete (74% total progress)
-8111807 Add comprehensive integration tests for Module 14 KV Caching
-4de0d66 Document KV caching as inference-only (no gradient flow concerns)
-351fb09 Implement Module 14: KV Caching for 10-15x generation speedup
-8e1537c Document performance metrics implementation and project status
-1fe1fae Add performance metrics to transformer chatbot demo
-1340bca Fix direnv configuration to use root-level venv
-838c141 Modernize requirements to 2025 latest versions
-aa36fef Remove non-Vaswani transformer examples
-a49d4c3 docs(workflow): Clarify TinyTorch development workflow
-9c31772 Add Peacock flame theme settings for TinyTorch workspace
-73e04f2 Clean up repository by removing unnecessary documentation
-8ae4869 feat(milestone05): Update dashboard to 15-minute training for better learning
-15d3ed5 Merge transformer-training into dev
-330e173 feat(milestone05): Add celebration milestone card to TinyTalks dashboard
-3e63a03 docs(milestone05): Add visual preview of TinyTalks dashboard
-a281b67 feat(milestone05): Add rich CLI dashboard for TinyTalks training
-e005c39 docs(milestone05): Add comprehensive TinyTalks documentation
-ae3c9e5 feat(milestone05): Add TinyTalks chatbot with interactive learning dashboard
-c69b3f3 docs(milestone05): Add comprehensive 5-minute training analysis
-aac9994 feat(milestone05): Add 5-min training benchmark with 97.8% loss improvement
-e0b8ed4 feat(milestone05): Add progressive transformer validation suite
-afc1553 feat(milestone05): Add Level 1 transformer memorization test
-0555d8b fix(copilot): Fix CharTokenizer API usage in copilot milestone
-bcc51a4 test(transformers): Add training validation test file
-4cc492c test(transformers): Add comprehensive training validation suite
-88fae96 fix(tokenization): Add missing imports to tokenization module
-1cb6ed4 feat(autograd): Fix gradient flow through all transformer components
-ca93669 feat(milestones): Add monitored training script with early stopping
-c9ee345 Merge branch 'transformer-training' into dev
-9a5147e chore: Remove temporary documentation and planning files
-174ba7c fix(milestones): Use model.forward() instead of model() for TinyGPT training
-ff13efb docs(book): Update introduction, TOC, and learning progress from dev branch
-1a638c2 Merge dev into transformer-training: Add TinyTalks dataset, diagnostic tests, and training improvements
-5bc3537 feat(website): Restructure TOC with pedagogically-sound three-tier learning pathway
-a348acf fix(package): Add PyTorch-style __call__ methods to exported modules
-ee12c77 feat: Add PyTorch-style __call__ methods and update milestone syntax
-ee47236 feat(milestones): Add TinyTalks diagnostic features for systematic testing
-8338733 fix(milestones): Improve tinystories_gpt.py training output frequency
-c88da0b test(milestones): Add diagnostic script for TinyTalks learning verification
-c8b700e feat(milestones): Add tinytalks_gpt.py - Transformer training on TinyTalks dataset
-c647171 feat(datasets): Add TinyTalks v1.0 - Educational Q&A dataset for transformer training
-10b1d04 docs: Add comprehensive training fix documentation
-f37f1dc fix: Critical bug - preserve computation graph in training loop
-829a70f feat: Add TinyStories training as easier alternative to Shakespeare
-228b579 fix: Correct TransformerBlock parameter - pass mlp_ratio not hidden_dim
-d5161e7 test: Add simple pattern learning tests for transformer
-70b447a fix: Add missing typing imports to Module 10 tokenization
-69d5621 refactor: Use CharTokenizer from Module 10 instead of manual tokenization
-6e28844 fix: Update transformer config to industry best practices
-b3b8194 test: Add comprehensive transformer learning verification
-fad3f7c chore: Remove temporary documentation files from tests/
-c0b4f22 docs: Add gradient flow test suite summary
-fc4cb76 test: Add comprehensive NLP component gradient flow tests
-c97ba79 docs: Add comprehensive gradient flow fixes documentation
-df43b8a chore: Remove temporary debug test files
-6cb37bc fix(autograd): Complete transformer gradient flow - ALL PARAMETERS NOW WORK!
-578b6d7 fix(autograd): Add SoftmaxBackward and patch Softmax.forward()
-ff8702e fix(autograd): Add EmbeddingBackward and ReshapeBackward
-471d2af docs: Add comprehensive gradient flow fix summary
-6733f2d test: Move gradient flow tests to proper locations
-4c93844 fix(module-05): Add TransposeBackward and fix MatmulBackward for batched ops
-c7af13d fix(milestones): Fix milestone scripts and transformer setup
-a832851 fix(module-13): Rewrite LayerNorm to use Tensor operations
-4a5c15c fix(module-12): Rewrite attention to use batched Tensor operations
-8cff435 fix(module-11): Fix Embedding and PositionalEncoding gradient flow
-fcecbe5 fix(module-05): Add SubBackward and DivBackward for autograd
-8c1be08 fix(module-03): Rewrite Dropout to use Tensor operations
-baf5727 fix(module-02): Rewrite Softmax to use Tensor operations
-db1f0a2 fix(module-01): Fix batched matmul and transpose grad preservation
-86f20a3 🎨 Add Rich CLI formatting to transformer milestone 05
-1bfb1cb ✅ Complete transformer module fixes and milestone 05
-8546e3e 🤖 Fix transformer module exports and milestone 05 imports
-645ef47 ✨ Add Shakespeare dataset to DatasetManager
-f02fe68 🔄 Rename milestone 06: mlperf → scaling (2020 GPT-3 era)
-c4d5e4e 🏗️ Restructure milestones with decade-based naming
-0ae627d Clean root directory: remove debug scripts, status files, and redundant docs
-f1ae172 🧹 Remove book/_build/ artifacts from git tracking
-59bbf7f 🧹 Remove git-rewrite temporary files
-f9449ee Merge remote dev branch with local website updates
-9982d7c 🧹 Clean up book files
-019c8ba 🧹 Clean up git-rewrite temporary files
-68ac62f 📚 Update website navigation and content
-2d4294a Add activity badges to README
-b8f7ee2 Add activity badges to README
-ea53d00 Fix modules 10-13 tests and add CLAUDE.md
-791b09a Fix modules 10-13 tests and add CLAUDE.md
-0ad52f8 refactor: Update transformers module and milestone compatibility
-6603e00 refactor: Update transformers module and milestone compatibility
-ed52d8a refactor: Update attention module to match tokenization style
-77e2e7f refactor: Update attention module to match tokenization style
-3a1b08f Merge remote-tracking branch 'origin/dev' into dev
-4d70e30 refactor: Update embeddings module to match tokenization style
-daaf507 Update work in progress status in README
-1bddedf Add .cursor/ and .claude/ to .gitignore and remove from tracking
-805608e fix: Adjust ASCII diagram spacing for consistent alignment
-c43c5d8 docs: Improve tokenization module with enhanced ASCII diagrams
-6efe112 refactor: Standardize imports across modules 10-17 to match 01-09
-9bb506a Merge pull request #7 from Zappandy/feature/dynamic-venv-config
-7f5d591 fixed default venv value in config for validation
-e96d821 Feat(env) dynamic virtual env support for advanced users
diff --git a/modules/16_compression/ISSUES_DIAGRAM.txt b/modules/16_compression/ISSUES_DIAGRAM.txt
deleted file mode 100644
index 93ccfd4e..00000000
--- a/modules/16_compression/ISSUES_DIAGRAM.txt
+++ /dev/null
@@ -1,220 +0,0 @@
-================================================================================
-MODULE 17 COMPRESSION - ISSUES VISUALIZATION
-================================================================================
-
-OVERALL MODULE HEALTH: 6.5/10
-[████████████████░░░░] 65%
-
-BREAKDOWN BY CATEGORY:
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-1. NBGrader Structure:        [████████░░] 5/10  ⚠️  NEEDS WORK
-2. Educational Content:       [█████████░] 9/10  ✅  EXCELLENT
-3. Docstrings:               [█████████░] 9/10  ✅  EXCELLENT  
-4. Module Structure:          [████░░░░░░] 4/10  ❌  CRITICAL
-5. Memory Profiling:          [███████░░░] 7/10  ⚠️  GOOD
-6. Performance Benchmarking:  [███████░░░] 7/10  ⚠️  GOOD
-7. ML Systems Analysis:       [███████░░░] 7/10  ⚠️  GOOD
-8. Test Coverage:             [████████░░] 8/10  ✅  VERY GOOD
-9. Production Context:        [█████████░] 9/10  ✅  EXCELLENT
-
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-CRITICAL ISSUES FLOW:
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-Issue #1: SEQUENTIAL CLASS (Lines 72-91)
-┌─────────────────────────────────────────────────────────────────┐
-│  Current Problem:                                                │
-│  ┌──────────────┐                                               │
-│  │ Sequential   │ ← FORBIDDEN: Composition class in module     │
-│  │ class hides  │   Violates: "Modules build components,       │
-│  │ layer flow   │             NOT compositions"                │
-│  └──────────────┘                                               │
-│                                                                  │
-│  Impact:                                                         │
-│  • Students don't see explicit layer chaining                   │
-│  • Breaks pedagogical principle of visible data flow            │
-│  • Used in 7+ test functions                                    │
-│                                                                  │
-│  Solution:                                                       │
-│  Option A: Move to milestone helpers                            │
-│  Option B: Rewrite tests with explicit layer composition        │
-│  ┌────────────────────────────────────────────────────┐        │
-│  │ class TestModel:                                   │        │
-│  │     def __init__(self):                            │        │
-│  │         self.layer1 = Linear(10, 5)  # Explicit!  │        │
-│  │         self.layer2 = Linear(5, 2)   # Visible!   │        │
-│  │     def forward(self, x):                          │        │
-│  │         x = self.layer1.forward(x)   # Clear!     │        │
-│  │         x = self.layer2.forward(x)   # Understood!│        │
-│  └────────────────────────────────────────────────────┘        │
-└─────────────────────────────────────────────────────────────────┘
-
-Issue #2: MISSING __main__ GUARDS (8 locations)
-┌─────────────────────────────────────────────────────────────────┐
-│  Current Problem:                                                │
-│  Line 379:  test_unit_measure_sparsity()      ← Runs on import! │
-│  Line 525:  test_unit_magnitude_prune()       ← Runs on import! │
-│  Line 684:  test_unit_structured_prune()      ← Runs on import! │
-│  Line 829:  test_unit_low_rank_approximate()  ← Runs on import! │
-│  Line 1064: test_unit_knowledge_distillation()← Runs on import! │
-│  Line 1227: test_unit_compress_model()        ← Runs on import! │
-│  Line 1317: demo_compression_with_profiler()  ← Runs on import! │
-│  Line 1377: analyze_compression_techniques()  ← Runs on import! │
-│  Line 1417: analyze_distillation_...()        ← Runs on import! │
-│                                                                  │
-│  Impact on Dependency Chain:                                     │
-│  ┌──────────┐     ┌──────────┐     ┌──────────┐                │
-│  │ Module   │────▶│ Module   │────▶│ Module   │                │
-│  │   17     │     │   18     │     │   19     │                │
-│  │(Compress)│     │(Accel.)  │     │(Bench.)  │                │
-│  └──────────┘     └──────────┘     └──────────┘                │
-│      │                │                 │                        │
-│      │ import         │ import          │                       │
-│      ▼                ▼                 ▼                        │
-│  Tests run!      Tests run!        Tests run!                   │
-│  (WRONG!)        (BREAKS!)         (BROKEN!)                    │
-│                                                                  │
-│  Solution: Add guard to EVERY test call                         │
-│  ┌──────────────────────────────────────────────────┐          │
-│  │ def test_unit_function():                        │          │
-│  │     # Test implementation                        │          │
-│  │     pass                                         │          │
-│  │                                                  │          │
-│  │ if __name__ == "__main__":  # ← ADD THIS        │          │
-│  │     test_unit_function()    # ← INDENT THIS     │          │
-│  └──────────────────────────────────────────────────┘          │
-└─────────────────────────────────────────────────────────────────┘
-
-Issue #3: INCOMPLETE NBGRADER METADATA (18+ cells)
-┌─────────────────────────────────────────────────────────────────┐
-│  Current Problem:                                                │
-│  Many cells missing complete metadata:                           │
-│  ✗ No schema_version                                            │
-│  ✗ Missing locked flags                                         │
-│  ✗ Inconsistent structure                                       │
-│                                                                  │
-│  Example of INCOMPLETE metadata:                                 │
-│  # %% nbgrader={"grade": false, "grade_id": "imports"}          │
-│                    ↑ Missing fields!                            │
-│                                                                  │
-│  Example of COMPLETE metadata:                                   │
-│  # %% nbgrader={                                                │
-│  #     "grade": false,                                          │
-│  #     "grade_id": "cell-imports",                              │
-│  #     "locked": false,                                         │
-│  #     "schema_version": 3,                                     │
-│  #     "solution": true,                                        │
-│  #     "task": false                                            │
-│  # }                                                            │
-│                                                                  │
-│  Impact: NBGrader validation fails, notebook conversion issues   │
-└─────────────────────────────────────────────────────────────────┘
-
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-FIX PRIORITY MAP:
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-Priority 1 (CRITICAL - Must Fix):
-┌────────────────────────────────────────────────────────┐
-│ 🔴 Sequential Class      → 1-2 hours  → BLOCKING      │
-│ 🔴 __main__ Guards       → 0.5 hours  → BLOCKING      │
-│ 🔴 NBGrader Metadata     → 0.5 hours  → BLOCKING      │
-└────────────────────────────────────────────────────────┘
-                         ▼
-              Total: 2-3 hours to unblock
-
-Priority 2 (HIGH - Strongly Recommended):
-┌────────────────────────────────────────────────────────┐
-│ 🟡 Sparse Storage Analysis    → 1 hour                │
-│ 🟡 Inference Timing Analysis  → 1 hour                │
-│ 🟡 Real vs Simulated Data     → 1 hour                │
-└────────────────────────────────────────────────────────┘
-                         ▼
-              Total: 3 hours for quality
-
-Priority 3 (MEDIUM - Nice to Have):
-┌────────────────────────────────────────────────────────┐
-│ 🟢 Cross-reference Review     → 0.5 hours             │
-│ 🟢 Academic Citations         → 0.5 hours             │
-│ 🟢 Final Polish              → 0.5 hours             │
-└────────────────────────────────────────────────────────┘
-                         ▼
-              Total: 1.5 hours for polish
-
-TOTAL ESTIMATED TIME: 6.5-7.5 hours for full compliance
-
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-TESTING VALIDATION FLOW:
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-After applying fixes, validate with:
-
-Step 1: Direct Execution
-┌────────────────────────────────────────────────────────┐
-│ $ python compression_dev.py                            │
-│ 🔬 Running unit tests...                               │
-│ ✅ All tests should pass                               │
-│ ✅ Tests should print output                           │
-└────────────────────────────────────────────────────────┘
-
-Step 2: Import Test (CRITICAL)
-┌────────────────────────────────────────────────────────┐
-│ $ python -c "from compression_dev import measure_..."  │
-│ ✅ Should import cleanly                               │
-│ ✅ Should NOT print test output                        │
-│ ✅ No errors                                           │
-└────────────────────────────────────────────────────────┘
-
-Step 3: Notebook Conversion
-┌────────────────────────────────────────────────────────┐
-│ $ jupytext --to notebook compression_dev.py            │
-│ ✅ Should convert without errors                       │
-│ ✅ All cells should have metadata                      │
-└────────────────────────────────────────────────────────┘
-
-Step 4: NBGrader Validation
-┌────────────────────────────────────────────────────────┐
-│ $ nbgrader validate compression_dev.ipynb              │
-│ ✅ Should pass validation                              │
-│ ✅ No metadata warnings                                │
-└────────────────────────────────────────────────────────┘
-
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-STRENGTHS TO PRESERVE:
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-✨ Outstanding Features (Keep These!):
-┌────────────────────────────────────────────────────────┐
-│ ✅ Clear educational progression                       │
-│ ✅ Excellent ASCII diagrams                            │
-│ ✅ Comprehensive docstrings                            │
-│ ✅ Real-world production context                       │
-│ ✅ Strong mathematical foundations                     │
-│ ✅ Good test coverage structure                        │
-│ ✅ Proper BEGIN/END SOLUTION blocks                    │
-│ ✅ Clear TODO/APPROACH/HINTS                           │
-└────────────────────────────────────────────────────────┘
-
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-FINAL STATUS:
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
-
-Current State:     🔴 NOT READY FOR EXPORT
-After Phase 1:     🟢 READY FOR EXPORT
-After Phase 2:     🟢 HIGH QUALITY
-After Phase 3:     🟢 PRODUCTION READY
-
-The module has excellent educational content and design.
-The issues are technical/architectural and can be systematically fixed.
-
-Recommendation: Implement Phase 1 (critical fixes) immediately.
-                Implement Phase 2 (high priority) before final release.
-                Implement Phase 3 (polish) as time permits.
-
-━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
diff --git a/modules/16_compression/REVIEW_SUMMARY.txt b/modules/16_compression/REVIEW_SUMMARY.txt
deleted file mode 100644
index bfa5eb42..00000000
--- a/modules/16_compression/REVIEW_SUMMARY.txt
+++ /dev/null
@@ -1,191 +0,0 @@
-================================================================================
-MODULE 17: COMPRESSION - REVIEW SUMMARY
-================================================================================
-
-Date: 2025-11-10
-Status: ⚠️ NEEDS FIXES BEFORE EXPORT
-Overall Score: 6.5/10
-
-================================================================================
-CRITICAL ISSUES (Must Fix)
-================================================================================
-
-1. SEQUENTIAL CLASS VIOLATION (Lines 72-91)
-   - Violates TinyTorch composition principle
-   - Modules should build ATOMIC COMPONENTS, not compositions
-   - Sequential hides layer interactions from students
-   - Action: Remove or move to milestone helpers
-
-2. MISSING __main__ GUARDS (8 locations)
-   - Tests run on module import (breaks dependency chain)
-   - Affects lines: 379, 525, 684, 829, 1064, 1227, 1317, 1377, 1417
-   - Action: Wrap all test calls in if __name__ == "__main__":
-
-3. INCOMPLETE NBGRADER METADATA (18+ cells)
-   - Missing schema_version, locked flags
-   - Inconsistent metadata structure
-   - Action: Add complete metadata to all cells
-
-================================================================================
-POSITIVE HIGHLIGHTS
-================================================================================
-
-✅ Excellent educational content and clear explanations
-✅ Outstanding ASCII diagrams for visualization
-✅ Comprehensive test coverage (unit + integration)
-✅ Strong production context throughout
-✅ Proper docstrings with TODO/APPROACH/HINTS
-✅ Good mathematical foundations
-✅ Real-world deployment scenarios
-
-================================================================================
-COMPLIANCE SCORES
-================================================================================
-
-NBGrader Structure:        5/10  ⚠️  (metadata incomplete)
-Educational Content:       9/10  ✅  (excellent)
-Docstrings:               9/10  ✅  (comprehensive)
-Imports/Module Structure:  4/10  ❌  (Sequential violation)
-Memory Profiling:         7/10  ⚠️  (good, could be better)
-Performance Benchmarking:  7/10  ⚠️  (present, needs more)
-ML Systems Analysis:      7/10  ⚠️  (good foundation)
-Test Coverage:            8/10  ✅  (comprehensive but guards missing)
-Production Context:       9/10  ✅  (excellent)
-
-================================================================================
-DETAILED FINDINGS
-================================================================================
-
-1. NBGrader Cell Structure: ⚠️ ISSUES
-   - Has Jupytext headers ✅
-   - BEGIN/END SOLUTION blocks present ✅
-   - Cell metadata incomplete ❌
-   - Test cells not properly locked ❌
-
-2. Educational Content: ✅ EXCELLENT
-   - Clear progression from basics to advanced
-   - Strong mathematical explanations
-   - Excellent ASCII diagrams
-   - Good real-world examples
-
-3. Docstrings: ✅ EXCELLENT
-   - All functions have comprehensive docs
-   - TODO/APPROACH/HINTS structure followed
-   - Clear examples provided
-   - Good hint quality
-
-4. Module Structure: ❌ CRITICAL VIOLATION
-   - Sequential class violates composition rules
-   - Otherwise well-organized
-   - Clear section structure
-
-5. Memory Profiling: ⚠️ GOOD
-   - Has profiler integration
-   - Shows parameter reduction
-   - Missing sparse storage analysis
-   - Could add more memory measurements
-
-6. Performance Benchmarking: ⚠️ GOOD
-   - Compression technique comparison present
-   - Missing inference timing analysis
-   - Needs real vs simulated data comparison
-
-7. ML Systems Analysis: ⚠️ GOOD
-   - Good compression trade-off discussion
-   - Production scenarios well-explained
-   - Could add more measurements
-   - Hardware implications discussed
-
-8. Test Coverage: ✅ EXCELLENT (but needs guards)
-   - Unit tests for all functions
-   - Comprehensive integration test
-   - Clear assertions
-   - Missing __main__ guards on calls
-
-9. Production Context: ✅ EXCELLENT
-   - Real deployment scenarios
-   - Hardware considerations
-   - Industry-standard techniques
-   - Clear use cases
-
-================================================================================
-FILES CREATED
-================================================================================
-
-1. REVIEW_REPORT.md
-   - Comprehensive 200+ line analysis
-   - Detailed issue breakdown
-   - Priority levels assigned
-   - Action plan included
-
-2. FIXES_REQUIRED.md
-   - Step-by-step fix instructions
-   - Code examples for all changes
-   - Complete checklist
-   - Testing procedures
-
-3. REVIEW_SUMMARY.txt (this file)
-   - Executive summary
-   - Quick reference scores
-   - Key action items
-
-================================================================================
-RECOMMENDED ACTION PLAN
-================================================================================
-
-PHASE 1: Critical Fixes (Required) - 2-3 hours
-  [ ] Remove Sequential class or move to helper
-  [ ] Add __main__ guards to all 8 test calls
-  [ ] Complete NBGrader metadata on all cells
-  [ ] Test import behavior
-
-PHASE 2: High Priority (Strongly Recommended) - 2-3 hours
-  [ ] Add sparse storage format analysis
-  [ ] Add inference timing measurements
-  [ ] Replace simulated with real data
-
-PHASE 3: Polish (Nice to Have) - 1 hour
-  [ ] Review cross-references
-  [ ] Add academic paper citations
-  [ ] Final consistency check
-
-Total Time: 5-7 hours for full compliance
-
-================================================================================
-IMMEDIATE NEXT STEPS
-================================================================================
-
-1. Review REVIEW_REPORT.md for detailed analysis
-2. Read FIXES_REQUIRED.md for specific code changes
-3. Implement Critical Fixes (Phase 1)
-4. Test with: python compression_dev.py
-5. Validate import: python -c "from compression_dev import measure_sparsity"
-6. Convert to notebook and validate NBGrader metadata
-7. Re-run this review after fixes
-
-================================================================================
-VERDICT
-================================================================================
-
-Current: NOT READY FOR EXPORT ❌
-After Critical Fixes: READY FOR EXPORT ✅
-
-This is a high-quality educational module with excellent content and
-pedagogy. The critical issues are architectural/technical and can be
-fixed systematically. Once the Sequential class violation and __main__
-guards are addressed, this module will be an excellent addition to
-TinyTorch.
-
-================================================================================
-CONTACT
-================================================================================
-
-Questions about this review:
-- See REVIEW_REPORT.md for comprehensive details
-- See FIXES_REQUIRED.md for implementation guidance
-- Consult TinyTorch standards document for reference
-
-Review completed: 2025-11-10
-Reviewer: TinyTorch Quality Assurance
-Module: 17_compression/compression_dev.py (1720 lines)
-================================================================================
diff --git a/modules/20_competition_ARCHIVED/competition.py b/modules/20_competition_ARCHIVED/competition.py
deleted file mode 100644
index 5625921f..00000000
--- a/modules/20_competition_ARCHIVED/competition.py
+++ /dev/null
@@ -1,891 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.17.1
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-# ---
-
-#| default_exp competition.submit
-
-# %% [markdown]
-"""
-# Module 20: TinyMLPerf Competition - Your Capstone Challenge
-
-Welcome to the capstone! You've built an entire ML system (M01-13) and learned optimization techniques (M14-19). Now compete in **TinyMLPerf** - a competition inspired by industry-standard MLPerf benchmarking!
-
-## 🔗 Prerequisites & Progress
-**You've Built**: Complete ML framework with all optimization techniques
-**You've Learned**: MLPerf principles and benchmarking methodology (Module 19)
-**You'll Do**: Compete in TinyMLPerf following Closed Division rules
-**You'll Produce**: Standardized TinyMLPerf submission
-
-**The Journey So Far**:
-```
-Modules 01-13: Build ML System (tensors → transformers)
-Modules 14-18: Learn Optimization Techniques  
-Module 19:     Learn MLPerf-Style Benchmarking
-Module 20:     Compete in TinyMLPerf! 🏅
-```
-
-## 🏅 TinyMLPerf: MLPerf for Educational Systems
-
-TinyMLPerf follows MLPerf principles adapted for educational ML systems:
-
-**Closed Division Rules (What You'll Do):**
-- ✅ Use provided baseline models (fair comparison)
-- ✅ Use provided test datasets (standardized evaluation)
-- ✅ Apply optimization techniques from Modules 14-18
-- ✅ Report all metrics (accuracy, latency, memory)
-- ✅ Document your optimization strategy
-
-**Why Closed Division?**
-- Fair apples-to-apples comparison
-- Tests your optimization skills (not model design)
-- Mirrors real-world MLPerf Inference competitions
-- Professionally credible methodology
-
-**Competition Categories:**
-- 🏃 Latency Sprint: Minimize inference time
-- 🏋️ Memory Challenge: Minimize model footprint
-- 🎯 Accuracy Contest: Maximize accuracy within constraints
-- 🏋️‍♂️ All-Around: Best balanced performance
-- 🚀 Extreme Push: Most aggressive optimization
-
-This module provides:
-1. **Validation**: Verify your TinyTorch installation
-2. **Baseline**: Official reference performance
-3. **Worked Example**: Complete optimization workflow
-4. **Competition Template**: Your submission workspace
-
-🔥 Let's compete following professional MLPerf methodology! 🏅
-"""
-
-# %% [markdown]
-"""
-## 📦 Where This Code Lives in the Final Package
-
-**Learning Side:** You work in `modules/20_competition/competition_dev.py`  
-**Building Side:** Code exports to `tinytorch.competition.submit`
-
-```python
-# Validation and baseline tools:
-from tinytorch.competition.submit import validate_installation, generate_baseline
-
-# Competition helpers:
-from tinytorch.competition.submit import load_baseline_model, generate_submission
-```
-
-**Why this matters:**
-- **Validation:** Ensures your TinyTorch installation works correctly
-- **Baseline:** Establishes reference performance for fair comparison
-- **Competition:** Provides standardized framework for submissions
-- **Integration:** Brings together all 19 modules into one complete workflow
-"""
-
-# %% [markdown]
-"""
-# 1. TinyMLPerf Rules & System Validation
-
-Before competing, let's understand TinyMLPerf rules and validate your environment. Following MLPerf methodology (learned in Module 19) ensures fair competition and reproducible results.
-
-## TinyMLPerf Closed Division Rules
-
-**You learned in Module 19 that MLPerf Closed Division requires:**
-1. **Fixed Models**: Use provided baseline architectures
-2. **Fixed Datasets**: Use provided test data
-3. **Fair Comparison**: Same starting point for everyone
-4. **Reproducibility**: Document all optimizations
-5. **Multiple Metrics**: Report accuracy, latency, memory
-
-**In TinyMLPerf Closed Division, you CAN:**
-- ✅ Apply quantization (Module 17)
-- ✅ Apply pruning/compression (Module 18)
-- ✅ Enable KV caching for transformers (Module 14)
-- ✅ Combine techniques in any order
-- ✅ Tune hyperparameters
-
-**In TinyMLPerf Closed Division, you CANNOT:**
-- ❌ Change baseline model architecture
-- ❌ Train on different data
-- ❌ Use external pretrained weights
-- ❌ Modify test dataset
-
-**Why these rules?**
-- Tests your OPTIMIZATION skills (not model design)
-- Fair apples-to-apples comparison
-- Mirrors professional MLPerf competitions
-- Results are meaningful and reproducible
-
-## System Validation
-
-Let's verify your TinyTorch installation works correctly before competing. MLPerf requires documenting your environment, so validation ensures reproducibility.
-
-**Validation checks:**
-- ✅ All 19 modules imported successfully
-- ✅ Core operations work (tensor, autograd, layers)
-- ✅ Optimization techniques available (M14-18)
-- ✅ Benchmarking tools functional (M19)
-"""
-
-# %%
-#| export
-import numpy as np
-import json
-import time
-from pathlib import Path
-from typing import Dict, List, Tuple, Any, Optional
-
-def validate_installation() -> Dict[str, bool]:
-    """
-    Validate TinyTorch installation and return status of each component.
-    
-    Returns:
-        Dictionary mapping module names to validation status (True = working)
-    
-    Example:
-        >>> status = validate_installation()
-        >>> print(status)
-        {'tensor': True, 'autograd': True, 'layers': True, ...}
-    """
-    validation_results = {}
-    
-    print("🔧 Validating TinyTorch Installation...")
-    print("=" * 60)
-    
-    # Core modules (M01-13)
-    core_modules = [
-        ("tensor", "tinytorch.core.tensor", "Tensor"),
-        ("autograd", "tinytorch.core.autograd", "enable_autograd"),
-        ("layers", "tinytorch.core.layers", "Linear"),
-        ("activations", "tinytorch.core.activations", "ReLU"),
-        ("losses", "tinytorch.core.training", "MSELoss"),
-        ("optimizers", "tinytorch.core.optimizers", "SGD"),
-        ("spatial", "tinytorch.core.spatial", "Conv2d"),
-        ("attention", "tinytorch.core.attention", "MultiHeadAttention"),
-        ("transformers", "tinytorch.models.transformer", "GPT"),
-    ]
-    
-    for name, module_path, class_name in core_modules:
-        try:
-            exec(f"from {module_path} import {class_name}")
-            validation_results[name] = True
-            print(f"✅ {name.capitalize()}: Working")
-        except Exception as e:
-            validation_results[name] = False
-            print(f"❌ {name.capitalize()}: Failed - {str(e)}")
-    
-    # Optimization modules (M14-18)
-    opt_modules = [
-        ("kv_caching", "tinytorch.generation.kv_cache", "enable_kv_cache"),
-        ("profiling", "tinytorch.profiling.profiler", "Profiler"),
-        ("quantization", "tinytorch.optimization.quantization", "quantize_model"),
-        ("compression", "tinytorch.optimization.compression", "magnitude_prune"),
-    ]
-    
-    for name, module_path, func_name in opt_modules:
-        try:
-            exec(f"from {module_path} import {func_name}")
-            validation_results[name] = True
-            print(f"✅ {name.replace('_', ' ').capitalize()}: Working")
-        except Exception as e:
-            validation_results[name] = False
-            print(f"❌ {name.replace('_', ' ').capitalize()}: Failed - {str(e)}")
-    
-    # Benchmarking (M19)
-    try:
-        from tinytorch.benchmarking.benchmark import Benchmark, OlympicEvent
-        validation_results["benchmarking"] = True
-        print(f"✅ Benchmarking: Working")
-    except Exception as e:
-        validation_results["benchmarking"] = False
-        print(f"❌ Benchmarking: Failed - {str(e)}")
-    
-    print("=" * 60)
-    
-    # Summary
-    total = len(validation_results)
-    working = sum(validation_results.values())
-    
-    if working == total:
-        print(f"🎉 Perfect! All {total}/{total} modules working!")
-        print("✅ You're ready to compete in TorchPerf Olympics!")
-    else:
-        print(f"⚠️  {working}/{total} modules working")
-        print(f"❌ {total - working} modules need attention")
-        print("\nPlease run: pip install -e . (in TinyTorch root)")
-    
-    return validation_results
-
-# %% [markdown]
-"""
-# 2. TinyMLPerf Baseline - Official Reference Performance
-
-Following MLPerf Closed Division rules, everyone starts with the SAME baseline model. This ensures fair comparison - we're measuring your optimization skills, not model design.
-
-## What is a TinyMLPerf Baseline?
-
-In MLPerf competitions, the baseline is the official reference implementation:
-- **Fixed Architecture:** Provided CNN (everyone uses the same)
-- **Fixed Dataset:** CIFAR-10 test set (standardized evaluation)
-- **Measured Metrics:** Accuracy, latency, memory (reproducible)
-- **Your Goal:** Beat baseline using optimization techniques from M14-18
-
-**This is MLPerf Closed Division:**
-- Everyone starts here ← Fair comparison
-- Apply YOUR optimizations ← Your skill
-- Measure improvement ← Objective scoring
-
-We provide a simple CNN on CIFAR-10 as the TinyMLPerf baseline. This gives everyone the same starting point.
-
-### Baseline Components
-
-1. **Model:** Standard CNN (no optimizations)
-2. **Metrics:** Accuracy, latency, memory, parameters
-3. **Test Data:** CIFAR-10 test set (standardized)
-4. **Hardware:** Your local machine (reported for reproducibility)
-
-The baseline establishes what "unoptimized" looks like. Your job: beat it!
-"""
-
-# %%
-#| export
-def load_baseline_model(model_name: str = "cifar10_cnn"):
-    """
-    Load a baseline model for TorchPerf Olympics competition.
-    
-    Args:
-        model_name: Name of baseline model to load
-            - "cifar10_cnn": Simple CNN for CIFAR-10 classification
-    
-    Returns:
-        Baseline model instance
-    
-    Example:
-        >>> model = load_baseline_model("cifar10_cnn")
-        >>> print(f"Parameters: {sum(p.size for p in model.parameters())}")
-    """
-    from tinytorch.core.layers import Linear
-    from tinytorch.core.spatial import Conv2d, MaxPool2d
-    from tinytorch.core.activations import ReLU
-    from tinytorch.core.tensor import Tensor
-    
-    # Flatten is not a separate class - it's just a reshape operation
-    class Flatten:
-        def forward(self, x):
-            batch_size = x.shape[0]
-            return Tensor(x.data.reshape(batch_size, -1))
-    
-    if model_name == "cifar10_cnn":
-        # Simple CNN: Conv -> Pool -> Conv -> Pool -> FC -> FC
-        class BaselineCNN:
-            def __init__(self):
-                self.name = "Baseline_CIFAR10_CNN"
-                
-                # Convolutional layers
-                self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
-                self.relu1 = ReLU()
-                self.pool1 = MaxPool2d(kernel_size=2, stride=2)
-                
-                self.conv2 = Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
-                self.relu2 = ReLU()
-                self.pool2 = MaxPool2d(kernel_size=2, stride=2)
-                
-                # Fully connected layers
-                self.flatten = Flatten()
-                self.fc1 = Linear(64 * 8 * 8, 128)
-                self.relu3 = ReLU()
-                self.fc2 = Linear(128, 10)  # 10 classes for CIFAR-10
-            
-            def forward(self, x):
-                # Forward pass
-                x = self.conv1.forward(x)
-                x = self.relu1.forward(x)
-                x = self.pool1.forward(x)
-                
-                x = self.conv2.forward(x)
-                x = self.relu2.forward(x)
-                x = self.pool2.forward(x)
-                
-                x = self.flatten.forward(x)
-                x = self.fc1.forward(x)
-                x = self.relu3.forward(x)
-                x = self.fc2.forward(x)
-                
-                return x
-            
-            def __call__(self, x):
-                return self.forward(x)
-        
-        return BaselineCNN()
-    else:
-        raise ValueError(f"Unknown baseline model: {model_name}")
-
-def generate_baseline(model_name: str = "cifar10_cnn", quick: bool = True) -> Dict[str, Any]:
-    """
-    Generate baseline performance metrics for a model.
-    
-    Args:
-        model_name: Name of baseline model
-        quick: If True, use quick estimates instead of full benchmarks
-    
-    Returns:
-        Baseline scorecard with metrics
-    
-    Example:
-        >>> baseline = generate_baseline("cifar10_cnn", quick=True)
-        >>> print(f"Baseline latency: {baseline['latency_ms']}ms")
-    """
-    print("📊 Generating Baseline Scorecard...")
-    print("=" * 60)
-    
-    # Load model
-    model = load_baseline_model(model_name)
-    print(f"✅ Loaded baseline model: {model.name}")
-    
-    # Count parameters using the standard .parameters() API from Module 03
-    def count_parameters(model):
-        """
-        Count total parameters in a model.
-
-        Uses the explicit .parameters() API from Module 03 instead of hasattr()
-        to count model parameters. This is cleaner and follows TinyTorch conventions.
-
-        Note: Previously used hasattr(attr, 'weights') which was incorrect -
-        TinyTorch uses .weight (singular) not .weights (plural).
-        """
-        total = 0
-        # Trust that model has .parameters() method (from Module 03)
-        try:
-            for param in model.parameters():
-                # Each param is a Tensor from Module 01 with .data attribute
-                total += param.data.size
-        except (AttributeError, TypeError):
-            # Fallback: model might not have parameters() method
-            # This shouldn't happen in TinyTorch, but handle gracefully
-            pass
-        return total
-    
-    params = count_parameters(model)
-    memory_mb = params * 4 / (1024 * 1024)  # Assuming float32
-    
-    if quick:
-        # Quick estimates for fast validation
-        print("⚡ Using quick estimates (set quick=False for full benchmark)")
-        
-        baseline = {
-            "model": model_name,
-            "accuracy": 85.0,  # Typical for this architecture
-            "latency_ms": 45.2,
-            "memory_mb": memory_mb,
-            "parameters": params,
-            "mode": "quick_estimate"
-        }
-    else:
-        # Full benchmark (requires more time)
-        from tinytorch.benchmarking.benchmark import Benchmark
-        
-        print("🔬 Running full benchmark (this may take a minute)...")
-        
-        benchmark = Benchmark([model], [{"name": "baseline"}], 
-                            warmup_runs=5, measurement_runs=20)
-        
-        # Measure latency
-        input_shape = (1, 3, 32, 32)  # CIFAR-10 input
-        latency_results = benchmark.run_latency_benchmark(input_shape=input_shape)
-        latency_ms = list(latency_results.values())[0].mean * 1000
-        
-        baseline = {
-            "model": model_name,
-            "accuracy": 85.0,  # Would need actual test set evaluation
-            "latency_ms": latency_ms,
-            "memory_mb": memory_mb,
-            "parameters": params,
-            "mode": "full_benchmark"
-        }
-    
-    # Display baseline
-    print("\n📋 BASELINE SCORECARD")
-    print("=" * 60)
-    print(f"Model:          {baseline['model']}")
-    print(f"Accuracy:       {baseline['accuracy']:.1f}%")
-    print(f"Latency:        {baseline['latency_ms']:.1f}ms")
-    print(f"Memory:         {baseline['memory_mb']:.2f}MB")
-    print(f"Parameters:     {baseline['parameters']:,}")
-    print("=" * 60)
-    print("📌 This is your starting point. Optimize to compete!")
-    print()
-    
-    return baseline
-
-# %% [markdown]
-"""
-# 3. TinyMLPerf Closed Division Workflow - Complete Example
-
-Let's see a complete TinyMLPerf submission following Closed Division rules. This example demonstrates the professional MLPerf methodology you learned in Module 19.
-
-**TinyMLPerf Closed Division Workflow:**
-1. **Load Official Baseline** (MLPerf requirement)
-2. **Apply Optimizations** (Modules 14-18 techniques)
-3. **Benchmark Systematically** (Module 19 tools)
-4. **Generate Submission** (MLPerf-compliant format)
-5. **Document Strategy** (Reproducibility requirement)
-
-This is your template - study it, then implement your own optimization strategy!
-
-## Example Strategy: All-Around Category
-
-For this worked example, we'll compete in the **All-Around** category (best balanced performance across all metrics).
-
-**Our Optimization Strategy:**
-- **Step 1:** Quantization (INT8) → 4x memory reduction
-- **Step 2:** Magnitude Pruning (60%) → Faster inference
-- **Step 3:** Systematic Benchmarking → Measure impact
-
-**Why this order?**
-- Quantize FIRST: Preserves more accuracy than pruning first
-- Prune SECOND: Reduces what needs to be quantized
-- Benchmark: Following MLPerf measurement methodology
-
-**This follows MLPerf Closed Division rules:**
-- ✅ Uses provided baseline CNN
-- ✅ Applies optimization techniques (not architecture changes)
-- ✅ Documents strategy clearly
-- ✅ Reports all required metrics
-"""
-
-# %%
-#| export
-def worked_example_optimization():
-    """
-    Complete worked example showing full optimization workflow.
-    
-    This demonstrates:
-    - Loading baseline model
-    - Applying multiple optimization techniques
-    - Benchmarking systematically
-    - Generating submission
-    
-    Students should study this and adapt for their own strategies!
-    """
-    print("🏅 WORKED EXAMPLE: Complete Optimization Workflow")
-    print("=" * 70)
-    print("Target: All-Around Event (balanced performance)")
-    print("Strategy: Quantization (INT8) → Pruning (60%)")
-    print("=" * 70)
-    print()
-    
-    # Step 1: Load Baseline
-    print("📦 Step 1: Load Baseline Model")
-    print("-" * 70)
-    baseline = load_baseline_model("cifar10_cnn")
-    baseline_metrics = generate_baseline("cifar10_cnn", quick=True)
-    print()
-    
-    # Step 2: Apply Quantization
-    print("🔧 Step 2: Apply INT8 Quantization (Module 17)")
-    print("-" * 70)
-    print("💡 Why quantize? Reduces memory 4x (FP32 → INT8)")
-    
-    # For demonstration, we'll simulate quantization
-    # In real competition, students would use:
-    # from tinytorch.optimization.quantization import quantize_model
-    # optimized = quantize_model(baseline, bits=8)
-    
-    print("✅ Quantized model (simulated)")
-    print("   - Memory: 12.4MB → 3.1MB (4x reduction)")
-    print()
-    
-    # Step 3: Apply Pruning
-    print("✂️  Step 3: Apply Magnitude Pruning (Module 18)")
-    print("-" * 70)
-    print("💡 Why prune? Removes 60% of weights for faster inference")
-    
-    # For demonstration, we'll simulate pruning
-    # In real competition, students would use:
-    # from tinytorch.optimization.compression import magnitude_prune
-    # optimized = magnitude_prune(optimized, sparsity=0.6)
-    
-    print("✅ Pruned model (simulated)")
-    print("   - Active parameters: 3.2M → 1.28M (60% removed)")
-    print()
-    
-    # Step 4: Benchmark Results
-    print("📊 Step 4: Benchmark Optimized Model (Module 19)")
-    print("-" * 70)
-    
-    # Simulated optimized metrics
-    optimized_metrics = {
-        "model": "Optimized_CIFAR10_CNN",
-        "accuracy": 83.5,  # Slight drop from aggressive optimization
-        "latency_ms": 22.1,
-        "memory_mb": 1.24,  # 4x quantization + 60% pruning
-        "parameters": 1280000,
-        "techniques": ["quantization_int8", "magnitude_prune_0.6"]
-    }
-    
-    print("Baseline vs Optimized:")
-    print(f"  Accuracy:    {baseline_metrics['accuracy']:.1f}% → {optimized_metrics['accuracy']:.1f}% (-1.5pp)")
-    print(f"  Latency:     {baseline_metrics['latency_ms']:.1f}ms → {optimized_metrics['latency_ms']:.1f}ms (2.0x faster ✅)")
-    print(f"  Memory:      {baseline_metrics['memory_mb']:.2f}MB → {optimized_metrics['memory_mb']:.2f}MB (10.0x smaller ✅)")
-    print(f"  Parameters:  {baseline_metrics['parameters']:,} → {optimized_metrics['parameters']:,} (60% fewer ✅)")
-    print()
-    
-    # Step 5: Generate Submission
-    print("📤 Step 5: Generate Competition Submission")
-    print("-" * 70)
-    
-    submission = {
-        "event": "all_around",
-        "athlete_name": "Example_Submission",
-        "baseline": baseline_metrics,
-        "optimized": optimized_metrics,
-        "improvements": {
-            "accuracy_drop": -1.5,
-            "latency_speedup": 2.0,
-            "memory_reduction": 10.0
-        },
-        "techniques_applied": ["quantization_int8", "magnitude_prune_0.6"],
-        "technique_order": "quantize_first_then_prune"
-    }
-    
-    print("✅ Submission generated!")
-    print(f"   Event: {submission['event']}")
-    print(f"   Techniques: {', '.join(submission['techniques_applied'])}")
-    print()
-    print("=" * 70)
-    print("🎯 This is the complete workflow!")
-    print("   Now it's your turn to implement your own optimization strategy.")
-    print("=" * 70)
-    
-    return submission
-
-# %% [markdown]
-"""
-# 4. Your TinyMLPerf Submission Template
-
-Now it's your turn! Below is your TinyMLPerf Closed Division submission template. Following MLPerf methodology ensures your results are reproducible and fairly comparable.
-
-## TinyMLPerf Closed Division Submission Process
-
-**Step 1: Choose Your Category**
-Pick ONE category to optimize for:
-- 🏃 **Latency Sprint:** Minimize inference time
-- 🏋️ **Memory Challenge:** Minimize model footprint
-- 🎯 **Accuracy Contest:** Maximize accuracy within constraints
-- 🏋️‍♂️ **All-Around:** Best balanced performance
-- 🚀 **Extreme Push:** Most aggressive optimization
-
-**Step 2: Design Your Optimization Strategy**
-- Review Module 19, Section 4.5 for combination strategies
-- Consider optimization order (quantize→prune vs prune→quantize)
-- Plan ablation study to understand each technique's impact
-- Document your reasoning (MLPerf reproducibility requirement)
-
-**Step 3: Implement in Template**
-- Write optimization code in `optimize_for_competition()`
-- Apply techniques from Modules 14-18
-- Follow TinyMLPerf Closed Division rules (no architecture changes!)
-
-**Step 4: Benchmark Systematically**
-- Use Module 19 benchmarking tools
-- Measure all required metrics (accuracy, latency, memory)
-- Run multiple times for statistical validity (MLPerf requirement)
-
-**Step 5: Generate MLPerf-Compliant Submission**
-- Run `generate_submission()` to create `submission.json`
-- Includes baseline comparison (MLPerf requirement)
-- Documents optimization strategy (reproducibility)
-- Ready for TinyMLPerf leaderboard upload
-
-## Submission Guidelines (MLPerf Inspired)
-
-- ✅ **Start with baseline:** Load provided CNN (don't modify architecture)
-- ✅ **Apply optimizations:** Use M14-18 techniques only
-- ✅ **Measure fairly:** Same hardware, same test data
-- ✅ **Document everything:** Strategy writeup required
-- ✅ **Report all metrics:** Accuracy, latency, memory (not just best one!)
-
-**Remember:** TinyMLPerf Closed Division tests your OPTIMIZATION skills, not model design. Work within the rules! 🏅
-"""
-
-# %%
-#| export
-def optimize_for_competition(baseline_model, event: str = "all_around"):
-    """
-    🏅 YOUR COMPETITION ENTRY - IMPLEMENT YOUR STRATEGY HERE!
-    
-    This is where you apply optimization techniques from Modules 14-18.
-    
-    Available techniques:
-    - Module 14: KV Caching (for transformers) - enable_kv_cache()
-    - Module 16: Acceleration (vectorization, fusion)
-    - Module 17: Quantization (INT8, INT4) - quantize_model()
-    - Module 18: Compression (pruning) - magnitude_prune()
-    
-    Args:
-        baseline_model: The unoptimized model
-        event: Which Olympic event you're competing in
-            - "latency_sprint": Minimize latency
-            - "memory_challenge": Minimize memory
-            - "accuracy_contest": Maximize accuracy
-            - "all_around": Best balance
-            - "extreme_push": Most aggressive
-    
-    Returns:
-        Your optimized model
-    
-    Example:
-        from tinytorch.optimization.quantization import quantize_model
-        from tinytorch.optimization.compression import magnitude_prune
-        
-        optimized = baseline_model
-        optimized = quantize_model(optimized, bits=8)
-        optimized = magnitude_prune(optimized, sparsity=0.7)
-        return optimized
-    """
-    
-    print(f"🏅 YOUR OPTIMIZATION STRATEGY FOR: {event}")
-    print("=" * 70)
-    
-    # Start with baseline
-    optimized_model = baseline_model
-    
-    # ============================================================
-    # YOUR CODE BELOW - Apply optimization techniques here!
-    # ============================================================
-    
-    # TODO: Students implement their optimization strategy
-    #
-    # Example strategies by event:
-    #
-    # Latency Sprint (speed priority):
-    #   - Heavy quantization (INT4 or INT8)
-    #   - Aggressive pruning (80-90%)
-    #   - Kernel fusion if applicable
-    #
-    # Memory Challenge (size priority):
-    #   - INT8 or INT4 quantization
-    #   - Aggressive pruning (70-90%)
-    #   - Compression techniques
-    #
-    # All-Around (balanced):
-    #   - INT8 quantization
-    #   - Moderate pruning (50-70%)
-    #   - Selective optimization
-    #
-    # Your strategy:
-    
-    
-    
-    # ============================================================
-    # YOUR CODE ABOVE
-    # ============================================================
-    
-    print("✅ Optimization complete!")
-    print("💡 Tip: Benchmark your result to see the impact!")
-    
-    return optimized_model
-
-def generate_submission(baseline_model, optimized_model, 
-                       event: str = "all_around",
-                       athlete_name: str = "YourName",
-                       techniques: List[str] = None) -> Dict[str, Any]:
-    """
-    Generate standardized competition submission.
-    
-    Args:
-        baseline_model: Original unoptimized model
-        optimized_model: Your optimized model
-        event: Olympic event name
-        athlete_name: Your name for leaderboard
-        techniques: List of techniques applied
-    
-    Returns:
-        Submission dictionary (will be saved as JSON)
-    """
-    print("📤 Generating Competition Submission...")
-    print("=" * 70)
-    
-    # Get baseline metrics
-    baseline_metrics = generate_baseline(quick=True)
-    
-    # For demonstration, estimate optimized metrics
-    # In real competition, this would benchmark the actual optimized model
-    print("🔬 Benchmarking optimized model...")
-    
-    # Placeholder: Students' actual optimizations would be measured here
-    optimized_metrics = {
-        "model": "Your_Optimized_Model",
-        "accuracy": 84.0,  # Measured
-        "latency_ms": 28.0,  # Measured
-        "memory_mb": 4.0,  # Measured
-        "parameters": 2000000,  # Measured
-    }
-    
-    # Calculate improvements
-    improvements = {
-        "accuracy_change": optimized_metrics["accuracy"] - baseline_metrics["accuracy"],
-        "latency_speedup": baseline_metrics["latency_ms"] / optimized_metrics["latency_ms"],
-        "memory_reduction": baseline_metrics["memory_mb"] / optimized_metrics["memory_mb"],
-    }
-    
-    # Create submission
-    submission = {
-        "event": event,
-        "athlete_name": athlete_name,
-        "baseline": baseline_metrics,
-        "optimized": optimized_metrics,
-        "improvements": improvements,
-        "techniques_applied": techniques or ["TODO: List your techniques"],
-        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
-    }
-    
-    # Save to JSON
-    output_file = Path("submission.json")
-    with open(output_file, "w") as f:
-        json.dump(submission, f, indent=2)
-    
-    print(f"✅ Submission saved to: {output_file}")
-    print()
-    print("📊 Your Results:")
-    print(f"  Event:           {event}")
-    print(f"  Accuracy:        {optimized_metrics['accuracy']:.1f}% (Δ {improvements['accuracy_change']:+.1f}pp)")
-    print(f"  Latency:         {optimized_metrics['latency_ms']:.1f}ms ({improvements['latency_speedup']:.2f}x faster)")
-    print(f"  Memory:          {optimized_metrics['memory_mb']:.2f}MB ({improvements['memory_reduction']:.2f}x smaller)")
-    print()
-    print("📤 Upload submission.json to TorchPerf Olympics platform!")
-    print("=" * 70)
-    
-    return submission
-
-# %% [markdown]
-"""
-# 5. Module Integration Test
-
-Complete validation and competition workflow test.
-"""
-
-# %% nbgrader={"grade": true, "grade_id": "test-module", "locked": true, "points": 10}
-def test_module():
-    """
-    Complete test of Module 20 functionality.
-    
-    This validates:
-    - Installation validation works
-    - Baseline generation works
-    - Worked example runs successfully
-    - Competition template is ready
-    """
-    print("=" * 70)
-    print("MODULE 20 INTEGRATION TEST")
-    print("=" * 70)
-    print()
-    
-    # Test 1: Validation
-    print("🔧 Test 1: System Validation")
-    validation_status = validate_installation()
-    assert len(validation_status) > 0, "Validation should return status dict"
-    print("✅ Validation working!")
-    print()
-    
-    # Test 2: Baseline Generation
-    print("📊 Test 2: Baseline Generation")
-    baseline = generate_baseline(quick=True)
-    assert "accuracy" in baseline, "Baseline should include accuracy"
-    assert "latency_ms" in baseline, "Baseline should include latency"
-    assert "memory_mb" in baseline, "Baseline should include memory"
-    print("✅ Baseline generation working!")
-    print()
-    
-    # Test 3: Worked Example
-    print("🏅 Test 3: Worked Example")
-    example_submission = worked_example_optimization()
-    assert "event" in example_submission, "Submission should include event"
-    assert "baseline" in example_submission, "Submission should include baseline"
-    assert "optimized" in example_submission, "Submission should include optimized"
-    print("✅ Worked example working!")
-    print()
-    
-    # Test 4: Competition Template
-    print("🎯 Test 4: Competition Template")
-    baseline_model = load_baseline_model("cifar10_cnn")
-    optimized = optimize_for_competition(baseline_model, event="all_around")
-    assert optimized is not None, "Optimization should return model"
-    print("✅ Competition template working!")
-    print()
-    
-    print("=" * 70)
-    print("✅ ALL TESTS PASSED!")
-    print("=" * 70)
-    print()
-    print("🎉 You're ready for TorchPerf Olympics!")
-    print("   Next steps:")
-    print("   1. Implement your optimization strategy in optimize_for_competition()")
-    print("   2. Run this module to generate submission.json")
-    print("   3. Upload to competition platform")
-    print()
-    print("🔥 Good luck! May the best optimizer win! 🏅")
-
-test_module()
-
-# %% [markdown]
-"""
-## 🤔 ML Systems Thinking: Competition as Learning
-
-TorchPerf Olympics isn't just about winning - it's about understanding trade-offs:
-
-**The Meta-Lesson**: Every optimization involves trade-offs:
-- Quantization: Speed vs Accuracy
-- Pruning: Size vs Performance
-- Caching: Memory vs Speed
-
-Professional ML engineers navigate these trade-offs daily. The competition forces you to:
-1. **Think systematically** about optimization strategies
-2. **Measure rigorously** using benchmarking tools
-3. **Make data-driven decisions** based on actual measurements
-4. **Document and justify** your choices
-
-The best submission isn't always the "fastest" or "smallest" - it's the one that best understands and navigates the trade-off space for their chosen event.
-
-What will your strategy be? 🤔
-"""
-
-# %% [markdown]
-"""
-## 🎯 MODULE SUMMARY: Competition & Validation
-
-**What You've Learned:**
-- ✅ How to validate your TinyTorch installation
-- ✅ How to generate baseline performance metrics
-- ✅ How to combine optimization techniques systematically
-- ✅ How to benchmark and measure impact
-- ✅ How to generate standardized competition submissions
-
-**The Complete Workflow:**
-```
-1. Validate  → Ensure environment works
-2. Baseline  → Establish reference performance
-3. Optimize  → Apply techniques from M14-18
-4. Benchmark → Measure impact using M19
-5. Submit    → Generate standardized submission
-```
-
-**Key Takeaway**: Competition teaches systematic optimization thinking. The goal isn't just winning - it's understanding the entire optimization process from baseline to submission.
-
-**Next Steps:**
-1. Study the worked example
-2. Implement your own optimization strategy
-3. Benchmark your results
-4. Generate submission.json
-5. Compete in TorchPerf Olympics!
-
-🔥 Now go optimize and win gold! 🏅
-"""
-
diff --git a/modules/20_competition_ARCHIVED/competition_dev.py b/modules/20_competition_ARCHIVED/competition_dev.py
deleted file mode 100644
index d73e3c38..00000000
--- a/modules/20_competition_ARCHIVED/competition_dev.py
+++ /dev/null
@@ -1,977 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: percent
-#       format_version: '1.3'
-#       jupytext_version: 1.18.1
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-# ---
-
-# %%
-#| default_exp competition.submit
-
-# %% [markdown]
-"""
-# Module 20: TinyMLPerf Competition - Your Capstone Challenge
-
-Welcome to the capstone! You've built an entire ML system from scratch (M01-13) and learned optimization techniques (M14-19). Now it's time to compete and show what you can do! 🏅
-
-## 🔗 Your Journey
-```
-Modules 01-13: Build ML System (tensors → transformers)
-Modules 14-18: Learn Optimization Techniques  
-Module 19:     Learn Benchmarking
-Module 20:     Compete in TinyMLPerf! 🏅
-```
-
-## 🏅 TinyMLPerf: Two Ways to Compete
-
-Inspired by industry-standard MLPerf (which you learned about in Module 19), TinyMLPerf offers **two competition tracks**:
-
-### 🔒 Closed Division - "Optimization Challenge"
-**What you do:**
-- Start with provided baseline model (everyone gets the same)
-- Apply optimization techniques from Modules 14-18
-- Compete on: Who optimizes best?
-
-**Best for:** Most students - clear rules, fair comparison
-**Focus:** Your optimization skills
-
-### 🔓 Open Division - "Innovation Challenge"  
-**What you do:**
-- Modify anything! Improve your implementations from M01-19
-- Design better architectures
-- Novel approaches encouraged
-
-**Best for:** Advanced students who want more creative freedom
-**Focus:** Your systems innovations
-
-## Competition Categories (Both Divisions)
-- 🏃 **Latency Sprint**: Fastest inference
-- 🏋️ **Memory Challenge**: Smallest model
-- 🎯 **Accuracy Contest**: Best accuracy within constraints
-- 🏋️‍♂️ **All-Around**: Best balanced performance
-- 🚀 **Extreme Push**: Most aggressive optimization
-
-## What This Module Provides
-1. **Validation**: Check your TinyTorch works
-2. **Baseline**: Starting point for Closed Division
-3. **Examples**: See both tracks in action
-4. **Template**: Your competition workspace
-
-Pick your track, optimize, and compete! 🔥
-"""
-
-# %% [markdown]
-"""
-## 📦 Where This Code Lives in the Final Package
-
-**Learning Side:** You work in `modules/20_competition/competition_dev.py`  
-**Building Side:** Code exports to `tinytorch.competition.submit`
-
-```python
-# Validation and baseline tools:
-from tinytorch.competition.submit import validate_installation, generate_baseline
-
-# Competition helpers:
-from tinytorch.competition.submit import load_baseline_model, generate_submission
-```
-
-**Why this matters:**
-- **Validation:** Ensures your TinyTorch installation works correctly
-- **Baseline:** Establishes reference performance for fair comparison
-- **Competition:** Provides standardized framework for submissions
-- **Integration:** Brings together all 19 modules into one complete workflow
-"""
-
-# %% [markdown]
-"""
-# 1. Pick Your Track & Validate
-
-Before competing, choose your track and make sure your TinyTorch installation works!
-
-## Two Tracks, Two Styles
-
-### 🔒 Closed Division - "The Optimization Challenge"
-- Everyone starts with the same baseline model
-- Apply techniques from Modules 14-18 (quantization, pruning, etc.)
-- Fair comparison: who optimizes best?
-- **Choose this if:** You want clear rules and direct competition
-
-### 🔓 Open Division - "The Innovation Challenge"
-- Modify anything! Improve YOUR TinyTorch implementations
-- Better Conv2d? Faster matmul? Novel architecture? All allowed!
-- Compete on innovation and creativity
-- **Choose this if:** You want freedom to explore and innovate
-
-**Can I do both?** Absolutely! Submit to both tracks.
-
-**Which is "better"?** Neither - they test different skills:
-- Closed = Optimization mastery
-- Open = Systems innovation
-
-## Quick Validation
-
-Before competing, let's verify everything works:
-- ✅ All modules imported successfully
-- ✅ Optimization techniques available
-- ✅ Benchmarking tools ready
-"""
-
-# %%
-#| export
-import numpy as np
-import json
-import time
-from pathlib import Path
-from typing import Dict, List, Tuple, Any, Optional
-from tinytorch.benchmarking.benchmark import Benchmark, calculate_normalized_scores
-from tinytorch.profiling.profiler import Profiler
-
-def validate_installation() -> Dict[str, bool]:
-    """
-    Validate TinyTorch installation and return status of each component.
-    
-    Returns:
-        Dictionary mapping module names to validation status (True = working)
-    
-    Example:
-        >>> status = validate_installation()
-        >>> print(status)
-        {'tensor': True, 'autograd': True, 'layers': True, ...}
-    """
-    validation_results = {}
-    
-    print("🔧 Validating TinyTorch Installation...")
-    print("=" * 60)
-    
-    # Core modules (M01-13)
-    core_modules = [
-        ("tensor", "tinytorch.core.tensor", "Tensor"),
-        ("autograd", "tinytorch.core.autograd", "enable_autograd"),
-        ("layers", "tinytorch.core.layers", "Linear"),
-        ("activations", "tinytorch.core.activations", "ReLU"),
-        ("losses", "tinytorch.core.training", "MSELoss"),
-        ("optimizers", "tinytorch.core.optimizers", "SGD"),
-        ("spatial", "tinytorch.core.spatial", "Conv2d"),
-        ("attention", "tinytorch.core.attention", "MultiHeadAttention"),
-        ("transformers", "tinytorch.models.transformer", "GPT"),
-    ]
-    
-    for name, module_path, class_name in core_modules:
-        try:
-            exec(f"from {module_path} import {class_name}")
-            validation_results[name] = True
-            print(f"✅ {name.capitalize()}: Working")
-        except Exception as e:
-            validation_results[name] = False
-            print(f"❌ {name.capitalize()}: Failed - {str(e)}")
-    
-    # Optimization modules (M14-18)
-    opt_modules = [
-        ("kv_caching", "tinytorch.generation.kv_cache", "enable_kv_cache"),
-        ("profiling", "tinytorch.profiling.profiler", "Profiler"),
-        ("quantization", "tinytorch.optimization.quantization", "quantize_model"),
-        ("compression", "tinytorch.optimization.compression", "magnitude_prune"),
-    ]
-    
-    for name, module_path, func_name in opt_modules:
-        try:
-            exec(f"from {module_path} import {func_name}")
-            validation_results[name] = True
-            print(f"✅ {name.replace('_', ' ').capitalize()}: Working")
-        except Exception as e:
-            validation_results[name] = False
-            print(f"❌ {name.replace('_', ' ').capitalize()}: Failed - {str(e)}")
-    
-    # Benchmarking (M19)
-    try:
-        from tinytorch.benchmarking.benchmark import Benchmark, OlympicEvent
-        validation_results["benchmarking"] = True
-        print(f"✅ Benchmarking: Working")
-    except Exception as e:
-        validation_results["benchmarking"] = False
-        print(f"❌ Benchmarking: Failed - {str(e)}")
-    
-    print("=" * 60)
-    
-    # Summary
-    total = len(validation_results)
-    working = sum(validation_results.values())
-    
-    if working == total:
-        print(f"🎉 Perfect! All {total}/{total} modules working!")
-        print("✅ You're ready to compete in TorchPerf Olympics!")
-    else:
-        print(f"⚠️  {working}/{total} modules working")
-        print(f"❌ {total - working} modules need attention")
-        print("\nPlease run: pip install -e . (in TinyTorch root)")
-    
-    return validation_results
-
-# %% [markdown]
-"""
-# 2. The Baseline (For Closed Division)
-
-If you're competing in **Closed Division**, everyone starts with this baseline model. If you're in **Open Division**, you can skip this or use it as a reference!
-
-## Baseline Model: Simple CNN on CIFAR-10
-
-We provide a simple CNN as the starting point for Closed Division:
-- **Architecture:** Conv → Pool → Conv → Pool → FC → FC
-- **Dataset:** CIFAR-10 (standardized test set)
-- **Metrics:** Accuracy, latency, memory (we'll measure together)
-
-**Closed Division:** Optimize THIS model using M14-18 techniques
-**Open Division:** Build/modify whatever you want!
-
-### Baseline Components
-
-1. **Model:** Standard CNN (no optimizations)
-2. **Metrics:** Accuracy, latency, memory, parameters
-3. **Test Data:** CIFAR-10 test set (standardized)
-4. **Hardware:** Your local machine (reported for reproducibility)
-
-The baseline establishes what "unoptimized" looks like. Your job: beat it!
-"""
-
-# %%
-#| export
-def load_baseline_model(model_name: str = "cifar10_cnn"):
-    """
-    Load a baseline model for TorchPerf Olympics competition.
-    
-    Args:
-        model_name: Name of baseline model to load
-            - "cifar10_cnn": Simple CNN for CIFAR-10 classification
-    
-    Returns:
-        Baseline model instance
-    
-    Example:
-        >>> model = load_baseline_model("cifar10_cnn")
-        >>> print(f"Parameters: {sum(p.size for p in model.parameters())}")
-    """
-    from tinytorch.core.layers import Linear
-    from tinytorch.core.spatial import Conv2d, MaxPool2d, Flatten
-    from tinytorch.core.activations import ReLU
-    
-    if model_name == "cifar10_cnn":
-        # Simple CNN: Conv -> Pool -> Conv -> Pool -> FC -> FC
-        class BaselineCNN:
-            def __init__(self):
-                self.name = "Baseline_CIFAR10_CNN"
-                
-                # Convolutional layers
-                self.conv1 = Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
-                self.relu1 = ReLU()
-                self.pool1 = MaxPool2d(kernel_size=2, stride=2)
-                
-                self.conv2 = Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
-                self.relu2 = ReLU()
-                self.pool2 = MaxPool2d(kernel_size=2, stride=2)
-                
-                # Fully connected layers
-                self.flatten = Flatten()
-                self.fc1 = Linear(64 * 8 * 8, 128)
-                self.relu3 = ReLU()
-                self.fc2 = Linear(128, 10)  # 10 classes for CIFAR-10
-            
-            def forward(self, x):
-                # Forward pass
-                x = self.conv1.forward(x)
-                x = self.relu1.forward(x)
-                x = self.pool1.forward(x)
-                
-                x = self.conv2.forward(x)
-                x = self.relu2.forward(x)
-                x = self.pool2.forward(x)
-                
-                x = self.flatten.forward(x)
-                x = self.fc1.forward(x)
-                x = self.relu3.forward(x)
-                x = self.fc2.forward(x)
-                
-                return x
-            
-            def __call__(self, x):
-                return self.forward(x)
-        
-        return BaselineCNN()
-    else:
-        raise ValueError(f"Unknown baseline model: {model_name}")
-
-def generate_baseline(model_name: str = "cifar10_cnn", quick: bool = True) -> Dict[str, Any]:
-    """
-    Generate baseline performance metrics for a model.
-    
-    Args:
-        model_name: Name of baseline model
-        quick: If True, use quick estimates instead of full benchmarks
-    
-    Returns:
-        Baseline scorecard with metrics
-    
-    Example:
-        >>> baseline = generate_baseline("cifar10_cnn", quick=True)
-        >>> print(f"Baseline latency: {baseline['latency_ms']}ms")
-    """
-    print("📊 Generating Baseline Scorecard...")
-    print("=" * 60)
-    
-    # Load model
-    model = load_baseline_model(model_name)
-    print(f"✅ Loaded baseline model: {model.name}")
-    
-    # Count parameters
-    def count_parameters(model):
-        total = 0
-        for attr_name in dir(model):
-            attr = getattr(model, attr_name)
-            if hasattr(attr, 'weights') and attr.weights is not None:
-                total += attr.weights.size
-            if hasattr(attr, 'bias') and attr.bias is not None:
-                total += attr.bias.size
-        return total
-    
-    params = count_parameters(model)
-    memory_mb = params * 4 / (1024 * 1024)  # Assuming float32
-    
-    if quick:
-        # Quick estimates for fast validation
-        print("⚡ Using quick estimates (set quick=False for full benchmark)")
-        
-        baseline = {
-            "model": model_name,
-            "accuracy": 85.0,  # Typical for this architecture
-            "latency_ms": 45.2,
-            "memory_mb": memory_mb,
-            "parameters": params,
-            "mode": "quick_estimate"
-        }
-    else:
-        # Full benchmark (requires more time)
-        from tinytorch.benchmarking.benchmark import Benchmark
-        
-        print("🔬 Running full benchmark (this may take a minute)...")
-        
-        benchmark = Benchmark([model], [{"name": "baseline"}], 
-                            warmup_runs=5, measurement_runs=20)
-        
-        # Measure latency
-        input_shape = (1, 3, 32, 32)  # CIFAR-10 input
-        latency_results = benchmark.run_latency_benchmark(input_shape=input_shape)
-        latency_ms = list(latency_results.values())[0].mean * 1000
-        
-        baseline = {
-            "model": model_name,
-            "accuracy": 85.0,  # Would need actual test set evaluation
-            "latency_ms": latency_ms,
-            "memory_mb": memory_mb,
-            "parameters": params,
-            "mode": "full_benchmark"
-        }
-    
-    # Display baseline
-    print("\n📋 BASELINE SCORECARD")
-    print("=" * 60)
-    print(f"Model:          {baseline['model']}")
-    print(f"Accuracy:       {baseline['accuracy']:.1f}%")
-    print(f"Latency:        {baseline['latency_ms']:.1f}ms")
-    print(f"Memory:         {baseline['memory_mb']:.2f}MB")
-    print(f"Parameters:     {baseline['parameters']:,}")
-    print("=" * 60)
-    print("📌 This is your starting point. Optimize to compete!")
-    print()
-    
-    return baseline
-
-# %% [markdown]
-"""
-# 3. Complete Example - See Both Tracks in Action
-
-Let's see complete examples for BOTH competition tracks!
-
-## Example 1: Closed Division - Optimization Master
-
-**Goal:** Compete in All-Around category using provided baseline
-
-**Strategy:**
-1. Load baseline CNN
-2. Apply quantization (INT8) → 4x memory reduction
-3. Apply pruning (60%) → Speed boost
-4. Benchmark and submit
-
-**Why this order?** Quantize first preserves more accuracy than pruning first.
-
-## Example 2: Open Division - Innovation Master
-
-**Goal:** Beat everyone with a novel approach
-
-**Strategy:**
-1. Improve YOUR Conv2d implementation (faster algorithm)
-2. OR design a better architecture (MobileNet-style)
-3. OR novel quantization (mixed precision per layer)
-4. Benchmark and submit
-
-**Freedom:** Modify anything in your TinyTorch implementation!
-
-Let's see the Closed Division example in detail below:
-"""
-
-# %%
-#| export
-def worked_example_optimization():
-    """
-    Complete worked example showing full optimization workflow.
-    
-    This demonstrates:
-    - Loading baseline model
-    - Applying multiple optimization techniques
-    - Benchmarking systematically
-    - Generating submission
-    
-    Students should study this and adapt for their own strategies!
-    """
-    print("🏅 WORKED EXAMPLE: Complete Optimization Workflow")
-    print("=" * 70)
-    print("Target: All-Around Event (balanced performance)")
-    print("Strategy: Quantization (INT8) → Pruning (60%)")
-    print("=" * 70)
-    print()
-    
-    # Step 1: Load Baseline
-    print("📦 Step 1: Load Baseline Model")
-    print("-" * 70)
-    baseline = load_baseline_model("cifar10_cnn")
-    baseline_metrics = generate_baseline("cifar10_cnn", quick=True)
-    print()
-    
-    # Step 2: Apply Quantization
-    print("🔧 Step 2: Apply INT8 Quantization (Module 17)")
-    print("-" * 70)
-    print("💡 Why quantize? Reduces memory 4x (FP32 → INT8)")
-    
-    # For demonstration, we'll simulate quantization
-    # In real competition, students would use:
-    # from tinytorch.optimization.quantization import quantize_model
-    # optimized = quantize_model(baseline, bits=8)
-    
-    print("✅ Quantized model (simulated)")
-    print("   - Memory: 12.4MB → 3.1MB (4x reduction)")
-    print()
-    
-    # Step 3: Apply Pruning
-    print("✂️  Step 3: Apply Magnitude Pruning (Module 18)")
-    print("-" * 70)
-    print("💡 Why prune? Removes 60% of weights for faster inference")
-    
-    # For demonstration, we'll simulate pruning
-    # In real competition, students would use:
-    # from tinytorch.optimization.compression import magnitude_prune
-    # optimized = magnitude_prune(optimized, sparsity=0.6)
-    
-    print("✅ Pruned model (simulated)")
-    print("   - Active parameters: 3.2M → 1.28M (60% removed)")
-    print()
-    
-    # Step 4: Benchmark Results
-    print("📊 Step 4: Benchmark Optimized Model (Module 19)")
-    print("-" * 70)
-    
-    # Simulated optimized metrics
-    optimized_metrics = {
-        "model": "Optimized_CIFAR10_CNN",
-        "accuracy": 83.5,  # Slight drop from aggressive optimization
-        "latency_ms": 22.1,
-        "memory_mb": 1.24,  # 4x quantization + 60% pruning
-        "parameters": 1280000,
-        "techniques": ["quantization_int8", "magnitude_prune_0.6"]
-    }
-    
-    print("Baseline vs Optimized:")
-    print(f"  Accuracy:    {baseline_metrics['accuracy']:.1f}% → {optimized_metrics['accuracy']:.1f}% (-1.5pp)")
-    print(f"  Latency:     {baseline_metrics['latency_ms']:.1f}ms → {optimized_metrics['latency_ms']:.1f}ms (2.0x faster ✅)")
-    print(f"  Memory:      {baseline_metrics['memory_mb']:.2f}MB → {optimized_metrics['memory_mb']:.2f}MB (10.0x smaller ✅)")
-    print(f"  Parameters:  {baseline_metrics['parameters']:,} → {optimized_metrics['parameters']:,} (60% fewer ✅)")
-    print()
-    
-    # Step 5: Generate Submission
-    print("📤 Step 5: Generate Competition Submission")
-    print("-" * 70)
-    
-    submission = {
-        "event": "all_around",
-        "athlete_name": "Example_Submission",
-        "baseline": baseline_metrics,
-        "optimized": optimized_metrics,
-        "improvements": {
-            "accuracy_drop": -1.5,
-            "latency_speedup": 2.0,
-            "memory_reduction": 10.0
-        },
-        "techniques_applied": ["quantization_int8", "magnitude_prune_0.6"],
-        "technique_order": "quantize_first_then_prune"
-    }
-    
-    print("✅ Submission generated!")
-    print(f"   Event: {submission['event']}")
-    print(f"   Techniques: {', '.join(submission['techniques_applied'])}")
-    print()
-    print("=" * 70)
-    print("🎯 This is the complete workflow!")
-    print("   Now it's your turn to implement your own optimization strategy.")
-    print("=" * 70)
-    
-    return submission
-
-# %% [markdown]
-"""
-# 4. Your Turn - Pick Your Track!
-
-Now it's time to compete! Choose your track and implement your strategy.
-
-## Choose Your Track
-
-### 🔒 Closed Division Template
-**If you choose Closed Division:**
-1. Pick a category (Latency Sprint, Memory Challenge, etc.)
-2. Design your optimization strategy
-3. Implement in `optimize_for_competition()` below
-4. Use techniques from Modules 14-18 only
-5. Generate submission
-
-**Good for:** Clear path, fair comparison, most students
-
-### 🔓 Open Division Template  
-**If you choose Open Division:**
-1. Pick a category
-2. Modify YOUR TinyTorch implementations (go edit earlier modules!)
-3. OR design novel architectures
-4. Re-export with `tito export` and benchmark
-5. Generate submission
-
-**Good for:** Creative freedom, systems innovation, advanced students
-
-## Competition Categories (Pick ONE)
-- 🏃 **Latency Sprint:** Fastest inference
-- 🏋️ **Memory Challenge:** Smallest model
-- 🎯 **Accuracy Contest:** Best accuracy within constraints
-- 🏋️‍♂️ **All-Around:** Best balanced performance
-- 🚀 **Extreme Push:** Most aggressive optimization
-
-## Template Below
-
-Use the `optimize_for_competition()` function to implement your strategy:
-- **Closed Division:** Apply M14-18 techniques
-- **Open Division:** Do whatever you want, document it!
-"""
-
-# %%
-#| export
-def optimize_for_competition(baseline_model, event: str = "all_around", division: str = "closed"):
-    """
-    🏅 YOUR COMPETITION ENTRY - IMPLEMENT YOUR STRATEGY HERE!
-    
-    Args:
-        baseline_model: Starting model (use for Closed, optional for Open)
-        event: Category you're competing in
-            - "latency_sprint": Minimize latency
-            - "memory_challenge": Minimize memory
-            - "accuracy_contest": Maximize accuracy
-            - "all_around": Best balance
-            - "extreme_push": Most aggressive
-        division: "closed" or "open" - which track you chose
-    
-    Returns:
-        Your optimized model
-    
-    🔒 CLOSED DIVISION Example:
-        from tinytorch.optimization.quantization import quantize_model
-        from tinytorch.optimization.compression import magnitude_prune
-        
-        optimized = baseline_model
-        optimized = quantize_model(optimized, bits=8)
-        optimized = magnitude_prune(optimized, sparsity=0.7)
-        return optimized
-    
-    🔓 OPEN DIVISION Example:
-        # Build your own model OR
-        # Use your improved implementations from earlier modules
-        # (after you've modified and re-exported them)
-        
-        from tinytorch.models import YourCustomArchitecture
-        optimized = YourCustomArchitecture()
-        return optimized
-    """
-    
-    print(f"🏅 YOUR OPTIMIZATION STRATEGY FOR: {event}")
-    print("=" * 70)
-    
-    # Start with baseline
-    optimized_model = baseline_model
-    
-    # ============================================================
-    # YOUR CODE BELOW - Apply optimization techniques here!
-    # ============================================================
-    
-    # TODO: Students implement their optimization strategy
-    #
-    # Example strategies by event:
-    #
-    # Latency Sprint (speed priority):
-    #   - Heavy quantization (INT4 or INT8)
-    #   - Aggressive pruning (80-90%)
-    #   - Kernel fusion if applicable
-    #
-    # Memory Challenge (size priority):
-    #   - INT8 or INT4 quantization
-    #   - Aggressive pruning (70-90%)
-    #   - Compression techniques
-    #
-    # All-Around (balanced):
-    #   - INT8 quantization
-    #   - Moderate pruning (50-70%)
-    #   - Selective optimization
-    #
-    # Your strategy:
-    
-    
-    
-    # ============================================================
-    # YOUR CODE ABOVE
-    # ============================================================
-    
-    print("✅ Optimization complete!")
-    print("💡 Tip: Benchmark your result to see the impact!")
-    
-    return optimized_model
-
-#| export
-def validate_submission(submission: Dict[str, Any]) -> Dict[str, Any]:
-    """
-    Validate competition submission with sanity checks.
-    
-    This catches honest mistakes like unrealistic speedups or accidental training.
-    Honor code system - we trust but verify basic reasonableness.
-    
-    Args:
-        submission: Submission dictionary to validate
-        
-    Returns:
-        Dict with validation results and warnings
-    """
-    checks = []
-    warnings = []
-    errors = []
-    
-    # Extract metrics
-    normalized = submission.get("normalized_scores", {})
-    speedup = normalized.get("speedup", 1.0)
-    compression = normalized.get("compression_ratio", 1.0)
-    accuracy_delta = normalized.get("accuracy_delta", 0.0)
-    
-    # Check 1: Speedup is reasonable (not claiming impossible gains)
-    if speedup > 50:
-        errors.append(f"❌ Speedup {speedup:.1f}x seems unrealistic (>50x)")
-    elif speedup > 20:
-        warnings.append(f"⚠️  Speedup {speedup:.1f}x is very high - please verify measurements")
-    else:
-        checks.append(f"✅ Speedup {speedup:.2f}x is reasonable")
-    
-    # Check 2: Compression is reasonable
-    if compression > 32:
-        errors.append(f"❌ Compression {compression:.1f}x seems unrealistic (>32x)")
-    elif compression > 16:
-        warnings.append(f"⚠️  Compression {compression:.1f}x is very high - please verify")
-    else:
-        checks.append(f"✅ Compression {compression:.2f}x is reasonable")
-    
-    # Check 3: Accuracy didn't improve (Closed Division rule - no training allowed!)
-    division = submission.get("division", "closed")
-    if division == "closed" and accuracy_delta > 1.0:
-        errors.append(f"❌ Accuracy improved by {accuracy_delta:.1f}pp - did you accidentally train the model?")
-    elif accuracy_delta > 0.5:
-        warnings.append(f"⚠️  Accuracy improved by {accuracy_delta:.1f}pp - verify no training occurred")
-    else:
-        checks.append(f"✅ Accuracy change {accuracy_delta:+.2f}pp is reasonable")
-    
-    # Check 4: GitHub repo provided
-    github_repo = submission.get("github_repo", "")
-    if not github_repo or github_repo == "":
-        warnings.append("⚠️  No GitHub repo provided - required for verification")
-    else:
-        checks.append(f"✅ GitHub repo provided: {github_repo}")
-    
-    # Check 5: Required fields present
-    required_fields = ["division", "event", "athlete_name", "baseline", "optimized", "normalized_scores"]
-    missing = [f for f in required_fields if f not in submission]
-    if missing:
-        errors.append(f"❌ Missing required fields: {', '.join(missing)}")
-    else:
-        checks.append("✅ All required fields present")
-    
-    # Check 6: Techniques documented
-    techniques = submission.get("techniques_applied", [])
-    if not techniques or "TODO" in str(techniques):
-        warnings.append("⚠️  No optimization techniques listed")
-    else:
-        checks.append(f"✅ Techniques documented: {', '.join(techniques[:3])}...")
-    
-    return {
-        "valid": len(errors) == 0,
-        "checks": checks,
-        "warnings": warnings,
-        "errors": errors
-    }
-
-#| export
-def generate_submission(baseline_model, optimized_model, 
-                       division: str = "closed",
-                       event: str = "all_around",
-                       athlete_name: str = "YourName",
-                       github_repo: str = "",
-                       techniques: List[str] = None) -> Dict[str, Any]:
-    """
-    Generate standardized TinyMLPerf competition submission with normalized scoring.
-    
-    Args:
-        baseline_model: Original unoptimized model
-        optimized_model: Your optimized model
-        division: "closed" or "open"
-        event: Competition category (latency_sprint, memory_challenge, all_around, etc.)
-        athlete_name: Your name for submission
-        github_repo: GitHub repository URL for code verification
-        techniques: List of optimization techniques applied
-    
-    Returns:
-        Submission dictionary (will be saved as JSON)
-    """
-    print("📤 Generating TinyMLPerf Competition Submission...")
-    print("=" * 70)
-    
-    # Get baseline metrics
-    baseline_metrics = generate_baseline(quick=True)
-    
-    # Benchmark optimized model
-    print("🔬 Benchmarking optimized model...")
-    
-    # Use Profiler and Benchmark from Module 19
-    profiler = Profiler()
-    
-    # For demonstration, we'll use placeholder metrics
-    # In real competition, students would measure their actual optimized model
-    optimized_metrics = {
-        "model": getattr(optimized_model, 'name', 'Optimized_Model'),
-        "accuracy": 84.0,  # Would be measured with actual test set
-        "latency_ms": 28.0,  # Would be measured with profiler
-        "memory_mb": 4.0,  # Would be measured with profiler
-        "parameters": 2000000,  # Would be counted
-    }
-    
-    # Calculate normalized scores using Module 19's function
-    baseline_for_norm = {
-        "latency": baseline_metrics["latency_ms"],
-        "memory": baseline_metrics["memory_mb"],
-        "accuracy": baseline_metrics["accuracy"]
-    }
-    
-    optimized_for_norm = {
-        "latency": optimized_metrics["latency_ms"],
-        "memory": optimized_metrics["memory_mb"],
-        "accuracy": optimized_metrics["accuracy"]
-    }
-    
-    normalized_scores = calculate_normalized_scores(baseline_for_norm, optimized_for_norm)
-    
-    # Create submission with all required fields
-    submission = {
-        "division": division,
-        "event": event,
-        "athlete_name": athlete_name,
-        "github_repo": github_repo,
-        "baseline": baseline_metrics,
-        "optimized": optimized_metrics,
-        "normalized_scores": {
-            "speedup": normalized_scores["speedup"],
-            "compression_ratio": normalized_scores["compression_ratio"],
-            "accuracy_delta": normalized_scores["accuracy_delta"],
-            "efficiency_score": normalized_scores["efficiency_score"]
-        },
-        "techniques_applied": techniques or ["TODO: Document your optimization techniques"],
-        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
-        "tinytorch_version": "0.1.0",
-        "honor_code": False  # Must be explicitly set to True after validation
-    }
-    
-    # Validate submission
-    print("\n🔍 Validating submission...")
-    validation = validate_submission(submission)
-    
-    # Display validation results
-    print("\n📋 Validation Results:")
-    for check in validation["checks"]:
-        print(f"  {check}")
-    for warning in validation["warnings"]:
-        print(f"  {warning}")
-    for error in validation["errors"]:
-        print(f"  {error}")
-    
-    if not validation["valid"]:
-        print("\n❌ Submission has errors - please fix before submitting")
-        return submission
-    
-    # Save to JSON
-    output_file = Path("submission.json")
-    with open(output_file, "w") as f:
-        json.dump(submission, f, indent=2)
-    
-    print(f"\n✅ Submission saved to: {output_file}")
-    print()
-    print("📊 Your Normalized Scores (MLPerf-style):")
-    print(f"  Division:        {division.upper()}")
-    print(f"  Event:           {event.replace('_', ' ').title()}")
-    print(f"  Speedup:         {normalized_scores['speedup']:.2f}x faster ⚡")
-    print(f"  Compression:     {normalized_scores['compression_ratio']:.2f}x smaller 💾")
-    print(f"  Accuracy:        {optimized_metrics['accuracy']:.1f}% (Δ {normalized_scores['accuracy_delta']:+.2f}pp)")
-    print(f"  Efficiency:      {normalized_scores['efficiency_score']:.2f}")
-    print()
-    print("📤 Next Steps:")
-    print("  1. Verify all metrics are correct")
-    print("  2. Push your code to GitHub (if not done)")
-    print("  3. Run: tito submit submission.json")
-    print("     (This will validate and prepare final submission)")
-    print()
-    print("=" * 70)
-    
-    return submission
-
-# %% [markdown]
-"""
-# 5. Module Integration Test
-
-Complete validation and competition workflow test.
-"""
-
-# %% nbgrader={"grade": true, "grade_id": "test-module", "locked": true, "points": 10}
-def test_module():
-    """
-    Complete test of Module 20 functionality.
-    
-    This validates:
-    - Installation validation works
-    - Baseline generation works
-    - Worked example runs successfully
-    - Competition template is ready
-    """
-    print("=" * 70)
-    print("MODULE 20 INTEGRATION TEST")
-    print("=" * 70)
-    print()
-    
-    # Test 1: Validation
-    print("🔧 Test 1: System Validation")
-    validation_status = validate_installation()
-    assert len(validation_status) > 0, "Validation should return status dict"
-    print("✅ Validation working!")
-    print()
-    
-    # Test 2: Baseline Generation
-    print("📊 Test 2: Baseline Generation")
-    baseline = generate_baseline(quick=True)
-    assert "accuracy" in baseline, "Baseline should include accuracy"
-    assert "latency_ms" in baseline, "Baseline should include latency"
-    assert "memory_mb" in baseline, "Baseline should include memory"
-    print("✅ Baseline generation working!")
-    print()
-    
-    # Test 3: Worked Example
-    print("🏅 Test 3: Worked Example")
-    example_submission = worked_example_optimization()
-    assert "event" in example_submission, "Submission should include event"
-    assert "baseline" in example_submission, "Submission should include baseline"
-    assert "optimized" in example_submission, "Submission should include optimized"
-    print("✅ Worked example working!")
-    print()
-    
-    # Test 4: Competition Template
-    print("🎯 Test 4: Competition Template")
-    baseline_model = load_baseline_model("cifar10_cnn")
-    optimized = optimize_for_competition(baseline_model, event="all_around")
-    assert optimized is not None, "Optimization should return model"
-    print("✅ Competition template working!")
-    print()
-    
-    print("=" * 70)
-    print("✅ ALL TESTS PASSED!")
-    print("=" * 70)
-    print()
-    print("🎉 You're ready for TorchPerf Olympics!")
-    print("   Next steps:")
-    print("   1. Implement your optimization strategy in optimize_for_competition()")
-    print("   2. Run this module to generate submission.json")
-    print("   3. Upload to competition platform")
-    print()
-    print("🔥 Good luck! May the best optimizer win! 🏅")
-
-test_module()
-
-# %% [markdown]
-"""
-## 🤔 ML Systems Thinking: Competition as Learning
-
-TorchPerf Olympics isn't just about winning - it's about understanding trade-offs:
-
-**The Meta-Lesson**: Every optimization involves trade-offs:
-- Quantization: Speed vs Accuracy
-- Pruning: Size vs Performance
-- Caching: Memory vs Speed
-
-Professional ML engineers navigate these trade-offs daily. The competition forces you to:
-1. **Think systematically** about optimization strategies
-2. **Measure rigorously** using benchmarking tools
-3. **Make data-driven decisions** based on actual measurements
-4. **Document and justify** your choices
-
-The best submission isn't always the "fastest" or "smallest" - it's the one that best understands and navigates the trade-off space for their chosen event.
-
-What will your strategy be? 🤔
-"""
-
-# %% [markdown]
-"""
-## 🎯 MODULE SUMMARY: Competition & Validation
-
-**What You've Learned:**
-- ✅ How to validate your TinyTorch installation
-- ✅ How to generate baseline performance metrics
-- ✅ How to combine optimization techniques systematically
-- ✅ How to benchmark and measure impact
-- ✅ How to generate standardized competition submissions
-
-**The Complete Workflow:**
-```
-1. Validate  → Ensure environment works
-2. Baseline  → Establish reference performance
-3. Optimize  → Apply techniques from M14-18
-4. Benchmark → Measure impact using M19
-5. Submit    → Generate standardized submission
-```
-
-**Key Takeaway**: Competition teaches systematic optimization thinking. The goal isn't just winning - it's understanding the entire optimization process from baseline to submission.
-
-**Next Steps:**
-1. Study the worked example
-2. Implement your own optimization strategy
-3. Benchmark your results
-4. Generate submission.json
-5. Compete in TorchPerf Olympics!
-
-🔥 Now go optimize and win gold! 🏅
-"""
-
diff --git a/modules/20_competition_ARCHIVED/module.yaml b/modules/20_competition_ARCHIVED/module.yaml
deleted file mode 100644
index 3907f65e..00000000
--- a/modules/20_competition_ARCHIVED/module.yaml
+++ /dev/null
@@ -1,59 +0,0 @@
-name: "Competition & Validation"
-module_number: "20"
-description: "TorchPerf Olympics preparation - validation, baseline, and competition submission"
-difficulty: "⭐⭐⭐" # 3 stars - capstone integration
-estimated_time: "1-2 hours"
-
-prerequisites:
-  - "Module 19: Benchmarking"
-  - "Modules 14-18: Optimization techniques"
-
-learning_objectives:
-  - "Validate TinyTorch installation and environment"
-  - "Generate baseline performance metrics"
-  - "Understand complete optimization workflow"
-  - "Create standardized competition submissions"
-
-key_concepts:
-  - "System validation and environment checks"
-  - "Baseline generation and reference metrics"
-  - "End-to-end optimization workflow"
-  - "Competition submission format"
-
-skills_developed:
-  - "Systematic validation and testing"
-  - "Performance measurement and comparison"
-  - "Integration of multiple optimization techniques"
-  - "Professional submission preparation"
-
-exports_to: "tinytorch/competition/submit.py"
-
-test_coverage:
-  - "Installation validation"
-  - "Baseline generation"
-  - "Worked example workflow"
-  - "Competition template structure"
-
-connections:
-  builds_on:
-    - "Module 19 for benchmarking tools"
-    - "Modules 14-18 for optimization techniques"
-  enables:
-    - "TorchPerf Olympics competition participation"
-    - "Systematic performance optimization"
-    - "Professional ML systems workflow"
-
-notes: |
-  This is the capstone module that brings together all previous modules.
-  It's lightweight (no new techniques) but shows the complete workflow from
-  validation through optimization to submission.
-  
-  Students learn:
-  1. How to validate their environment works
-  2. What baseline performance looks like
-  3. How to apply optimizations systematically
-  4. How to package work for competition
-  
-  The module includes a complete worked example and a template for students
-  to implement their own optimization strategies.
-