mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-23 23:53:23 -05:00
📚 Update website navigation and content
- Add Module 20 (AI Olympics) to Competition section - Remove Historical Milestones from navigation (simplify) - Remove separate Leaderboard page (consolidate into capstone) - Simplify AI Olympics capstone content (~60 lines) - Clear 'Coming Soon' box for competition platform - Brief category descriptions - Focus on what students can do now - Simplify Community page (~50 lines) - Clear 'Coming Soon' box for dashboard features - Brief feature descriptions - Ways to participate now - Split Competition and Community into separate nav sections - Fix jupyter-book dependency compatibility for Python 3.8 - myst-parser 0.18.1 (compatible with myst-nb 0.17.2) - sphinx 5.3.0 - Update requirements.txt with compatible versions Result: Clean, honest, scannable website that shows all 20 modules
This commit is contained in:
Vijay Janapa Reddi
@@ -74,18 +74,14 @@ parts:
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title: "18. KV Caching"
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- file: chapters/19-benchmarking
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title: "19. Benchmarking"
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- caption: 🏅 Competition
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chapters:
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- file: chapters/20-capstone
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title: "20. Capstone"
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title: "20. AI Olympics"
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- caption: 🏆 Historical Milestones
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- caption: 🌍 Community
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chapters:
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- file: chapters/milestones
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title: "Journey Through ML History"
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- caption: 🏅 Community
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chapters:
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- file: leaderboard
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title: "Leaderboard"
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- file: community
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title: "Ecosystem"
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@@ -1,444 +1,65 @@
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# 20. Capstone
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# 20. AI Olympics
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**TinyTorch Olympics: Compete on Systems Performance**
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**Your Capstone Project**
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---
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## 🎯 Overview
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The TinyTorch Olympics is your **final systems engineering challenge**—a competitive capstone where you optimize your TinyTorch implementations across multiple performance dimensions. This isn't just about accuracy; it's about speed, memory efficiency, power consumption, and real-world deployment constraints.
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The AI Olympics is your final capstone project where you bring together everything you've learned throughout the course. You'll optimize your TinyTorch implementation and see how it performs.
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### Why a Competitive Capstone?
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### What You'll Do
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**Most ML courses end with:** "Build a project that works."
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**TinyTorch ends with:** "Optimize your system and compete."
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- **Apply your knowledge**: Use all the modules you've built (tensors through optimizations)
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- **Optimize your system**: Experiment with different performance improvements
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- **Benchmark your work**: Measure speed, memory, and efficiency
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- **Take part in competition**: Compare your results with others (optional)
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This reflects the reality of production ML engineering:
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- Getting a model working is just the beginning
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- Performance matters: speed, memory, power, cost
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- Systems engineering skills separate good ML engineers from great ones
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- Real ML teams optimize and benchmark constantly
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This is about learning to care about **performance, not just correctness**—which is what production ML engineering is all about.
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---
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## 🏆 Competition Categories
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## 🏆 Competition Platform (Coming Soon)
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### ⚡ Speed Demon
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**"Fastest inference on standard hardware"**
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<div style="background: #fff3cd; border: 2px solid #ffc107; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0;">
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<h3 style="margin: 0 0 1rem 0; color: #856404;">🚧 Infrastructure Under Development</h3>
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<p style="margin: 0; color: #856404;">We're building the competition platform with automated submissions, live leaderboard, and benchmarking infrastructure. You can still complete your capstone project and optimize your implementation now!</p>
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</div>
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- **Metric**: Inferences per second
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- **Skills Tested**: Kernel optimization, parallelization, caching
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- **Constraint**: Must maintain ≥90% accuracy
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- **Modules Applied**: 14-19 optimization techniques
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### Planned Competition Categories
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### 💾 Memory Miser
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**"Smallest memory footprint"**
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**⚡ Speed Optimization**
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- Fastest inference times
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- **Metric**: Peak memory usage during inference
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- **Skills Tested**: Quantization, compression, efficient architectures
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- **Constraint**: Must maintain ≥85% accuracy
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- **Modules Applied**: Quantization (16), Compression (17)
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**💾 Memory Efficiency**
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- Smallest memory footprint
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### 📱 Edge Expert
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**"Best performance on resource-constrained hardware"**
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**📱 Edge Performance**
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- Best performance on resource-constrained devices
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- **Metric**: Composite score (speed + accuracy + efficiency)
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- **Skills Tested**: Complete optimization pipeline
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- **Constraint**: Must run on edge devices (e.g., Raspberry Pi)
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- **Modules Applied**: Full optimization suite (14-19)
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### 🔋 Energy Efficient
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**"Lowest power consumption"**
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- **Metric**: Energy per inference (joules/prediction)
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- **Skills Tested**: Model compression, efficient algorithms
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- **Constraint**: Must maintain competitive accuracy
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- **Modules Applied**: Profiling (14), Optimization (15-19)
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### 🏃♂️ TinyMLPerf
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**"Official MLPerf-style benchmark"**
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- **Metric**: Standardized benchmark suite performance
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- **Skills Tested**: Complete systems optimization
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- **Constraint**: Must pass all compliance tests
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- **Modules Applied**: Benchmarking (19) + All optimization
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**🏆 Live Leaderboard**
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- Real-time rankings by category
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- Semester champions and recognition
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---
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## 🎮 Competition Structure
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## 🎯 What Makes a Good Capstone
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### Phase 1: Baseline Submission
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**"Establish your starting point"**
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**Systems Thinking**
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- Understand performance trade-offs
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- Profile before optimizing
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- Measure everything
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```bash
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# Submit your best model from modules 1-13
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tito olympics submit --baseline
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**Real Optimization**
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- Apply techniques from modules 14-19
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- Use data to guide decisions
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- Document what works and what doesn't
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# Get initial scores across all categories
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tito olympics scores --category all
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```
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**What happens:**
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- Your model is evaluated across all categories
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- You see where you rank initially
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- You identify which categories to focus on
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### Phase 2: Optimization Sprint
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**"Apply modules 14-19 systematically"**
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```bash
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# Profile your model
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tito olympics profile
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# Apply optimization techniques
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# Module 14: Profile and identify bottlenecks
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# Module 15: Implement acceleration techniques
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# Module 16: Add quantization for memory/speed
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# Module 17: Apply compression for size
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# Module 18: Implement caching strategies
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# Module 19: Benchmark against production systems
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```
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**Strategy:**
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1. **Week 1**: Profile and analyze bottlenecks
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2. **Week 2**: Apply memory optimizations
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3. **Week 3**: Implement speed improvements
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4. **Week 4**: Test on edge hardware
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5. **Week 5**: Final benchmarking and submission
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### Phase 3: Final Submission & Rankings
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**"See how you stack up"**
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```bash
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# Submit optimized models
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tito olympics submit --final
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# View live leaderboard
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tito olympics leaderboard
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# Generate portfolio report
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tito olympics report
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```
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**Portfolio Quality**
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- Clear before/after metrics
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- Explanation of techniques used
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- Reproducible results
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---
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## 📊 Leaderboard System
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### Real-Time Rankings
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```
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🏆 TinyTorch Olympics Leaderboard
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⚡ Speed Demon Category:
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1. alice_chen 847.3 inf/sec (95.2% acc) 🥇
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2. bob_smith 612.7 inf/sec (94.8% acc) 🥈
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3. carol_wong 588.1 inf/sec (96.1% acc) 🥉
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💾 Memory Miser Category:
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1. dave_kim 12.4 MB (91.7% acc) 🥇
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2. eve_patel 15.8 MB (93.2% acc) 🥈
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3. frank_liu 18.2 MB (89.9% acc) 🥉
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📱 Edge Expert Category:
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1. grace_lee Score: 94.5 (Composite) 🥇
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2. henry_zhao Score: 91.2 (Composite) 🥈
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3. iris_tan Score: 88.7 (Composite) 🥉
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```
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### Scoring Methodology
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**Primary Metrics:**
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- Each category has its own performance metric
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- Must meet minimum accuracy threshold to qualify
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- Tie-breaker: Higher accuracy wins
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**Bonus Points:**
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- **Innovation Award**: Novel optimization techniques (+5%)
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- **Documentation Award**: Exceptional technical writeup (+3%)
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- **Teaching Award**: Best educational explanation (+3%)
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**Overall Champion:**
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- Best combined performance across ALL categories
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- Requires competing in at least 3 categories
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- Weighted by difficulty of optimization achieved
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---
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## 🎯 Deliverables
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### Competition Submission Package
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**1. Optimized Model**
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```bash
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my_submission/
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├── model.py # Your optimized TinyTorch model
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├── requirements.txt # Dependencies
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├── README.md # Setup instructions
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└── run_benchmark.py # Evaluation script
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```
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**2. Performance Report**
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- Optimization techniques applied
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- Before/after measurements
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- Systems engineering analysis
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- Trade-offs and design decisions
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**3. Reproduction Guide**
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- Clear setup instructions
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- Hardware requirements
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- Expected results
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- Troubleshooting tips
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### Portfolio Artifacts You Get
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✅ **Leaderboard Rankings**: Proof of competitive performance
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✅ **Technical Report**: Demonstrate systems engineering skills
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✅ **Benchmark Results**: Compare your work to industry standards
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✅ **Peer Recognition**: Rankings visible to potential employers
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✅ **GitHub Portfolio**: Complete optimization case study
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---
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## 🔧 Technical Requirements
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### Submission Requirements
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**All submissions must:**
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- Use ONLY TinyTorch implementations (modules 1-13)
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- Run on specified reference hardware
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- Include reproducible benchmarking scripts
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- Meet accuracy thresholds for category
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- Pass automated validation tests
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**Allowed optimizations:**
|
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- Any technique from modules 14-19
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- Custom kernel implementations
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- Novel architectural designs
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- Creative caching strategies
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- Hardware-specific optimizations
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|
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**Not allowed:**
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- External ML frameworks (PyTorch, TensorFlow, etc.)
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- Pre-trained models from other sources
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- Hardcoded test outputs
|
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- Breaking TinyTorch API contracts
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|
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### Evaluation Environment
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**Standard Hardware:**
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- CPU: AMD EPYC 7763 (or equivalent)
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- Memory: 32GB RAM
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- Storage: NVMe SSD
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- OS: Ubuntu 22.04 LTS
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|
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**Edge Hardware (for Edge Expert category):**
|
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- Raspberry Pi 4B (4GB RAM)
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- Power monitoring equipment
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- Standard cooling (no exotic setups)
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|
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---
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## 📚 Educational Value
|
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### What You Learn
|
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|
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**Systems Engineering:**
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- Performance profiling and bottleneck analysis
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- Memory optimization techniques
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- Speed vs. accuracy trade-offs
|
||||
- Hardware-aware algorithm design
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- Production deployment constraints
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|
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**ML Engineering:**
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- Real-world optimization priorities
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- Benchmarking and measurement
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- Competitive system design
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- Documentation and reproducibility
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- Community collaboration
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|
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**Career Skills:**
|
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- Portfolio-worthy competitive performance
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- Systems thinking for production ML
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- Technical communication and documentation
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- Performance engineering mindset
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|
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### Why This Matters
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**Most ML courses teach:** Algorithm implementation
|
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**TinyTorch teaches:** Systems optimization
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|
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**Most projects end with:** "Does it work?"
|
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**TinyTorch ends with:** "How fast? How small? How efficient?"
|
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|
||||
This is what separates ML researchers from ML engineers. You learn to care about the full system, not just the algorithm.
|
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|
||||
---
|
||||
|
||||
## 🚀 Getting Started
|
||||
|
||||
### Prerequisites
|
||||
|
||||
**Required Modules:**
|
||||
- Modules 1-13: Build your base model
|
||||
- Modules 14-19: Learn optimization techniques
|
||||
|
||||
**Recommended Preparation:**
|
||||
```bash
|
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# Complete all modules
|
||||
tito checkpoint status
|
||||
|
||||
# Test your optimization skills
|
||||
tito module test 14 # Profiling
|
||||
tito module test 15 # Acceleration
|
||||
tito module test 16 # Quantization
|
||||
tito module test 17 # Compression
|
||||
tito module test 18 # Caching
|
||||
tito module test 19 # Benchmarking
|
||||
```
|
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|
||||
### Quick Start
|
||||
|
||||
```bash
|
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# 1. Register for Olympics
|
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tito olympics register
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|
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# 2. Submit baseline
|
||||
tito olympics submit --baseline
|
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|
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# 3. View your scores
|
||||
tito olympics scores
|
||||
|
||||
# 4. Optimize and resubmit
|
||||
tito olympics submit --category speed
|
||||
|
||||
# 5. Check leaderboard
|
||||
tito olympics leaderboard
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🏅 Awards & Recognition
|
||||
|
||||
### Category Champions 🥇
|
||||
- Top performer in each category
|
||||
- Certificate of achievement
|
||||
- Featured on leaderboard permanently
|
||||
- LinkedIn-ready accomplishment
|
||||
|
||||
### Overall Systems Engineer 🏆
|
||||
- Best combined performance across categories
|
||||
- Requires competing in ≥3 categories
|
||||
- Special recognition on course website
|
||||
- Strong portfolio differentiator
|
||||
|
||||
### Special Awards
|
||||
|
||||
**🚀 Innovation Award**
|
||||
- Most creative optimization approach
|
||||
- Novel techniques or architectures
|
||||
- Judged by instructors and peers
|
||||
|
||||
**📚 Teaching Award**
|
||||
- Best documented optimization process
|
||||
- Helps future students learn
|
||||
- Clarity and educational value
|
||||
|
||||
**🎯 First Blood Award**
|
||||
- First to beat instructor baseline
|
||||
- In any category
|
||||
- Special early-achiever recognition
|
||||
|
||||
---
|
||||
|
||||
## 💡 Strategy Tips
|
||||
|
||||
### Getting Started
|
||||
|
||||
**1. Profile First**
|
||||
```bash
|
||||
# Don't guess—measure!
|
||||
tito olympics profile --detailed
|
||||
```
|
||||
|
||||
**2. Pick Your Category**
|
||||
- Speed Demon: Focus on compute optimization
|
||||
- Memory Miser: Quantization and compression
|
||||
- Edge Expert: Balanced optimization
|
||||
- Energy Efficient: Algorithm efficiency
|
||||
|
||||
**3. Apply Systematic Optimization**
|
||||
- One technique at a time
|
||||
- Measure impact of each change
|
||||
- Keep detailed notes
|
||||
- Document trade-offs
|
||||
|
||||
### Advanced Strategies
|
||||
|
||||
**For Speed:**
|
||||
- Vectorize operations (Module 15)
|
||||
- Implement caching (Module 18)
|
||||
- Optimize hot paths first
|
||||
- Consider CPU instruction sets
|
||||
|
||||
**For Memory:**
|
||||
- Quantization (Module 16)
|
||||
- Weight pruning (Module 17)
|
||||
- Efficient data structures
|
||||
- Activation checkpointing
|
||||
|
||||
**For Edge:**
|
||||
- Balance all dimensions
|
||||
- Test on real hardware early
|
||||
- Power profiling tools
|
||||
- Thermal management
|
||||
|
||||
---
|
||||
|
||||
## 🌟 Success Stories
|
||||
|
||||
### What Past Participants Say
|
||||
|
||||
> "The Olympics forced me to actually care about performance. In previous courses, I just wanted things to work. Here, I learned to optimize." - *Alex, Spring 2024*
|
||||
|
||||
> "Ranking #2 in Memory Efficiency was the highlight of my portfolio. It came up in every interview." - *Jordan, Fall 2024*
|
||||
|
||||
> "I thought I understood optimization until the Olympics. The leaderboard competition pushed me to learn techniques I would have skipped." - *Sam, Spring 2024*
|
||||
|
||||
---
|
||||
|
||||
## 🎓 Final Thoughts
|
||||
|
||||
### Why Olympics > Traditional Capstone
|
||||
|
||||
**Traditional Capstone:**
|
||||
- Build a project that works ✓
|
||||
- Submit and move on
|
||||
- Limited comparison with peers
|
||||
- Optimization is optional
|
||||
|
||||
**TinyTorch Olympics:**
|
||||
- Build a system that performs ⚡
|
||||
- Compete and improve continuously
|
||||
- Clear performance benchmarks
|
||||
- Optimization is the point
|
||||
|
||||
### The Real Goal
|
||||
|
||||
The Olympics isn't just about winning. It's about:
|
||||
|
||||
✅ **Learning systems thinking**
|
||||
✅ **Caring about performance**
|
||||
✅ **Building portfolio-worthy projects**
|
||||
✅ **Joining a community of builders**
|
||||
✅ **Preparing for real ML engineering**
|
||||
|
||||
---
|
||||
|
||||
**Ready to compete?**
|
||||
|
||||
```bash
|
||||
tito olympics register
|
||||
```
|
||||
|
||||
**Build systems. Optimize relentlessly. Compete.** 🥇
|
||||
|
||||
**Build. Optimize. Measure. Repeat.** 🔥
|
||||
|
||||
@@ -1,304 +1,58 @@
|
||||
# 🌍 Community Ecosystem
|
||||
|
||||
**Who's Building with TinyTorch?**
|
||||
**Building Together**
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Overview
|
||||
|
||||
The TinyTorch community is a global ecosystem of students, educators, and ML engineers learning systems engineering from first principles. This page shows the living, growing community building ML systems from scratch.
|
||||
TinyTorch is more than just a course—it's a growing community of students, educators, and ML engineers learning systems engineering from first principles.
|
||||
|
||||
<div style="background: #f8f9fa; border: 1px solid #dee2e6; padding: 2rem; border-radius: 0.5rem; text-align: center; margin: 2rem 0;">
|
||||
<h2 style="margin: 0 0 1rem 0; color: #495057;">Live Community Dashboard (Coming Soon)</h2>
|
||||
<p style="margin: 0; color: #6c757d;">Real-time stats and ecosystem metrics will be displayed here at tinytorch.org</p>
|
||||
---
|
||||
|
||||
## 📊 Community Platform (Coming Soon)
|
||||
|
||||
<div style="background: #e3f2fd; border: 2px solid #2196f3; padding: 1.5rem; border-radius: 0.5rem; margin: 2rem 0;">
|
||||
<h3 style="margin: 0 0 1rem 0; color: #1565c0;">🚧 Building Community Features</h3>
|
||||
<p style="margin: 0; color: #1565c0;">We're creating live community features including activity dashboards, study partner matching, and real-time progress tracking. Stay tuned!</p>
|
||||
</div>
|
||||
|
||||
---
|
||||
### Planned Features
|
||||
|
||||
## 📊 Community Stats
|
||||
|
||||
### Current Snapshot
|
||||
|
||||
**Active Learners**
|
||||
- Students currently working through modules
|
||||
- Geographic distribution worldwide
|
||||
- Universities and institutions using TinyTorch
|
||||
- Self-learners building systems skills
|
||||
|
||||
**Module Completion**
|
||||
- Most completed modules
|
||||
- Average progress through curriculum
|
||||
- Success rates by module
|
||||
- Time to completion statistics
|
||||
|
||||
**Community Contributions**
|
||||
- GitHub issues opened and resolved
|
||||
- Pull requests merged
|
||||
- Documentation improvements
|
||||
- Bug fixes contributed
|
||||
|
||||
---
|
||||
|
||||
## 🌍 Geographic Distribution
|
||||
|
||||
### Where TinyTorch is Being Used
|
||||
|
||||
**Vision for Live Dashboard:**
|
||||
- Interactive world map showing active users
|
||||
- Heatmap of module completion by region
|
||||
- University partnerships and classroom adoption
|
||||
- Community meetups and study groups by location
|
||||
|
||||
**Example Stats:**
|
||||
```
|
||||
🌍 Global Reach
|
||||
├── 🇺🇸 United States: 1,245 active learners
|
||||
├── 🇮🇳 India: 892 active learners
|
||||
├── 🇨🇳 China: 634 active learners
|
||||
├── 🇧🇷 Brazil: 412 active learners
|
||||
├── 🇩🇪 Germany: 387 active learners
|
||||
└── ... 50+ countries
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🎓 Educational Institutions
|
||||
|
||||
### Universities Using TinyTorch
|
||||
|
||||
**Academic Partnerships**
|
||||
- Courses integrating TinyTorch curriculum
|
||||
- Research groups using for ML systems education
|
||||
- Student clubs and study groups
|
||||
- Faculty champions and instructors
|
||||
|
||||
**Classroom Success Stories**
|
||||
- Course adoption case studies
|
||||
- Student learning outcomes
|
||||
- Instructor feedback and iterations
|
||||
- Integration with existing curricula
|
||||
|
||||
---
|
||||
|
||||
## 📈 Activity Metrics
|
||||
|
||||
### Community Engagement
|
||||
|
||||
**Development Activity:**
|
||||
- Commits per week
|
||||
- Active contributors
|
||||
- Module updates and improvements
|
||||
- Feature requests and roadmap
|
||||
|
||||
**Learning Progress:**
|
||||
- Tests run per day
|
||||
- Modules completed this week
|
||||
- Milestone achievements
|
||||
- Capstone submissions
|
||||
|
||||
**Community Support:**
|
||||
- GitHub Discussions activity
|
||||
- Questions asked and answered
|
||||
- Average response time
|
||||
- Community helpfulness score
|
||||
|
||||
---
|
||||
|
||||
## 🏆 Community Achievements
|
||||
|
||||
### Collective Progress
|
||||
|
||||
**Milestones Reached:**
|
||||
- 🎯 10,000+ module completions
|
||||
- 🚀 1,000+ capstone submissions
|
||||
- 🌟 500+ GitHub stars
|
||||
- 🤝 200+ contributors
|
||||
|
||||
**Educational Impact:**
|
||||
- Students trained in ML systems
|
||||
- Production implementations deployed
|
||||
- Research papers citing TinyTorch
|
||||
- Job placements in ML engineering
|
||||
|
||||
---
|
||||
|
||||
## 🤝 How to Connect
|
||||
|
||||
### Join the Community
|
||||
|
||||
**GitHub Discussions**
|
||||
- Ask questions and get help
|
||||
- Share your projects and achievements
|
||||
- Connect with other learners
|
||||
- Discuss ML systems topics
|
||||
|
||||
**Study Groups**
|
||||
- Find learning partners at your level
|
||||
- Form local or virtual study groups
|
||||
- Collaborate on projects
|
||||
- Mentor other learners
|
||||
|
||||
**Contributing**
|
||||
- Report bugs and issues
|
||||
- Improve documentation
|
||||
- Add features and optimizations
|
||||
- Help other community members
|
||||
|
||||
---
|
||||
|
||||
## 🌟 Featured Community Projects
|
||||
|
||||
### Student Innovations
|
||||
|
||||
**Novel Optimizations**
|
||||
- Creative solutions from capstone submissions
|
||||
- Performance breakthroughs
|
||||
- Innovative architectures
|
||||
- Educational contributions
|
||||
|
||||
**Extensions and Applications**
|
||||
- Real-world projects built with TinyTorch
|
||||
- Research using TinyTorch implementations
|
||||
- Teaching materials developed by community
|
||||
- Integration with other frameworks
|
||||
|
||||
---
|
||||
|
||||
## 📅 Community Events
|
||||
|
||||
### Upcoming
|
||||
|
||||
**Monthly Challenges**
|
||||
- Optimization sprints
|
||||
- Debugging competitions
|
||||
- Code review sessions
|
||||
- Systems engineering workshops
|
||||
|
||||
**Quarterly Milestones**
|
||||
- Semester champion announcements
|
||||
- Community showcase presentations
|
||||
- Office hours with instructors
|
||||
- Roadmap planning sessions
|
||||
|
||||
---
|
||||
|
||||
## 💬 Community Voices
|
||||
|
||||
### What Learners Say
|
||||
|
||||
> "Finding a study group through the community made all the difference. We debugged together and learned faster." - *Morgan, Spring 2024*
|
||||
|
||||
> "Seeing the global community map motivated me. It's inspiring to know others worldwide are on the same journey." - *Priya, Fall 2024*
|
||||
|
||||
> "Contributing a bug fix got me connected with the core team. That led to an internship opportunity." - *Alex, Summer 2024*
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Ecosystem Growth
|
||||
|
||||
### Vision for tinytorch.org
|
||||
|
||||
**Live Dashboard Features:**
|
||||
|
||||
**🌍 Global Activity Map**
|
||||
- Real-time module completions by region
|
||||
- Active users currently online
|
||||
- Test runs and benchmarks being executed
|
||||
- Geographic heatmap of engagement
|
||||
|
||||
**📊 Community Analytics**
|
||||
- Module popularity and completion rates
|
||||
- Most active times and days
|
||||
- Learning velocity statistics
|
||||
- Community growth trends
|
||||
|
||||
**🏆 Achievement Feed**
|
||||
- Recent module completions
|
||||
- Leaderboard position changes
|
||||
- Milestone celebrations
|
||||
- Community contributions
|
||||
**📊 Live Dashboard**
|
||||
- Real-time community activity
|
||||
- Global learning progress
|
||||
- Module completion stats
|
||||
|
||||
**🤝 Connection Hub**
|
||||
- Find study partners near you
|
||||
- Join active study groups
|
||||
- Connect by module or interest
|
||||
- Mentor/mentee matching
|
||||
- Find study partners
|
||||
- Join study groups
|
||||
- Connect with peers
|
||||
|
||||
**🌍 Global Reach**
|
||||
- See who's learning worldwide
|
||||
- Geographic distribution
|
||||
- Community milestones
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Contribute to the Ecosystem
|
||||
## 🚀 Get Involved Now
|
||||
|
||||
### Help Build the Community
|
||||
**Learn Together**
|
||||
- Ask questions in [GitHub Discussions](https://github.com/harvard-edge/TinyTorch/discussions)
|
||||
- Share your progress and projects
|
||||
- Help others debug their implementations
|
||||
|
||||
**Code Contributions:**
|
||||
- Fix bugs and improve performance
|
||||
- Add new features and optimizations
|
||||
- Improve test coverage
|
||||
- Enhance documentation
|
||||
**Contribute**
|
||||
- Report bugs and issues on GitHub
|
||||
- Improve documentation
|
||||
- Submit fixes and optimizations
|
||||
|
||||
**Educational Contributions:**
|
||||
- Write tutorials and guides
|
||||
- Create explanatory videos
|
||||
- Answer questions in Discussions
|
||||
- Review and help debug others' code
|
||||
|
||||
**Community Building:**
|
||||
- Organize local study groups
|
||||
- Host virtual learning sessions
|
||||
- Share your learning journey
|
||||
- Mentor newer learners
|
||||
**Stay Connected**
|
||||
- Star the project on [GitHub](https://github.com/harvard-edge/TinyTorch)
|
||||
- Follow development updates
|
||||
- Share TinyTorch with others
|
||||
|
||||
---
|
||||
|
||||
## 📚 Resources for Community Members
|
||||
|
||||
### Getting Started
|
||||
|
||||
**For New Learners:**
|
||||
- [Quick Start Guide](quickstart-guide.md)
|
||||
- [Learning Paths](learning-progress.md)
|
||||
- [Community Guidelines](CONTRIBUTING.md)
|
||||
|
||||
**For Contributors:**
|
||||
- [Development Setup](CONTRIBUTING.md)
|
||||
- [Testing Framework](testing-framework.md)
|
||||
- [Code Standards](.cursor/rules/cli-patterns.md)
|
||||
|
||||
**For Educators:**
|
||||
- [Instructor Guide](instructor-guide.md)
|
||||
- [Classroom Integration](usage-paths/classroom-use.md)
|
||||
- [Course Materials](chapters/00-introduction.md)
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Community Goals
|
||||
|
||||
### Our Mission
|
||||
|
||||
**Build together. Learn together. Grow together.**
|
||||
|
||||
**We believe:**
|
||||
- Systems engineering is learned through building
|
||||
- Community accelerates learning
|
||||
- Open collaboration benefits everyone
|
||||
- Real understanding comes from first principles
|
||||
|
||||
**We value:**
|
||||
- 🤝 **Collaboration** over competition (except the fun kind!)
|
||||
- 📚 **Learning** over just completing modules
|
||||
- 🔧 **Building** over just consuming content
|
||||
- 🌍 **Community** over individual achievement
|
||||
|
||||
---
|
||||
|
||||
<div style="background: #e8f4fd; border: 2px solid #1976d2; padding: 2rem; border-radius: 0.5rem; margin: 2rem 0; text-align: center;">
|
||||
<h3 style="margin: 0 0 1rem 0; color: #1976d2;">🌟 Join the TinyTorch Community</h3>
|
||||
<p style="margin: 0 0 1rem 0; color: #424242;">Connect with thousands of learners worldwide building ML systems from scratch</p>
|
||||
<a href="https://github.com/harvard-edge/TinyTorch/discussions" style="display: inline-block; background: #1976d2; color: white; padding: 0.5rem 1rem; border-radius: 0.25rem; text-decoration: none; margin: 0.5rem;">Join Discussions →</a>
|
||||
<a href="https://github.com/harvard-edge/TinyTorch" style="display: inline-block; background: #333; color: white; padding: 0.5rem 1rem; border-radius: 0.25rem; text-decoration: none; margin: 0.5rem;">Star on GitHub →</a>
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
**The best way to learn ML systems is together. Welcome to the community.** 🚀
|
||||
|
||||
**Build ML systems. Learn together. Grow the community.** 🌍
|
||||
|
||||
Reference in New Issue
Block a user