TinyTorch/capabilities

🚀 TinyTorch Capability Showcase

"Look what you built!" moments for students

This directory contains showcase files that demonstrate what students have accomplished after completing each module. These are not exercises - they're celebrations of achievement!

How to Use

After completing a module, run the corresponding showcase file to see your implementation in action:

# Method 1: Direct execution
python capabilities/01_tensor_operations.py
python capabilities/02_neural_intelligence.py
python capabilities/03_forward_inference.py
# ... and so on

# Method 2: Using tito (if available)
tito demo capability 01
tito demo capability 02
tito demo capability 03

Or run all available showcases:

# Run all showcases you've unlocked
for f in capabilities/*.py; do echo "Running $f"; python "$f"; echo; done

Philosophy

These showcases follow the "Look what you built!" philosophy:

• No additional coding required - Just run and watch
• Uses only your TinyTorch code - Demonstrates your actual implementations
• Visually impressive - Rich terminal output with colors and animations
• Achievement celebration - Makes progress tangible and exciting
• Quick and satisfying - 30 seconds to 2 minutes of pure awesomeness

Showcase Files

File	After Module	What It Shows
01_tensor_operations.py	02 (Tensor)	Matrix operations with ASCII visualization
02_neural_intelligence.py	03 (Activations)	How activations create intelligence
03_forward_inference.py	05 (Dense)	Real digit recognition with your network
04_image_processing.py	06 (Spatial)	Convolution edge detection
05_attention_visualization.py	07 (Attention)	Attention heatmaps
06_data_pipeline.py	09 (DataLoader)	Real CIFAR-10 data loading
07_full_training.py	11 (Training)	Live CNN training with progress bars
08_model_compression.py	12 (Compression)	Model size optimization
09_performance_profiling.py	14 (Benchmarking)	System performance analysis
10_production_systems.py	15 (MLOps)	Production deployment simulation
11_tinygpt_mastery.py	16 (TinyGPT)	Your GPT generating text!

Dependencies

Each showcase file imports only from your TinyTorch implementation:

from tinytorch.core.tensor import Tensor
from tinytorch.core.activations import ReLU
# etc.

Plus Rich for beautiful terminal output:

from rich.console import Console
from rich.progress import Progress
from rich.panel import Panel

Sample Weights and Data

The weights/ and data/ directories contain:

• Pre-trained weights for demo models
• Sample data for quick showcase runs
• All files are small and optimized for fast loading

Making Your Own Showcases

Want to create more capability showcases? Follow these guidelines:

1. Import only from tinytorch - Use what they built
2. Make it visual - Use Rich for colors, progress bars, ASCII art
3. Keep it short - 30 seconds to 2 minutes max
4. Celebrate achievement - End with congratulations
5. No user input required - Just run and watch

Example template:

from rich.console import Console
from rich.panel import Panel
from tinytorch.core.tensor import Tensor

console = Console()

def main():
    console.print(Panel.fit("🚀 YOUR CAPABILITY SHOWCASE", style="bold magenta"))
    
    # Show something impressive with their code
    tensor = Tensor([[1, 2], [3, 4]])
    result = tensor @ tensor  # Uses their implementation!
    
    console.print(f"✨ Result: {result}")
    console.print("\n🎉 YOU BUILT THIS! Amazing work!")

if __name__ == "__main__":
    main()

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Remember: These showcases exist to make your learning journey tangible and exciting. Each one proves that you're building real, working ML systems from scratch!