mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-01 06:07:31 -05:00
45a9cef548
- Removed 01_setup module (archived to archive/setup_module) - Renumbered all modules: tensor is now 01, activations is 02, etc. - Added tito setup command for environment setup and package installation - Added numeric shortcuts: tito 01, tito 02, etc. for quick module access - Fixed view command to find dev files correctly - Updated module dependencies and references - Improved user experience: immediate ML learning instead of boring setup
162 lines
6.5 KiB
Markdown
162 lines
6.5 KiB
Markdown
# 🔥 Module: Setup
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## 📊 Module Info
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- **Difficulty**: ⭐ Beginner
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- **Time Estimate**: 1-2 hours
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- **Prerequisites**: Basic Python knowledge
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- **Next Steps**: Tensor module
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Welcome to TinyTorch! This foundational module introduces the complete development workflow that powers every subsequent module. You'll master the nbdev notebook-to-Python workflow, implement your first TinyTorch components, and establish the coding patterns used throughout the entire course.
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## 🎯 Learning Objectives
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By the end of this module, you will be able to:
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- **Master the nbdev workflow**: Write code with `#| export` directives and understand the notebook-to-package pipeline
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- **Implement system utilities**: Build functions for system information collection and developer profiles
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- **Use TinyTorch CLI tools**: Run tests, sync modules, and check development progress
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- **Write production-ready code**: Follow professional patterns for error handling, testing, and documentation
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- **Establish development rhythm**: Understand the build → test → iterate cycle that drives all TinyTorch modules
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## 🧠 Build → Use → Reflect
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This module follows TinyTorch's **Build → Use → Reflect** framework:
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1. **Build**: Implement core utilities (hello functions, system info, developer profiles)
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2. **Use**: Apply these components in real development workflows and testing scenarios
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3. **Master**: Understand how this foundation supports all advanced TinyTorch modules and establish professional development habits
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## 📚 What You'll Build
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### Core Functions
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```python
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# Welcome and basic operations
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hello_tinytorch() # ASCII art and course introduction
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add_numbers(2, 3) # Foundation arithmetic operations
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# System information utilities
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info = SystemInfo()
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print(f"Platform: {info.platform}")
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print(f"Compatible: {info.is_compatible()}")
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# Developer profile management
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profile = DeveloperProfile(name="Your Name", affiliation="University")
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print(profile.get_full_profile())
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```
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### System Information Class
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- **Platform detection**: Python version, operating system, machine architecture
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- **Compatibility checking**: Verify minimum requirements for TinyTorch development
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- **Environment validation**: Ensure proper setup for course progression
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### Developer Profile Class
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- **Personalized signatures**: Professional code attribution and contact information
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- **ASCII art integration**: Custom flame art loading with graceful fallbacks
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- **Educational customization**: Personalize your TinyTorch learning experience
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## 🚀 Getting Started
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### Prerequisites
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Ensure you have completed the TinyTorch installation and environment setup:
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```bash
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# Activate TinyTorch environment
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source bin/activate-tinytorch.sh
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# Verify installation
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tito doctor
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```
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### Development Workflow
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1. **Open the development notebook**: `modules/source/01_setup/setup_dev.py`
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2. **Follow the guided implementation**: Complete TODO sections with provided scaffolding
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3. **Export your code**: `tito export --module setup`
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4. **Test your implementation**: `tito test --module setup`
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5. **Verify integration**: `tito nbdev build` to ensure package compatibility
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## 🧪 Testing Your Implementation
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### Comprehensive Test Suite
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Run the full test suite to verify your implementation:
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```bash
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# TinyTorch CLI (recommended)
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tito test --module setup
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# Direct pytest execution
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python -m pytest tests/ -k setup -v
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```
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### Test Coverage (20 Tests)
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- ✅ **Function execution**: All functions run without errors
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- ✅ **Output validation**: Correct content and formatting
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- ✅ **Arithmetic operations**: Basic, negative, and floating-point math
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- ✅ **System information**: Platform detection and compatibility
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- ✅ **Developer profiles**: Default and custom configurations
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- ✅ **ASCII art handling**: File loading and fallback behavior
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- ✅ **Error recovery**: Graceful handling of missing files
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- ✅ **Integration testing**: All components work together
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### Inline Testing
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The module includes educational inline tests that run during development:
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```python
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# Example inline test output
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🔬 Unit Test: SystemInfo functionality...
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✅ System detection works
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✅ Compatibility checking works
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📈 Progress: SystemInfo ✓
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```
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## 🎯 Key Concepts
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### Real-World Applications
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- **Development Environment Management**: Like PyTorch's system compatibility checking
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- **Professional Code Attribution**: Similar to open-source project contributor systems
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- **Educational Scaffolding**: Mirrors industry onboarding and training workflows
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- **System Validation**: Foundation for deployment compatibility (used in modules 12-14)
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### Core Programming Patterns
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- **NBDev Integration**: Write once in notebooks, deploy everywhere as Python packages
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- **Export Directives**: Strategic use of `#| export` for clean package structure
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- **Error Handling**: Graceful fallbacks for missing resources and system incompatibilities
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- **Object-Oriented Design**: Classes with clear responsibilities and professional interfaces
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- **Testing Philosophy**: Comprehensive coverage with both unit and integration approaches
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### TinyTorch Foundation
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This module establishes patterns used throughout the course:
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- **Module → Package Mapping**: `setup_dev.py` → `tinytorch.core.setup`
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- **Development Workflow**: Edit → Export → Test → Iterate cycle
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- **Educational Structure**: Guided implementation with instructor solutions
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- **Professional Standards**: Production-ready code with full test coverage
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## 🎉 Ready to Build?
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You're about to establish the foundation that will power your entire TinyTorch journey! This module teaches the development workflow mastery that professional ML engineers use daily.
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Every advanced concept you'll learn - from tensors to optimizers to MLOps - builds on the solid patterns you're about to implement here. Take your time, test thoroughly, and enjoy building something that really works!
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```{grid} 3
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:gutter: 3
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:margin: 2
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{grid-item-card} 🚀 Launch Builder
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:link: https://mybinder.org/v2/gh/VJProductions/TinyTorch/main?filepath=modules/source/01_setup/setup_dev.py
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:class-title: text-center
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:class-body: text-center
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Interactive development environment
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{grid-item-card} 📓 Open in Colab
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:link: https://colab.research.google.com/github/VJProductions/TinyTorch/blob/main/modules/source/01_setup/setup_dev.ipynb
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:class-title: text-center
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:class-body: text-center
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Google Colab notebook
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{grid-item-card} 👀 View Source
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:link: https://github.com/VJProductions/TinyTorch/blob/main/modules/source/01_setup/setup_dev.py
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:class-title: text-center
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:class-body: text-center
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Browse the code on GitHub
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``` |