mirror of
https://github.com/MLSysBook/TinyTorch.git
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b92642a96e
- Add module.yaml files for setup, tensor, activations, layers, and autograd modules - Enhanced tito status command with --metadata flag for rich information display - Created metadata schema with learning objectives, dependencies, components, and more - Added metadata generation script (bin/generate_module_metadata.py) - Comprehensive documentation in docs/development/module-metadata-system.md - Status command now shows module status, difficulty, time estimates, and detailed metadata - Supports dependency tracking, component-level status, and educational information - Enables rich CLI experience with structured module information
110 lines
3.0 KiB
YAML
110 lines
3.0 KiB
YAML
# TinyTorch Module Metadata
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# This file contains structured information about the module for CLI tools and documentation
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# Basic Information
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name: "setup"
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title: "Setup - TinyTorch Development Workflow"
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description: "Learn the complete TinyTorch development workflow while introducing fundamental programming concepts"
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version: "1.0.0"
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author: "TinyTorch Team"
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last_updated: "2024-12-19"
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# Module Status
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status: "complete" # complete, in_progress, not_started, deprecated
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implementation_status: "stable" # stable, beta, alpha, experimental
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# Learning Information
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learning_objectives:
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- "Understand the nbdev notebook-to-Python workflow"
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- "Write your first TinyTorch code with #| export directives"
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- "Implement system information collection and developer profiles"
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- "Run tests and use the CLI tools"
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- "Get comfortable with the development rhythm"
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key_concepts:
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- "NBDev workflow"
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- "Export directives"
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- "Module → Package mapping"
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- "Teaching vs. Building"
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- "Student implementation"
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# Dependencies
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dependencies:
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prerequisites: [] # No prerequisites for setup module
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builds_on: [] # No previous modules required
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enables: ["tensor", "activations", "layers"] # Modules that depend on this
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# Educational Metadata
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difficulty: "beginner" # beginner, intermediate, advanced
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estimated_time: "2-3 hours"
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pedagogical_pattern: "Build → Use → Understand"
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# Implementation Details
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components:
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- name: "hello_tinytorch"
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type: "function"
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description: "Display ASCII art and welcome message"
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status: "complete"
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- name: "add_numbers"
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type: "function"
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description: "Basic arithmetic (foundation of ML operations)"
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status: "complete"
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- name: "SystemInfo"
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type: "class"
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description: "Collect and display Python version, platform, and machine info"
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status: "complete"
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- name: "DeveloperProfile"
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type: "class"
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description: "Personalized developer information and signatures"
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status: "complete"
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# Package Export Information
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exports_to: "tinytorch.core.utils"
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export_directive: "core.utils"
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# Testing Information
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test_coverage: "comprehensive" # comprehensive, partial, minimal, none
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test_count: 20
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test_categories:
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- "Function execution"
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- "Output validation"
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- "Arithmetic operations"
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- "System information"
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- "Developer profiles"
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- "ASCII art handling"
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- "Error recovery"
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- "Integration testing"
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# File Structure
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required_files:
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- "setup_dev.py"
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- "setup_dev.ipynb"
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- "tests/test_setup.py"
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- "README.md"
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- "tinytorch_flame.txt"
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# Systems Focus
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systems_concepts:
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- "File I/O operations"
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- "Platform detection"
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- "Error handling"
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- "Code organization"
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- "Testing patterns"
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# Real-world Applications
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applications:
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- "Development environment setup"
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- "System compatibility checking"
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- "Professional code attribution"
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- "ASCII art customization"
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# Next Steps
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next_modules: ["tensor"]
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completion_criteria:
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- "All tests pass"
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- "Can create custom developer profile"
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- "Understand nbdev workflow"
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- "Ready for tensor module" |