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
107 lines
2.9 KiB
YAML
107 lines
2.9 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: "tensor"
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title: "Tensor - Core Data Structure"
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description: "Implement the fundamental data structure that powers all ML systems - N-dimensional arrays with ML-specific operations"
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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 tensors as N-dimensional arrays with ML-specific operations"
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- "Implement a complete Tensor class with arithmetic operations"
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- "Handle shape management, data types, and memory layout"
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- "Build the foundation for neural networks and automatic differentiation"
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key_concepts:
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- "N-dimensional arrays"
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- "Shape management"
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- "Element-wise operations"
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- "Broadcasting"
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- "Memory layout"
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- "Data types"
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# Dependencies
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dependencies:
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prerequisites: ["setup"]
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builds_on: ["setup"]
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enables: ["activations", "layers", "networks", "autograd"]
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# Educational Metadata
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difficulty: "intermediate" # beginner, intermediate, advanced
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estimated_time: "4-6 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: "Tensor"
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type: "class"
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description: "Core tensor class with shape, data, and operations"
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status: "complete"
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- name: "arithmetic_operations"
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type: "methods"
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description: "Addition, subtraction, multiplication, division"
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status: "complete"
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- name: "shape_operations"
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type: "methods"
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description: "Reshape, transpose, squeeze, unsqueeze"
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status: "complete"
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- name: "reduction_operations"
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type: "methods"
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description: "Sum, mean, max, min along dimensions"
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status: "complete"
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# Package Export Information
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exports_to: "tinytorch.core.tensor"
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export_directive: "core.tensor"
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# Testing Information
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test_coverage: "comprehensive" # comprehensive, partial, minimal, none
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test_count: 25
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test_categories:
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- "Construction and properties"
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- "Arithmetic operations"
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- "Shape management"
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- "Reduction operations"
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- "Error handling"
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- "Edge cases"
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# File Structure
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required_files:
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- "tensor_dev.py"
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- "tensor_dev.ipynb"
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- "tests/test_tensor.py"
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- "README.md"
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# Systems Focus
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systems_concepts:
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- "Memory management"
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- "Data layout optimization"
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- "Numerical stability"
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- "Broadcasting semantics"
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- "Type safety"
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# Real-world Applications
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applications:
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- "Neural network computations"
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- "Scientific computing"
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- "Computer vision"
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- "Natural language processing"
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# Next Steps
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next_modules: ["activations", "layers"]
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completion_criteria:
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- "All tests pass"
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- "Can perform basic tensor operations"
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- "Understand tensor shapes and broadcasting"
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- "Ready for neural network layers" |