mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-07-24 14:04:34 -05:00
Vijay Janapa Reddi
d04d66a716
Major Educational Framework Enhancements: • Deploy interactive NBGrader text response questions across ALL modules • Replace passive question lists with active 150-300 word student responses • Enable comprehensive ML Systems learning assessment and grading TinyGPT Integration (Module 16): • Complete TinyGPT implementation showing 70% component reuse from TinyTorch • Demonstrates vision-to-language framework generalization principles • Full transformer architecture with attention, tokenization, and generation • Shakespeare demo showing autoregressive text generation capabilities Module Structure Standardization: • Fix section ordering across all modules: Tests → Questions → Summary • Ensure Module Summary is always the final section for consistency • Standardize comprehensive testing patterns before educational content Interactive Question Implementation: • 3 focused questions per module replacing 10-15 passive questions • NBGrader integration with manual grading workflow for text responses • Questions target ML Systems thinking: scaling, deployment, optimization • Cumulative knowledge building across the 16-module progression Technical Infrastructure: • TPM agent for coordinated multi-agent development workflows • Enhanced documentation with pedagogical design principles • Updated book structure to include TinyGPT as capstone demonstration • Comprehensive QA validation of all module structures Framework Design Insights: • Mathematical unity: Dense layers power both vision and language models • Attention as key innovation for sequential relationship modeling • Production-ready patterns: training loops, optimization, evaluation • System-level thinking: memory, performance, scaling considerations Educational Impact: • Transform passive learning to active engagement through written responses • Enable instructors to assess deep ML Systems understanding • Provide clear progression from foundations to complete language models • Demonstrate real-world framework design principles and trade-offs
39 lines
1.1 KiB
YAML
39 lines
1.1 KiB
YAML
# TinyTorch Module Metadata
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# Essential system information for CLI tools and build systems
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name: "capstone"
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title: "Capstone Project"
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description: "Optimize and extend your complete TinyTorch framework through systems engineering"
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# Dependencies - Used by CLI for module ordering and prerequisites
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dependencies:
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prerequisites: [
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"setup", "tensor", "activations", "layers", "networks", "cnn",
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"dataloader", "autograd", "optimizers", "training", "compression",
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"kernels", "benchmarking", "mlops"
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]
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enables: []
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# Package Export - What gets built into tinytorch package
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exports_to: "tinytorch.capstone"
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# File Structure - What files exist in this module
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files:
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dev_file: "capstone_dev.py"
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readme: "README.md"
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tests: "inline"
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# Educational Metadata
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difficulty: "⭐⭐⭐⭐⭐ 🥷"
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time_estimate: "Capstone Project"
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# Components - What's implemented in this module
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components:
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- "PerformanceProfiler"
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- "MemoryOptimizer"
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- "BatchNormalization"
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- "TransformerBlock"
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- "MultiGPUTraining"
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- "AdvancedOptimizer"
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- "FrameworkBenchmark"
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- "DeveloperTools" |