mirror of
https://github.com/MLSysBook/TinyTorch.git
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4f9c6e40bd
- Added educational metadata (difficulty, time_estimate) to all module.yaml files - Updated convert_readmes.py to read from YAML instead of hardcoded mappings - Standardized difficulty progression: ⭐ → ⭐⭐ → ⭐⭐⭐ → ⭐⭐⭐⭐ → ⭐⭐⭐⭐⭐🥷 - Fixed path resolution for YAML reading in book build process - Eliminated duplication: single source of truth for educational metadata - Capstone gets special ninja treatment (⭐⭐⭐⭐⭐🥷) as beyond-expert level
43 lines
1.2 KiB
YAML
43 lines
1.2 KiB
YAML
# TinyTorch Module Metadata
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# Essential system information for CLI tools and build systems
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name: "11_kernels"
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title: "Kernels - Hardware-Aware Optimization"
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description: "Custom operations, performance optimization, and hardware-aware computing for ML systems"
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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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"00_setup", "01_tensor", "02_activations", "03_layers",
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"04_networks", "05_cnn", "06_dataloader", "07_autograd",
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"08_optimizers", "09_training", "10_compression"
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]
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enables: ["12_benchmarking", "13_mlops"]
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# Package Export - What gets built into tinytorch package
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exports_to: "tinytorch.core.kernels"
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# File Structure - What files exist in this module
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files:
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dev_file: "kernels_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: "8-10 hours"
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# Components - What's implemented in this module
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components:
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- "matmul_custom"
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- "relu_custom"
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- "conv2d_custom"
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- "matmul_vectorized"
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- "matmul_cache_optimized"
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- "matmul_parallel"
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- "quantized_matmul"
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- "sparse_matmul"
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- "pruned_conv2d"
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- "KernelProfiler"
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- "PerformanceBenchmark"
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- "HardwareProfiler" |