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https://github.com/MLSysBook/TinyTorch.git
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59d58718f9
✅ Renamed modules for clearer pedagogical flow: - 05_networks → 05_dense (multi-layer dense/fully connected networks) - 06_cnn → 06_spatial (convolutional networks for spatial patterns) - 06_attention → 07_attention (attention mechanisms for sequences) ✅ Shifted remaining modules down by 1: - 07_dataloader → 08_dataloader - 08_autograd → 09_autograd - 09_optimizers → 10_optimizers - 10_training → 11_training - 11_compression → 12_compression - 12_kernels → 13_kernels - 13_benchmarking → 14_benchmarking - 14_mlops → 15_mlops - 15_capstone → 16_capstone ✅ Updated module metadata (module.yaml files): - Updated names, descriptions, dependencies - Fixed prerequisite chains and enables relationships - Updated export paths to match new names New learner progression: Foundation → Individual Layers → Dense Networks → Spatial Networks → Attention Networks → Training Pipeline Perfect pedagogical flow: Build one layer → Stack dense layers → Add spatial patterns → Add attention mechanisms → Learn to train them all.
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" |