mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-07-23 13:49:38 -05:00
Vijay Janapa Reddi
3bdfddca51
Clean, dependency-driven organization: - Part I (1-5): MLPs for XORNet - Part II (6-10): CNNs for CIFAR-10 - Part III (11-15): Transformers for TinyGPT Key improvements: - Dropped modules 16-17 (regularization/systems) to maintain scope - Moved normalization to module 13 (Part III where it's needed) - Created three CIFAR-10 examples: random, MLP, CNN - Each part introduces ONE major innovation (FC → Conv → Attention) CIFAR-10 now showcases progression: - test_random_baseline.py: ~10% (random chance) - train_mlp.py: ~55% (no convolutions) - train_cnn.py: ~60%+ (WITH Conv2D - shows why convolutions matter!) This follows actual ML history and each module is needed for its capstone.
32 lines
931 B
YAML
32 lines
931 B
YAML
# TinyTorch Module Metadata
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# Essential system information for CLI tools and build systems
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name: "training"
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title: "Training"
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description: "Neural network training loops, loss functions, and metrics"
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# Dependencies - Used by CLI for module ordering and prerequisites
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dependencies:
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prerequisites: ["setup", "tensor", "activations", "layers", "networks", "dataloader", "autograd", "optimizers"]
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enables: ["compression", "kernels", "benchmarking", "mlops"]
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# Package Export - What gets built into tinytorch package
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exports_to: "tinytorch.core.training"
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# File Structure - What files exist in this module
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files:
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dev_file: "training_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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- "MeanSquaredError"
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- "CrossEntropyLoss"
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- "BinaryCrossEntropyLoss"
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- "Accuracy"
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- "Trainer" |