mirror of
https://github.com/MLSysBook/TinyTorch.git
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9b245fe5ea
- Add training_dev.py with comprehensive educational structure - Implement MeanSquaredError, CrossEntropyLoss, BinaryCrossEntropyLoss - Add Accuracy metric with extensible framework - Create Trainer class for complete training orchestration - Include comprehensive inline tests for all components - Add module.yaml with proper dependencies and metadata - Create detailed README.md with examples and applications - Add test_training_integration.py with real component integration tests - Follow TinyTorch NBDev educational pattern with Build → Use → Optimize - Ready for real-world training workflows with validation and monitoring
28 lines
852 B
YAML
28 lines
852 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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# 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" |