Model persistence is crucial for deploying trained models, resuming training, and sharing work. PyTorch offers flexible approaches for saving model state.
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State Dict
Save/load model parameters only (recommended approach)
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Whole Model
Save entire model object (less flexible but simpler)
⏱️
Checkpoint
Save training state including optimizer and scheduler
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TorchScript
Production-ready serialization for deployment
Loading Strategies
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Basic Loading
Load saved models for inference or continued training
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Transfer Learning
Load pre-trained models and adapt for new tasks
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Partial Loading
Load specific layers or handle architecture changes
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Device Mapping
Handle GPU/CPU device differences during loading
Model Persistence Best Practices
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Production Deployment
Learn TorchScript and ONNX export for production environments with examples
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Model Versioning
Strategies for managing model versions and metadata tracking
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Model Compression
Techniques for reducing model size while maintaining performance