PyTorch Library

8.

Model Persistence and Deployment

8.1.

Model Serialization

8.1.1.

State Dictionary Approach

8.1.1.1.

model.state_dict()

8.1.1.2.

Parameter-only Saving

8.1.1.3.

Optimizer State Saving

8.1.2.

Complete Model Serialization

8.1.2.1.

torch.save() Usage

8.1.2.2.

Architecture and Weights

8.1.2.3.

Compatibility Considerations

8.1.3.

Partial Model Loading

8.1.3.1.

Selective Parameter Loading

8.1.3.2.

Transfer Learning Applications

8.1.3.3.

Fine-tuning Scenarios

8.2.

Checkpointing Strategies

8.2.1.

Training Checkpoints

8.2.1.1.

Periodic Saving

8.2.1.2.

Best Model Preservation

8.2.1.3.

Resume Training Capability

8.2.2.

Checkpoint Management

8.2.2.1.

File Organization

8.2.2.2.

Version Control

8.2.2.3.

Storage Optimization

8.2.3.

Distributed Checkpointing

8.2.3.1.

Multi-GPU Scenarios

8.2.3.2.

Synchronization Requirements

8.2.3.3.

Fault Tolerance

8.3.

Model Export and Conversion

8.3.1.

TorchScript Compilation

8.3.1.1.

torch.jit.script

8.3.1.2.

torch.jit.trace

8.3.1.3.

Production Optimization

8.3.2.

8.3.2.1.

Cross-framework Compatibility

8.3.2.2.

Inference Engine Integration

8.3.2.3.

Model Optimization

8.3.3.

Mobile Deployment

8.3.3.1.

8.3.3.2.

Model Quantization

8.3.3.3.

Size Optimization

8.4.

Production Deployment

8.4.1.

Inference Optimization

8.4.1.1.

Batch Processing

8.4.1.2.

Memory Management

8.4.1.3.

Latency Optimization

8.4.2.

Serving Infrastructure

8.4.2.1.

REST API Development

8.4.2.2.

Containerization

8.4.2.3.

Scalability Considerations

8.4.3.

Model Monitoring

8.4.3.1.

Performance Tracking

8.4.3.2.

Drift Detection

8.4.3.3.

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9. Advanced PyTorch Features