Model Compression

term_id: model_compression

Category: training_techniques

Definition

This category includes methods like pruning, quantization, and knowledge distillation aimed at shrinking model footprint while maintaining performance. It is essential for deploying complex AI models on devices with limited memory, storage, and processing power, enabling faster inference times and lower energy consumption for edge deployment scenarios.

Summary

Model compression refers to techniques that reduce the size and computational requirements of machine learning models.

Key Concepts

  • Quantization
  • Pruning
  • Knowledge Distillation
  • Inference Speed

Use Cases

  • Deploying models on mobile devices
  • Reducing cloud inference costs
  • Accelerating real-time video processing

Code Example

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import torch.quantization as quant
model = quant.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8)

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