Inference speed is critical for production ML systems. This guide covers practical techniques for accelerating model inference.
Compilation Techniques
torch.compile
PyTorch 2.0's torch.compile provides automatic optimization through graph compilation, often achieving 2-3x speedups with minimal code changes.
TorchScript
TorchScript enables ahead-of-time compilation, removing Python overhead and enabling deployment in C++ environments.
Runtime Optimization
ONNX Runtime
ONNX Runtime provides highly optimized inference across multiple hardware platforms, often outperforming native PyTorch inference.
Mixed Precision
Using FP16 or INT8 precision can significantly reduce inference time and memory usage with minimal accuracy loss.
Hardware Considerations
Different optimization strategies work best on different hardware:
- GPU: Focus on batch processing and mixed precision
- CPU: Leverage ONNX Runtime and quantization
- Edge devices: Prioritize model size and quantization
Best Practices
- Profile before optimizing
- Measure end-to-end latency, not just model inference
- Consider accuracy-speed tradeoffs
- Test on target hardware