Deep Learning — Deployment Toolkit

The value of these toolkits is best illustrated through concrete examples. Consider deploying a YOLOv8 object detection model on a Jetson Orin edge device. Using raw PyTorch, one might achieve 10 FPS at FP32. By passing the model through TensorRT, performing INT8 quantization with calibration, and enabling layer fusion, the same model can exceed 100 FPS—a tenfold improvement, all without changing a single line of model architecture code.

Another powerhouse from NVIDIA, Triton supports multiple frameworks (TensorFlow, PyTorch, ONNX) and allows you to serve different models simultaneously on a single GPU or CPU. deep learning deployment toolkit

These platforms help track model versions and performance metrics, ensuring that you can rollback or update models with full visibility. Conclusion The value of these toolkits is best illustrated

Deploying to a smartphone or an IoT sensor requires a specialized toolkit focused on power efficiency and minimal memory footprint. By passing the model through TensorRT, performing INT8