Part 10/13:
Mig showed code snippets detailing conversion steps, emphasizing that the most significant performance improvements occur after converting models to ONNX and applying Tensority optimizations, achieving 2x-3x speedups and reduced model sizes.
Practical Hands-On: Vision Transformer Deployment
A detailed example illustrated deploying a Vision Transformer (ViT):
Load a pretrained ViT model in PyTorch.
Export to ONNX.
Convert to TensorRT/FP16 precision for faster inference.
Structure the Triton model repository with configuration files specifying model inputs, outputs, batch sizes, and deployment options.
Use Triton API to serve the model, set dynamic batching parameters, and monitor GPU utilization.