How to Deploy an AI Model in Python with PyTriton

Introduction

• PyTriton is a Python interface that allows developers to use NVIDIA Triton Inference Server to serve AI models within Python code.
• It enables rapid prototyping and testing of ML models while achieving performance and efficiency with high GPU utilization.
• PyTriton simplifies the deployment process by providing a familiar Flask-like interface.

Benefits of PyTriton

• PyTriton allows developers to bring up Triton Inference Server with a single line of code.
• It eliminates the need for setting up model repositories and model format conversion.
• Existing inference pipeline code can be used without modification.
• PyTriton supports various decorators to adapt model input.
• It provides the benefits of Triton Inference Server in a Python development environment.

Example Code

• Online Learning: PyTriton can be used to continuously learn from new data in production using the MNIST dataset.
• Hugging Face OPT Model: PyTriton can also be used with Hugging Face OPT model in JAX, with inference performed on multiple nodes.

Summary

• PyTriton is a simple interface for using Triton Inference Server in Python.
• It enables rapid prototyping and testing of ML models with high performance and efficiency.
• PyTriton offers a Flask-like interface and supports features such as dynamic batching and concurrent model execution.
• Try PyTriton using the provided code examples or with your own model.