Triton-Inference-Server-Deployment-with-ONNX-Models

Triton Inference Server Deployment with ONNX Models

This repository provides an example configuration for deploying models using the Triton Inference Server. The Triton Inference Server is a versatile inference serving platform that supports multiple deep learning frameworks.

Prerequisites

Docker installed on your machine Basic knowledge of Docker and deep learning frameworks (PyTorch, TensorFlow, ONNX Runtime, etc.)

Getting Started

Follow the steps below to configure and start the Triton Inference Server on your local machine.

Step 1: Clone the Repository

Clone this repository to your local machine:

• git clone https://github.com/tamanna18/Triton-Inference-Server-Deployment-with-ONNX-Models/tree/main

• cd Triton-Inference-Server-Deployment-with-ONNX-Models

Step 2: Set up the Model Repository

The Triton Inference Server requires a model repository where your models will be stored. In this repository, we will use the /models directory as the model repository.

Replace <model_repository_path> with the absolute path to your desired model repository directory in the following command:

• docker run --rm -p 8000:8000 --name triton-server-container -v "<model_repository_path>:/models" nvcr.io/nvidia/tritonserver:21.06-py3 bash

This command starts a Docker container and mounts the model repository directory to the /models directory within the container.

Step 3: Start the Triton Inference Server

Inside the Docker container, run the following command to start the Triton Inference Server:

• tritonserver --model-repository=/models

The server will start and display log output indicating the initialization of different backends (PyTorch, TensorFlow, ONNX Runtime, etc.). Note any warnings or errors that may occur during startup.

Step 4: Test the Server

To test if the Triton Inference Server is running successfully, open a new terminal window and run the following command:

• curl -v http://localhost:8000/v2/models

You should receive a response containing information about the available models in the model repository

Additional Configuration

• If you have GPU support on your machine, you can use the --gpus all flag when running the Docker command to enable GPU functionality.
• Make sure to specify the correct model repository path in the Docker command and ensure that the model repository directory contains the necessary model files and a config.pbtxt file for each model.
• Refer to the Triton Inference Server documentation for more advanced configuration options and deployment scenarios.

Contributing

Contributions to this repository are welcome! If you have any suggestions, bug fixes, or improvements, please feel free to submit a pull request.