--- comments: true description: A step-by-step guide on integrating Ultralytics YOLOv8 with Triton Inference Server for scalable and high-performance deep learning inference deployments. keywords: YOLOv8, Triton Inference Server, ONNX, Deep Learning Deployment, Scalable Inference, Ultralytics, NVIDIA, Object Detection, Cloud Inferencing --- # Triton Inference Server with Ultralytics YOLOv8 The [Triton Inference Server](https://developer.nvidia.com/nvidia-triton-inference-server) (formerly known as TensorRT Inference Server) is an open-source software solution developed by NVIDIA. It provides a cloud inferencing solution optimized for NVIDIA GPUs. Triton simplifies the deployment of AI models at scale in production. Integrating Ultralytics YOLOv8 with Triton Inference Server allows you to deploy scalable, high-performance deep learning inference workloads. This guide provides steps to set up and test the integration.



Watch: Getting Started with NVIDIA Triton Inference Server.

## What is Triton Inference Server? Triton Inference Server is designed to deploy a variety of AI models in production. It supports a wide range of deep learning and machine learning frameworks, including TensorFlow, PyTorch, ONNX Runtime, and many others. Its primary use cases are: - Serving multiple models from a single server instance. - Dynamic model loading and unloading without server restart. - Ensemble inferencing, allowing multiple models to be used together to achieve results. - Model versioning for A/B testing and rolling updates. ## Prerequisites Ensure you have the following prerequisites before proceeding: - Docker installed on your machine. - Install `tritonclient`: ```bash pip install tritonclient[all] ``` ## Exporting YOLOv8 to ONNX Format Before deploying the model on Triton, it must be exported to the ONNX format. ONNX (Open Neural Network Exchange) is a format that allows models to be transferred between different deep learning frameworks. Use the `export` function from the `YOLO` class: ```python from ultralytics import YOLO # Load a model model = YOLO('yolov8n.pt') # load an official model # Export the model onnx_file = model.export(format='onnx', dynamic=True) ``` ## Setting Up Triton Model Repository The Triton Model Repository is a storage location where Triton can access and load models. 1. Create the necessary directory structure: ```python from pathlib import Path # Define paths triton_repo_path = Path('tmp') / 'triton_repo' triton_model_path = triton_repo_path / 'yolo' # Create directories (triton_model_path / '1').mkdir(parents=True, exist_ok=True) ``` 2. Move the exported ONNX model to the Triton repository: ```python from pathlib import Path # Move ONNX model to Triton Model path Path(onnx_file).rename(triton_model_path / '1' / 'model.onnx') # Create config file (triton_model_path / 'config.pdtxt').touch() ``` ## Running Triton Inference Server Run the Triton Inference Server using Docker: ```python import subprocess import time from tritonclient.http import InferenceServerClient # Define image https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver tag = 'nvcr.io/nvidia/tritonserver:23.09-py3' # 6.4 GB # Pull the image subprocess.call(f'docker pull {tag}', shell=True) # Run the Triton server and capture the container ID container_id = subprocess.check_output( f'docker run -d --rm -v {triton_repo_path}:/models -p 8000:8000 {tag} tritonserver --model-repository=/models', shell=True).decode('utf-8').strip() # Wait for the Triton server to start triton_client = InferenceServerClient(url='localhost:8000', verbose=False, ssl=False) # Wait until model is ready for _ in range(10): with contextlib.suppress(Exception): assert triton_client.is_model_ready(model_name) break time.sleep(1) ``` Then run inference using the Triton Server model: ```python from ultralytics import YOLO # Load the Triton Server model model = YOLO(f'http://localhost:8000/yolo', task='detect') # Run inference on the server results = model('path/to/image.jpg') ``` Cleanup the container: ```python # Kill and remove the container at the end of the test subprocess.call(f'docker kill {container_id}', shell=True) ``` --- By following the above steps, you can deploy and run Ultralytics YOLOv8 models efficiently on Triton Inference Server, providing a scalable and high-performance solution for deep learning inference tasks. If you face any issues or have further queries, refer to the [official Triton documentation](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html) or reach out to the Ultralytics community for support.