towards the next release. The current release is version 2.73.0

and corresponds to the 26.09 container release on NVIDIA GPU Cloud (NGC).

Triton Inference Server

Triton Inference Server is an open source inference serving software that streamlines AI inferencing. Triton enables teams to deploy any AI model from multiple deep learning and machine learning frameworks, including TensorRT, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton Inference Server supports inference across cloud, data center, edge and embedded devices on NVIDIA GPUs, x86 and ARM CPU, or AWS Inferentia. Triton Inference Server delivers optimized performance for many query types, including real time, batched, ensembles and audio/video streaming. Triton inference Server is part of NVIDIA AI Enterprise, a software platform that accelerates the data science pipeline and streamlines the development and deployment of production AI.

Major features include:

• Supports multiple deep learning

• Supports multiple machine learning

• Concurrent model

• Dynamic batching
• Sequence batching and

• Provides Backend API that

• Supports writing custom backends in python, a.k.a.

• Model pipelines using

• HTTP/REST and GRPC inference

• A C API and

• Metrics indicating GPU utilization, server

New to Triton Inference Server? Make use of these tutorials to begin your Triton journey!

Join the Triton and TensorRT community and stay current on the latest product updates, bug fixes, content, best practices, and more. Need enterprise support? NVIDIA global support is available for Triton Inference Server with the NVIDIA AI Enterprise software suite.

Serve a Model in 3 Easy Steps

# Step 1: Create the example model repository
git clone -b r26.09 https://github.com/triton-inference-server/server.git
cd server/docs/examples
./fetch_models.sh
Step 2: Launch triton from the NGC Triton container
docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:26.09-py3 tritonserver --model-repository=/models --model-control-mode explicit --load-model densenet_onnx
Step 3: Sending an Inference Request
In a separate console, launch the image_client example from the NGC Triton SDK container
docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:26.09-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
Inference should return the following
Image '/workspace/images/mug.jpg':
    15.346230 (504) = COFFEE MUG
    13.224326 (968) = CUP
    10.422965 (505) = COFFEEPOT

Examples and Tutorials

Check out NVIDIA LaunchPad for free access to a set of hands-on labs with Triton Inference Server hosted on NVIDIA infrastructure.

Specific end-to-end examples for popular models, such as ResNet, BERT, and DLRM are located in the NVIDIA Deep Learning Examples page on GitHub. The NVIDIA Developer Zone contains additional documentation, presentations, and examples.

Documentation

Build and Deploy

The recommended way to build and use Triton Inference Server is with Docker images.

• Install Triton Inference Server with Docker containers (Recommended)
• Install Triton Inference Server without Docker containers
• Build a custom Triton Inference Server Docker container
• Build Triton Inference Server from source
• Examples for deploying Triton Inference Server with Kubernetes and Helm on GCP,

• Secure Deployment Considerations

Using Triton

Preparing Models for Triton Inference Server

The first step in using Triton to serve your models is to place one or more models into a model repository. Depending on the type of the model and on what Triton capabilities you want to enable for the model, you may need to create a model configuration for the model.

• Add custom operations to Triton if needed by your model
• Enable model pipelining with Model Ensemble

• Optimize your models setting scheduling and batching

• Use the Model Analyzer tool

• Learn how to explicitly manage what models are available by loading and

Configure and Use Triton Inference Server

• Read the Quick Start Guide to run Triton Inference

• Triton supports multiple execution engines, called

• Not all the above backends are supported on every platform supported by Triton.

• Learn how to optimize performance using the

• Learn how to manage loading and unloading models in

• Send requests directly to Triton with the HTTP/REST JSON-based

Client Support and Examples

A Triton client application sends inference and other requests to Triton. The Python and C++ client libraries provide APIs to simplify this communication.

• Review client examples for C++,

• Configure HTTP

• Send input data (e.g. a jpeg image) directly to Triton in the body of an HTTP

Extend Triton

Triton Inference Server's architecture is specifically designed for modularity and flexibility

• Customize Triton Inference Server container for your use case
• Create custom backends

• Create decoupled backends and models that can send

• Use a Triton repository agent to add functionality

• Deploy Triton on Jetson and JetPack
• Use Triton on AWS

Additional Documentation

• FAQ
• User Guide
• Customization Guide
• Release Notes
• GPU, Driver, and CUDA Support

Contributing

Contributions to Triton Inference Server are more than welcome. To contribute please review the contribution guidelines. If you have a backend, client, example or similar contribution that is not modifying the core of Triton, then you should file a PR in the contrib repo.

Reporting problems, asking questions

We appreciate any feedback, questions or bug reporting regarding this project. When posting issues in GitHub, follow the process outlined in the Stack Overflow document. Ensure posted examples are:

• minimal – use as little code as possible that still produces the

• complete – provide all parts needed to reproduce the problem. Check

• verifiable – test the code you're about to provide to make sure it

For issues, please use the provided bug report and feature request templates.

For questions, we recommend posting in our community GitHub Discussions.

For more information

Please refer to the NVIDIA Developer Triton page for more information.