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Triton Inference Server




You are currently on the main branch which tracks under-development progress towards the next release. The current release is version 2.45.0 and corresponds to the 24.04 container release on NVIDIA GPU Cloud (NGC).

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, TensorFlow, 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:

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 r24.04
cd server/docs/examples

# Step 2: Launch triton from the NGC Triton container
docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models tritonserver --model-repository=/models

# 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
/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

Please read the QuickStart guide for additional information regarding this example. The quickstart guide also contains an example of how to launch Triton on CPU-only systems. New to Triton and wondering where to get started? Watch the Getting Started video.

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.


Build and Deploy

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

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.

Configure and Use Triton Inference Server

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.

Extend Triton

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

Additional Documentation


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 same problem
  • complete – provide all parts needed to reproduce the problem. Check if you can strip external dependencies and still show the problem. The less time we spend on reproducing problems the more time we have to fix it
  • verifiable – test the code you're about to provide to make sure it reproduces the problem. Remove all other problems that are not related to your request/question.

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.