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>[!WARNING]
>You are currently on the main branch which tracks under-development progress
>towards the next release. The current release is version 2.71.0
>and corresponds to the 26.07 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

frameworks](https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton)

• [Supports multiple machine learning

frameworks](https://github.com/triton-inference-server/fil_backend)

• [Concurrent model

execution](docs/user_guide/architecture.md#concurrent-model-execution)

• Dynamic batching
• Sequence batching and

implicit state management
for stateful models

• Provides Backend API that

allows adding custom backends and pre/post processing operations

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

Python-based backends.

• Model pipelines using

Ensembling or [Business
Logic Scripting
(BLS)](https://github.com/triton-inference-server/python_backend#business-logic-scripting)

• [HTTP/REST and GRPC inference

protocols](docs/customization_guide/inference_protocols.md) based on the community
developed [KServe
protocol](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2)

• A C API and

Java API
allow Triton to link directly into your application for edge and other in-process use cases

• Metrics indicating GPU utilization, server

throughput, server latency, and more

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 r26.07 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:26.07-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:26.07-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

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.

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,

AWS, and NVIDIA FleetCommand

• 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](docs/user_guide/model_configuration.md) for the model.

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

and Business Logic Scripting (BLS)

• Optimize your models setting scheduling and batching

parameters and model instances.

• Use the Model Analyzer tool

to help optimize your model configuration with profiling

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

unloading models](docs/user_guide/model_management.md)

Configure and Use Triton Inference Server

• Read the Quick Start Guide to run Triton Inference

Server on both GPU and CPU

• Triton supports multiple execution engines, called

backends, including
TensorRT,
PyTorch,
ONNX,
OpenVINO,
Python, and more

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

Look at the
Backend-Platform Support Matrix
to learn which backends are supported on your target platform.

• Learn how to optimize performance using the

Performance Analyzer
and
Model Analyzer

• Learn how to manage loading and unloading models in

Triton

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

or gRPC protocols](docs/customization_guide/inference_protocols.md#httprest-and-grpc-protocols)

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++,

Python,
and Java

• Configure HTTP

and gRPC
client options

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

request without any additional metadata](https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_binary_data.md#raw-binary-request)

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

in either C/C++
or Python

• Create decoupled backends and models that can send

multiple responses for a request or not send any responses for a request

• Use a Triton repository agent to add functionality

that operates when a model is loaded and unloaded, such as authentication,
decryption, or conversion

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

Inferentia](https://github.com/triton-inference-server/python_backend/tree/main/inferentia)

Additional Documentation

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

Matrix](https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html)

Contributing

Contributions to Triton Inference Server are more than welcome. To
contribute please review the [contribution
guidelines](CONTRIBUTING.md). 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](https://github.com/triton-inference-server/contrib).

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.