Skip to content

docs: fix capitalization of Triton Inference Server #8252

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

docs: fix capitalization of Triton Inference Server #8252

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 13 additions & 10 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,12 +25,13 @@
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-->

[![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)

>[!WARNING]
>You are currently on the `main` branch which tracks under-development progress
>towards the next release. The current release is version [2.58.0](https://github.com/triton-inference-server/server/releases/latest)
>and corresponds to the 25.05 container release on NVIDIA GPU Cloud (NGC).
> [!WARNING]
> You are currently on the `main` branch which tracks under-development progress
> towards the next release. The current release is version [2.58.0](https://github.com/triton-inference-server/server/releases/latest)
> and corresponds to the 25.05 container release on NVIDIA GPU Cloud (NGC).

# Triton Inference Server

Expand All @@ -41,7 +42,7 @@ 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
batched, ensembles and audio/video streaming. Triton Inference Server is part of
[NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/),
a software platform that accelerates the data science pipeline and streamlines
the development and deployment of production AI.
Expand Down Expand Up @@ -82,7 +83,7 @@ to begin your Triton journey!

Join the [Triton and TensorRT community](https://www.nvidia.com/en-us/deep-learning-ai/triton-tensorrt-newsletter/) 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
and more. Need enterprise support? NVIDIA global support is available for Triton
Inference Server with the
[NVIDIA AI Enterprise software suite](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/).

Expand All @@ -107,6 +108,7 @@ Image '/workspace/images/mug.jpg':
13.224326 (968) = CUP
10.422965 (505) = COFFEEPOT
```

Please read the [QuickStart](docs/getting_started/quickstart.md) guide for additional information
regarding this example. The quickstart guide also contains an example of how to launch Triton on [CPU-only systems](docs/getting_started/quickstart.md#run-on-cpu-only-system). New to Triton and wondering where to get started? Watch the [Getting Started video](https://youtu.be/NQDtfSi5QF4).

Expand All @@ -130,7 +132,7 @@ contains additional documentation, presentations, and examples.
The recommended way to build and use Triton Inference Server is with Docker
images.

- [Install Triton Inference Server with Docker containers](docs/customization_guide/build.md#building-with-docker) (*Recommended*)
- [Install Triton Inference Server with Docker containers](docs/customization_guide/build.md#building-with-docker) (_Recommended_)
- [Install Triton Inference Server without Docker containers](docs/customization_guide/build.md#building-without-docker)
- [Build a custom Triton Inference Server Docker container](docs/customization_guide/compose.md)
- [Build Triton Inference Server from source](docs/customization_guide/build.md#building-on-unsupported-platforms)
Expand Down Expand Up @@ -186,7 +188,7 @@ configuration](docs/user_guide/model_configuration.md) for the model.

#### Client Support and Examples

A Triton *client* application sends inference and other requests to Triton. The
A Triton _client_ application sends inference and other requests to Triton. The
[Python and C++ client libraries](https://github.com/triton-inference-server/client)
provide APIs to simplify this communication.

Expand Down Expand Up @@ -215,7 +217,7 @@ designed for modularity and flexibility
decryption, or conversion
- Deploy Triton on [Jetson and JetPack](docs/user_guide/jetson.md)
- [Use Triton on AWS
Inferentia](https://github.com/triton-inference-server/python_backend/tree/main/inferentia)
Inferentia](https://github.com/triton-inference-server/python_backend/tree/main/inferentia)

### Additional Documentation

Expand All @@ -224,7 +226,7 @@ designed for modularity and flexibility
- [Customization Guide](docs/README.md#customization-guide)
- [Release Notes](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/index.html)
- [GPU, Driver, and CUDA Support
Matrix](https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html)
Matrix](https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html)

## Contributing

Expand All @@ -241,6 +243,7 @@ We appreciate any feedback, questions or bug reporting regarding this project.
When posting [issues in GitHub](https://github.com/triton-inference-server/server/issues),
follow the process outlined in the [Stack Overflow document](https://stackoverflow.com/help/mcve).
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
Expand Down