Update README.md for 2.59.0 / 25.06 release (#8268)

Author: mc-nv

README.md

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 [![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)
 
->[!WARNING]
-> You are currently on the `r25.06` branch which tracks under-development progress towards the next release.
+# 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,
+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](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.
+
+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](docs/user_guide/model_configuration.md#dynamic-batcher)
+- [Sequence batching](docs/user_guide/model_configuration.md#sequence-batcher) and
+  [implicit state management](docs/user_guide/architecture.md#implicit-state-management)
+  for stateful models
+- Provides [Backend API](https://github.com/triton-inference-server/backend) that
+  allows adding custom backends and pre/post processing operations
+- Supports writing custom backends in python, a.k.a.
+  [Python-based backends.](https://github.com/triton-inference-server/backend/blob/r25.06/docs/python_based_backends.md#python-based-backends)
+- Model pipelines using
+  [Ensembling](docs/user_guide/architecture.md#ensemble-models) 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](docs/customization_guide/inference_protocols.md#in-process-triton-server-api) and
+  [Java API](docs/customization_guide/inference_protocols.md#java-bindings-for-in-process-triton-server-api)
+  allow Triton to link directly into your application for edge and other in-process use cases
+- [Metrics](docs/user_guide/metrics.md) indicating GPU utilization, server
+  throughput, server latency, and more
+
+**New to Triton Inference Server?** Make use of
+[these tutorials](https://github.com/triton-inference-server/tutorials)
+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
+Inference Server with the
+[NVIDIA AI Enterprise software suite](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/).
+
+## Serve a Model in 3 Easy Steps
+
+```bash
+# Step 1: Create the example model repository
+git clone -b r25.06 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:25.06-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:25.06-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](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).
+
+## Examples and Tutorials
+
+Check out [NVIDIA LaunchPad](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/trial/)
+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](https://github.com/NVIDIA/DeepLearningExamples)
+page on GitHub. The
+[NVIDIA Developer Zone](https://developer.nvidia.com/nvidia-triton-inference-server)
+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](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)
+- [Build Triton Inference Server for Windows 10](docs/customization_guide/build.md#building-for-windows-10)
+- Examples for deploying Triton Inference Server with Kubernetes and Helm on [GCP](deploy/gcp/README.md),
+  [AWS](deploy/aws/README.md), and [NVIDIA FleetCommand](deploy/fleetcommand/README.md)
+- [Secure Deployment Considerations](docs/customization_guide/deploy.md)
+
+### 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](docs/user_guide/model_repository.md). 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](docs/user_guide/custom_operations.md)
+- Enable model pipelining with [Model Ensemble](docs/user_guide/architecture.md#ensemble-models)
+  and [Business Logic Scripting (BLS)](https://github.com/triton-inference-server/python_backend#business-logic-scripting)
+- Optimize your models setting [scheduling and batching](docs/user_guide/architecture.md#models-and-schedulers)
+  parameters and [model instances](docs/user_guide/model_configuration.md#instance-groups).
+- Use the [Model Analyzer tool](https://github.com/triton-inference-server/model_analyzer)
+  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](docs/getting_started/quickstart.md) to run Triton Inference
+  Server on both GPU and CPU
+- Triton supports multiple execution engines, called
+  [backends](https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton), including
+  [TensorRT](https://github.com/triton-inference-server/tensorrt_backend),
+  [TensorFlow](https://github.com/triton-inference-server/tensorflow_backend),
+  [PyTorch](https://github.com/triton-inference-server/pytorch_backend),
+  [ONNX](https://github.com/triton-inference-server/onnxruntime_backend),
+  [OpenVINO](https://github.com/triton-inference-server/openvino_backend),
+  [Python](https://github.com/triton-inference-server/python_backend), and more
+- Not all the above backends are supported on every platform supported by Triton.
+  Look at the
+  [Backend-Platform Support Matrix](https://github.com/triton-inference-server/backend/blob/r25.06/docs/backend_platform_support_matrix.md)
+  to learn which backends are supported on your target platform.
+- Learn how to [optimize performance](docs/user_guide/optimization.md) using the
+  [Performance Analyzer](https://github.com/triton-inference-server/perf_analyzer/blob/r25.06/README.md)
+  and
+  [Model Analyzer](https://github.com/triton-inference-server/model_analyzer)
+- Learn how to [manage loading and unloading models](docs/user_guide/model_management.md) 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](https://github.com/triton-inference-server/client)
+provide APIs to simplify this communication.
+
+- Review client examples for [C++](https://github.com/triton-inference-server/client/blob/r25.06/src/c%2B%2B/examples),
+  [Python](https://github.com/triton-inference-server/client/blob/r25.06/src/python/examples),
+  and [Java](https://github.com/triton-inference-server/client/blob/r25.06/src/java/src/main/java/triton/client/examples)
+- Configure [HTTP](https://github.com/triton-inference-server/client#http-options)
+  and [gRPC](https://github.com/triton-inference-server/client#grpc-options)
+  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/r25.06/docs/protocol/extension_binary_data.md#raw-binary-request)
+
+### Extend Triton
+
+[Triton Inference Server's architecture](docs/user_guide/architecture.md) is specifically
+designed for modularity and flexibility
+
+- [Customize Triton Inference Server container](docs/customization_guide/compose.md) for your use case
+- [Create custom backends](https://github.com/triton-inference-server/backend)
+  in either [C/C++](https://github.com/triton-inference-server/backend/blob/r25.06/README.md#triton-backend-api)
+  or [Python](https://github.com/triton-inference-server/python_backend)
+- Create [decoupled backends and models](docs/user_guide/decoupled_models.md) that can send
+  multiple responses for a request or not send any responses for a request
+- Use a [Triton repository agent](docs/customization_guide/repository_agents.md) to add functionality
+  that operates when a model is loaded and unloaded, such as authentication,
+  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/r25.06/inferentia)
+
+### Additional Documentation
+
+- [FAQ](docs/user_guide/faq.md)
+- [User Guide](docs/README.md#user-guide)
+- [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)
+
+## 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](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
+  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.](https://github.com/triton-inference-server/server/discussions)
+
+## For more information
+
+Please refer to the [NVIDIA Developer Triton page](https://developer.nvidia.com/nvidia-triton-inference-server)
+for more information.