From d90e2094845e7dfdc55c14678376ebce3b1ffcfe Mon Sep 17 00:00:00 2001
From: Misha Chornyi <99709299+mc-nv@users.noreply.github.com>
Date: Wed, 11 Jun 2025 12:07:05 -0700
Subject: [PATCH 1/6] TPRD-1554: update readme and versions (#8244)

---
 Dockerfile.sdk                                |   2 +-
 README.md                                     | 233 +-----------------
 build.py                                      |   4 +-
 deploy/aws/values.yaml                        |   2 +-
 deploy/fleetcommand/values.yaml               |   6 +-
 deploy/gcp/values.yaml                        |   2 +-
 .../perf-analyzer-script/triton_client.yaml   |   2 +-
 .../server-deployer/build_and_push.sh         |   6 +-
 .../server-deployer/chart/triton/values.yaml  |   6 +-
 .../server-deployer/schema.yaml               |   2 +-
 .../gke-marketplace-app/trt-engine/README.md  |   6 +-
 deploy/k8s-onprem/values.yaml                 |   2 +-
 deploy/oci/values.yaml                        |   2 +-
 docs/introduction/compatibility.md            |  25 +-
 docs/introduction/release_notes.md            |   4 +-
 python/openai/README.md                       |   2 +-
 qa/common/gen_jetson_trt_models               |   2 +-
 qa/common/gen_qa_custom_ops                   |   2 +-
 qa/common/gen_qa_model_repository             |   2 +-
 19 files changed, 41 insertions(+), 271 deletions(-)

diff --git a/Dockerfile.sdk b/Dockerfile.sdk
index 33414c3fe4..d1c4b5a189 100644
--- a/Dockerfile.sdk
+++ b/Dockerfile.sdk
@@ -29,7 +29,7 @@
 #
 
 # Base image on the minimum Triton container
-ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:25.05-py3-min
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:25.06-py3-min
 
 ARG TRITON_CLIENT_REPO_SUBDIR=clientrepo
 ARG TRITON_PA_REPO_SUBDIR=perfanalyzerrepo
diff --git a/README.md b/README.md
index 2de74ae96a..3acc072ce3 100644
--- a/README.md
+++ b/README.md
@@ -28,235 +28,4 @@
 [![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).
-
-# 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/main/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 r25.05 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:25.05-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:25.05-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/main/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/main/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/main/src/c%2B%2B/examples),
-  [Python](https://github.com/triton-inference-server/client/blob/main/src/python/examples),
-  and [Java](https://github.com/triton-inference-server/client/blob/main/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/main/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/main/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/main/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.
+> You are currently on the `r25.06` branch which tracks under-development progress towards the next release.
diff --git a/build.py b/build.py
index 3da4ad89e3..ff069c7d20 100755
--- a/build.py
+++ b/build.py
@@ -73,12 +73,12 @@
 DEFAULT_TRITON_VERSION_MAP = {
     "release_version": "2.60.0dev",
     "triton_container_version": "25.07dev",
-    "upstream_container_version": "25.05",
+    "upstream_container_version": "25.06",
     "ort_version": "1.22.0",
     "ort_openvino_version": "2025.1.0",
     "standalone_openvino_version": "2025.1.0",
     "dcgm_version": "3.3.6",
-    "vllm_version": "0.8.4",
+    "vllm_version": "0.9.0.1",
     "rhel_py_version": "3.12.3",
 }
 
diff --git a/deploy/aws/values.yaml b/deploy/aws/values.yaml
index cddaaaf3a6..c24b0ed7b8 100644
--- a/deploy/aws/values.yaml
+++ b/deploy/aws/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.05-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.06-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: s3://triton-inference-server-repository/model_repository
   numGpus: 1
diff --git a/deploy/fleetcommand/values.yaml b/deploy/fleetcommand/values.yaml
index b4044b795a..d2300ef421 100644
--- a/deploy/fleetcommand/values.yaml
+++ b/deploy/fleetcommand/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.05-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.06-py3
   pullPolicy: IfNotPresent
   numGpus: 1
   serverCommand: tritonserver
@@ -47,13 +47,13 @@ image:
     #
     # To set model control mode, uncomment and configure below
     # TODO: Fix the following url, it is invalid
-    # See https://github.com/triton-inference-server/server/blob/r25.05/docs/user_guide/model_management.md
+    # See https://github.com/triton-inference-server/server/blob/r25.06/docs/user_guide/model_management.md
     #  for more details
     #- --model-control-mode=explicit|poll|none
     #
     # Additional server args
     #
-    # see https://github.com/triton-inference-server/server/blob/r25.05/README.md
+    # see https://github.com/triton-inference-server/server/blob/r25.06/README.md
     #  for more details
 
 service:
diff --git a/deploy/gcp/values.yaml b/deploy/gcp/values.yaml
index 3770934cc8..73948fb39d 100644
--- a/deploy/gcp/values.yaml
+++ b/deploy/gcp/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.05-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.06-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: gs://triton-inference-server-repository/model_repository
   numGpus: 1
diff --git a/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml b/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml
index 38a65ed8e8..8be23e0f2c 100644
--- a/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml
+++ b/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml
@@ -33,7 +33,7 @@ metadata:
   namespace: default
 spec:
   containers:
-  - image: nvcr.io/nvidia/tritonserver:25.05-py3-sdk
+  - image: nvcr.io/nvidia/tritonserver:25.06-py3-sdk
     imagePullPolicy: Always
     name: nv-triton-client
     securityContext:
diff --git a/deploy/gke-marketplace-app/server-deployer/build_and_push.sh b/deploy/gke-marketplace-app/server-deployer/build_and_push.sh
index 6cb6f76e0a..b31804b9a2 100755
--- a/deploy/gke-marketplace-app/server-deployer/build_and_push.sh
+++ b/deploy/gke-marketplace-app/server-deployer/build_and_push.sh
@@ -27,9 +27,9 @@
 
 export REGISTRY=gcr.io/$(gcloud config get-value project | tr ':' '/')
 export APP_NAME=tritonserver
-export MAJOR_VERSION=2.58
-export MINOR_VERSION=2.58.0
-export NGC_VERSION=25.05-py3
+export MAJOR_VERSION=2.59
+export MINOR_VERSION=2.59.0
+export NGC_VERSION=25.06-py3
 
 docker pull nvcr.io/nvidia/$APP_NAME:$NGC_VERSION
 
diff --git a/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml b/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml
index ed69180192..52b1ab2f21 100644
--- a/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml
@@ -31,14 +31,14 @@ maxReplicaCount: 3
 tritonProtocol: HTTP
 # HPA GPU utilization autoscaling target
 HPATargetAverageValue: 85
-modelRepositoryPath: gs://triton_sample_models/25.05
-publishedVersion: '2.58.0'
+modelRepositoryPath: gs://triton_sample_models/25.06
+publishedVersion: '2.59.0'
 gcpMarketplace: true
 
 image:
   registry: gcr.io
   repository: nvidia-ngc-public/tritonserver
-  tag: 25.05-py3
+  tag: 25.06-py3
   pullPolicy: IfNotPresent
   # modify the model repository here to match your GCP storage bucket
   numGpus: 1
diff --git a/deploy/gke-marketplace-app/server-deployer/schema.yaml b/deploy/gke-marketplace-app/server-deployer/schema.yaml
index 68c171d28a..acaafab408 100644
--- a/deploy/gke-marketplace-app/server-deployer/schema.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/schema.yaml
@@ -89,7 +89,7 @@ properties:
   modelRepositoryPath:
     type: string
     title: Bucket where models are stored. Please make sure the user/service account to create the GKE app has permission to this GCS bucket. Read Triton documentation on configs and formatting details, supporting TensorRT, TensorFlow, Pytorch, Onnx ... etc.
-    default: gs://triton_sample_models/25.05
+    default: gs://triton_sample_models/25.06
   image.ldPreloadPath:
     type: string
     title: Leave this empty by default. Triton allows users to create custom layers for backend such as TensorRT plugin or Tensorflow custom ops, the compiled shared library must be provided via LD_PRELOAD environment variable.
diff --git a/deploy/gke-marketplace-app/trt-engine/README.md b/deploy/gke-marketplace-app/trt-engine/README.md
index 01475e551c..dba63cf63e 100644
--- a/deploy/gke-marketplace-app/trt-engine/README.md
+++ b/deploy/gke-marketplace-app/trt-engine/README.md
@@ -33,7 +33,7 @@
 ```
 docker run --gpus all -it --network host \
     --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
-    -v ~:/scripts nvcr.io/nvidia/tensorrt:25.05-py3
+    -v ~:/scripts nvcr.io/nvidia/tensorrt:25.06-py3
 
 pip install onnx six torch tf2onnx tensorflow
 
@@ -57,7 +57,7 @@ mkdir -p engines
 
 python3 builder.py -m models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/model.ckpt -o engines/bert_large_int8_bs1_s128.engine -b 1 -s 128 -c models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/ -v models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/vocab.txt --int8 --fp16 --strict --calib-num 1 -iln -imh
 
-gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/25.05/bert/1/model.plan
+gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/25.06/bert/1/model.plan
 ```
 
-For each Triton upgrade, container version used to generate the model, and the model path in GCS `gs://triton_sample_models/25.05/` should be updated accordingly with the correct version.
+For each Triton upgrade, container version used to generate the model, and the model path in GCS `gs://triton_sample_models/25.06/` should be updated accordingly with the correct version.
diff --git a/deploy/k8s-onprem/values.yaml b/deploy/k8s-onprem/values.yaml
index 27cb9bf33b..ac6d7a3480 100644
--- a/deploy/k8s-onprem/values.yaml
+++ b/deploy/k8s-onprem/values.yaml
@@ -30,7 +30,7 @@ tags:
   openshift: false
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.05-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.06-py3
   pullPolicy: IfNotPresent
   modelRepositoryServer: < Replace with the IP Address of your file server >
   modelRepositoryPath: /srv/models
diff --git a/deploy/oci/values.yaml b/deploy/oci/values.yaml
index b082864f3e..be6eb13a0e 100644
--- a/deploy/oci/values.yaml
+++ b/deploy/oci/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:25.05-py3
+  imageName: nvcr.io/nvidia/tritonserver:25.06-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: s3://https://<OCI_NAMESPACE>.compat.objectstorage.<OCI_REGION>.oraclecloud.com:443/triton-inference-server-repository
   numGpus: 1
diff --git a/docs/introduction/compatibility.md b/docs/introduction/compatibility.md
index afa4ab0ac8..4332d4c8ae 100644
--- a/docs/introduction/compatibility.md
+++ b/docs/introduction/compatibility.md
@@ -38,20 +38,21 @@
 
 | Triton release version	 | NGC Tag	 | Python version	 | Torch version | TensorRT version | TensorRT-LLM version | CUDA version | CUDA Driver version | Size |
 | --- | ---  | --- | --- | --- | --- | --- | --- | --- |
+| 25.06 | nvcr.io/nvidia/tritonserver:25.06-trtllm-python-py3 | Python 3.12.3  | 2.7.0a0+79aa17489c.nv25.4 | 10.10.0.31 | 0.20.0 | 12.9.0.036 | 575.51.03 | 18.3G |
 | 25.05 | nvcr.io/nvidia/tritonserver:25.05-trtllm-python-py3 | Python 3.12.3  | 2.7.0a0+7c8ec84dab.nv25.3 | 10.9.0.34 | 0.19.0 | 12.8.1.012 | 570.124.06 | 17G |
 | 25.04 | nvcr.io/nvidia/tritonserver:25.04-trtllm-python-py3 | Python 3.12.3  | 2.7.0a0+7c8ec84dab.nv25.3 | 10.9.0.34 | 0.18.2 | 12.8.1.012 | 570.124.06 | 17G |
-| 25.03 | nvcr.io/nvidia/tritonserver:25.03-trtllm-python-py3 | Python 3.12.3  | 2.7.0a0%2B7c8ec84dab.nv25.3 | 10.9.0.34 | 0.18.0 | 12.8.1.012 | 570.124.06 | 28G |
-| 25.02 | nvcr.io/nvidia/tritonserver:25.02-trtllm-python-py3 | Python 3.12.3 | 2.6.0a0%2Becf3bae40a.nv25.1 | 10.8.0.43 | 0.17.0.post1 | 12.8.0.038 | 570.86.10 | 28G |
-| 25.01 | nvcr.io/nvidia/tritonserver:25.01-trtllm-python-py3 | Python 3.12.3  | 2.6.0a0%2Becf3bae40a.nv25.1 | 10.8.0.43 | 0.17.0 | 12.8.0.038 | 570.86.10 | 30G |
-| 24.12 | nvcr.io/nvidia/tritonserver:24.12-trtllm-python-py3 | Python 3.12.3  | 2.6.0a0%2Bdf5bbc09d1.nv24.11 | 10.7.0 | 0.16.0 | 12.6.3 | 560.35.05 | 22G |
-| 24.11 | nvcr.io/nvidia/tritonserver:24.11-trtllm-python-py3 | Python 3.10.12  | 2.5.0a0%2Be000cf0ad9.nv24.10 | 10.6.0 | 0.15.0 | 12.6.3 | 555.42.06 | 24.8G |
-| 24.10 | nvcr.io/nvidia/tritonserver:24.10-trtllm-python-py3 | Python 3.10.12  | 2.4.0a0%2B3bcc3cddb5.nv24.7 | 10.4.0 | 0.14.0 | 12.5.1.007 | 555.42.06 | 23.3G |
-| 24.09 | nvcr.io/nvidia/tritonserver:24.09-trtllm-python-py3 | Python 3.10.12  | 2.4.0a0%2B3bcc3cddb5.nv24.7 | 10.4.0 | 0.13.0 | 12.5.1.007 | 555.42.06 | 21G |
-| 24.08 | nvcr.io/nvidia/tritonserver:24.08-trtllm-python-py3 | Python 3.10.12 | 2.4.0a0%2B3bcc3cddb5.nv24.7 | 10.3.0 | 0.12.0 | 12.5.1.007 | 555.42.06 | 21G |
-| 24.07 | nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3 | Python 3.10.12 | 2.4.0a0%2B07cecf4168.nv24.5 | 10.1.0 | 0.11.0 | 12.4.1.003 | 550.54.15 | 23G |
-| 24.06 | nvcr.io/nvidia/tritonserver:24.06-trtllm-python-py3 | Python 3.10.12  | 2.3.0a0%2B40ec155e58.nv24.3 | 10.0.1 | 0.10.0 | 12.4.0.041 | 550.54.14 | 31G |
-| 24.05 | nvcr.io/nvidia/tritonserver:24.05-trtllm-python-py3 | Python 3.10.12  | 2.3.0a0%2Bebedce2 | 10.0.1.6  | 0.9.0 |  12.3.2.001 | 545.23.08 | 34G |
-| 24.04 | nvcr.io/nvidia/tritonserver:24.04-trtllm-python-py3 | Python 3.10.12  | 2.3.0a0%2Bebedce2 | 9.3.0.post12.dev1 | 0.9.0  | 12.3.2.001 | 545.23.08 | 34G |
+| 25.03 | nvcr.io/nvidia/tritonserver:25.03-trtllm-python-py3 | Python 3.12.3  | 2.7.0a0+7c8ec84dab.nv25.3 | 10.9.0.34 | 0.18.0 | 12.8.1.012 | 570.124.06 | 28G |
+| 25.02 | nvcr.io/nvidia/tritonserver:25.02-trtllm-python-py3 | Python 3.12.3 | 2.6.0a0+ecf3bae40a.nv25.1 | 10.8.0.43 | 0.17.0.post1 | 12.8.0.038 | 570.86.10 | 28G |
+| 25.01 | nvcr.io/nvidia/tritonserver:25.01-trtllm-python-py3 | Python 3.12.3  | 2.6.0a0+ecf3bae40a.nv25.1 | 10.8.0.43 | 0.17.0 | 12.8.0.038 | 570.86.10 | 30G |
+| 24.12 | nvcr.io/nvidia/tritonserver:24.12-trtllm-python-py3 | Python 3.12.3  | 2.6.0a0+df5bbc09d1.nv24.11 | 10.7.0 | 0.16.0 | 12.6.3 | 560.35.05 | 22G |
+| 24.11 | nvcr.io/nvidia/tritonserver:24.11-trtllm-python-py3 | Python 3.10.12  | 2.5.0a0+e000cf0ad9.nv24.10 | 10.6.0 | 0.15.0 | 12.6.3 | 555.42.06 | 24.8G |
+| 24.10 | nvcr.io/nvidia/tritonserver:24.10-trtllm-python-py3 | Python 3.10.12  | 2.4.0a0+3bcc3cddb5.nv24.7 | 10.4.0 | 0.14.0 | 12.5.1.007 | 555.42.06 | 23.3G |
+| 24.09 | nvcr.io/nvidia/tritonserver:24.09-trtllm-python-py3 | Python 3.10.12  | 2.4.0a0+3bcc3cddb5.nv24.7 | 10.4.0 | 0.13.0 | 12.5.1.007 | 555.42.06 | 21G |
+| 24.08 | nvcr.io/nvidia/tritonserver:24.08-trtllm-python-py3 | Python 3.10.12 | 2.4.0a0+3bcc3cddb5.nv24.7 | 10.3.0 | 0.12.0 | 12.5.1.007 | 555.42.06 | 21G |
+| 24.07 | nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3 | Python 3.10.12 | 2.4.0a0+07cecf4168.nv24.5 | 10.1.0 | 0.11.0 | 12.4.1.003 | 550.54.15 | 23G |
+| 24.06 | nvcr.io/nvidia/tritonserver:24.06-trtllm-python-py3 | Python 3.10.12  | 2.3.0a0+40ec155e58.nv24.3 | 10.0.1 | 0.10.0 | 12.4.0.041 | 550.54.14 | 31G |
+| 24.05 | nvcr.io/nvidia/tritonserver:24.05-trtllm-python-py3 | Python 3.10.12  | 2.3.0a0+ebedce2 | 10.0.1.6  | 0.9.0 |  12.3.2.001 | 545.23.08 | 34G |
+| 24.04 | nvcr.io/nvidia/tritonserver:24.04-trtllm-python-py3 | Python 3.10.12  | 2.3.0a0+ebedce2 | 9.3.0.post12.dev1 | 0.9.0  | 12.3.2.001 | 545.23.08 | 34G |
 
 ## Container Name: vllm-python-py3
 
diff --git a/docs/introduction/release_notes.md b/docs/introduction/release_notes.md
index f297ec3ac2..7038abbb01 100644
--- a/docs/introduction/release_notes.md
+++ b/docs/introduction/release_notes.md
@@ -25,9 +25,9 @@
 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 -->
-# [Triton Inference Server Release 25.05](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-25-05.html#rel-25-05)
+# [Triton Inference Server Release 25.06](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-25-06.html#rel-25-06)
 
-The Triton Inference Server container image, release 25.05, is available
+The Triton Inference Server container image, release 25.06, is available
 on [NGC](https://ngc.nvidia.com/catalog/containers/nvidia:tritonserver) and
 is open source
 on [GitHub](https://github.com/triton-inference-server/server). Release notes can
diff --git a/python/openai/README.md b/python/openai/README.md
index 3d770caa2e..e93c9c1f16 100644
--- a/python/openai/README.md
+++ b/python/openai/README.md
@@ -51,7 +51,7 @@
 docker run -it --net=host --gpus all --rm \
   -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
   -e HF_TOKEN \
-  nvcr.io/nvidia/tritonserver:25.05-vllm-python-py3
+  nvcr.io/nvidia/tritonserver:25.06-vllm-python-py3
 ```
 
 2. Launch the OpenAI-compatible Triton Inference Server:
diff --git a/qa/common/gen_jetson_trt_models b/qa/common/gen_jetson_trt_models
index 8726e7c271..b081fe9c4e 100755
--- a/qa/common/gen_jetson_trt_models
+++ b/qa/common/gen_jetson_trt_models
@@ -34,7 +34,7 @@
 # Make all generated files accessible outside of container
 umask 0000
 # Set the version of the models
-TRITON_VERSION=${TRITON_VERSION:=25.05}
+TRITON_VERSION=${TRITON_VERSION:=25.06}
 # Set the CUDA device to use
 CUDA_DEVICE=${RUNNER_ID:=0}
 # Set TensorRT image
diff --git a/qa/common/gen_qa_custom_ops b/qa/common/gen_qa_custom_ops
index 96801e4470..108b1daa53 100755
--- a/qa/common/gen_qa_custom_ops
+++ b/qa/common/gen_qa_custom_ops
@@ -37,7 +37,7 @@
 ##
 ############################################################################
 
-TRITON_VERSION=${TRITON_VERSION:=25.05}
+TRITON_VERSION=${TRITON_VERSION:=25.06}
 NVIDIA_UPSTREAM_VERSION=${NVIDIA_UPSTREAM_VERSION:=$TRITON_VERSION}
 PYTORCH_IMAGE=${PYTORCH_IMAGE:=nvcr.io/nvidia/pytorch:$NVIDIA_UPSTREAM_VERSION-py3}
 UBUNTU_IMAGE=${UBUNTU_IMAGE:=ubuntu:24.04}
diff --git a/qa/common/gen_qa_model_repository b/qa/common/gen_qa_model_repository
index 7f4741d1c7..2deeb97da5 100755
--- a/qa/common/gen_qa_model_repository
+++ b/qa/common/gen_qa_model_repository
@@ -48,7 +48,7 @@
 ##
 ############################################################################
 
-TRITON_VERSION=${TRITON_VERSION:=25.05}
+TRITON_VERSION=${TRITON_VERSION:=25.06}
 
 # ONNX. Use ONNX_OPSET 0 to use the default for ONNX version
 ONNX_VERSION=1.16.1

From c0bc6aecec7da501b5a84949e55340409db000a7 Mon Sep 17 00:00:00 2001
From: Yingge He <157551214+yinggeh@users.noreply.github.com>
Date: Wed, 18 Jun 2025 13:35:42 -0700
Subject: [PATCH 2/6] ci: Fix onnx_model_store resnet_v1 name issues (#8255)

Co-authored-by: Misha Chornyi <99709299+mc-nv@users.noreply.github.com>
---
 qa/L0_long_running_stress/scenarios.py | 10 +++++-----
 qa/L0_long_running_stress/test.sh      |  5 ++---
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/qa/L0_long_running_stress/scenarios.py b/qa/L0_long_running_stress/scenarios.py
index 650f228ab5..5b952f5b97 100755
--- a/qa/L0_long_running_stress/scenarios.py
+++ b/qa/L0_long_running_stress/scenarios.py
@@ -203,7 +203,7 @@ def __init__(
         # Add no validation models
         self.options_.append(
             PerfAnalyzerScenario.ModelOption(
-                "resnet_v1_50_def", 32, (1, 4, 1), queue_latency_range_us
+                "resnet_v1_50", 32, (1, 4, 1), queue_latency_range_us
             )
         )
         for trial in sequence_trials:
@@ -334,7 +334,7 @@ def run(self, client_metadata):
 class ResNetScenario(Scenario):
     def __init__(self, name, batch_size=32, verbose=False, out_stream=sys.stdout):
         super().__init__(name, [], verbose, out_stream)
-        self.model_name_ = "resnet_v1_50_def"
+        self.model_name_ = "resnet_v1_50"
         self.batch_size_ = batch_size
 
         img = self.preprocess("../images/vulture.jpeg")
@@ -353,7 +353,7 @@ def preprocess(self, filename):
         return scaled
 
     def postprocess(self, results):
-        output_array = results.as_numpy("resnet_v1_50/predictions/Softmax")
+        output_array = results.as_numpy("resnet_v1_50/predictions/Softmax:0")
         if len(output_array) != self.batch_size_:
             raise Exception(
                 "expected {} results, got {}".format(
@@ -377,12 +377,12 @@ def postprocess(self, results):
     def run(self, client_metadata):
         triton_client = client_metadata[0]
 
-        inputs = [grpcclient.InferInput("input", self.image_data_.shape, "FP32")]
+        inputs = [grpcclient.InferInput("input:0", self.image_data_.shape, "FP32")]
         inputs[0].set_data_from_numpy(self.image_data_)
 
         outputs = [
             grpcclient.InferRequestedOutput(
-                "resnet_v1_50/predictions/Softmax", class_count=1
+                "resnet_v1_50/predictions/Softmax:0", class_count=1
             )
         ]
         res = triton_client.infer(self.model_name_, inputs, outputs=outputs)
diff --git a/qa/L0_long_running_stress/test.sh b/qa/L0_long_running_stress/test.sh
index 1973028bcf..92d8c09b0d 100755
--- a/qa/L0_long_running_stress/test.sh
+++ b/qa/L0_long_running_stress/test.sh
@@ -130,9 +130,8 @@ cp -r ../custom_models/custom_zero_1_float32 $MODEL_DIR/custom_zero_1_float32 &&
         echo "{ key: \"execute_delay_ms\"; value: { string_value: \"10000\" }}" >> config.pbtxt && \
         echo "]" >> config.pbtxt)
 
-cp -r $DATADIR/onnx_model_store/resnet_v1_50 $MODEL_DIR/resnet_v1_50_def && \
-  (cd $MODEL_DIR/resnet_v1_50_def && \
-    sed -i 's/^name: "resnet_v1_50"/name: "resnet_v1_50_def"/' config.pbtxt && \
+cp -r $DATADIR/onnx_model_store/resnet_v1_50 $MODEL_DIR/. && \
+  (cd $MODEL_DIR/resnet_v1_50 && \
     echo "optimization { }" >> config.pbtxt)
 
 SERVER_ARGS="--model-repository=`pwd`/$MODEL_DIR"

From b4b14878720a2a7ae783b1406ded868760e21c20 Mon Sep 17 00:00:00 2001
From: Misha Chornyi <99709299+mc-nv@users.noreply.github.com>
Date: Wed, 18 Jun 2025 13:38:04 -0700
Subject: [PATCH 3/6] Update versions (#8260)

---
 deploy/fleetcommand/Chart.yaml                                | 2 +-
 .../server-deployer/chart/triton/Chart.yaml                   | 4 ++--
 .../gke-marketplace-app/server-deployer/data-test/schema.yaml | 2 +-
 deploy/gke-marketplace-app/server-deployer/schema.yaml        | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/deploy/fleetcommand/Chart.yaml b/deploy/fleetcommand/Chart.yaml
index 0664a5d959..b819cbb4a5 100644
--- a/deploy/fleetcommand/Chart.yaml
+++ b/deploy/fleetcommand/Chart.yaml
@@ -26,7 +26,7 @@
 
 apiVersion: v1
 # appVersion is the Triton version; update when changing release
-appVersion: "2.58.0"
+appVersion: "2.59.0"
 description: Triton Inference Server (Fleet Command)
 name: triton-inference-server
 # version is the Chart version; update when changing anything in the chart
diff --git a/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml b/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml
index 5011733bbc..3206248935 100644
--- a/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml
@@ -25,7 +25,7 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 apiVersion: v1
-appVersion: "2.58"
+appVersion: "2.59"
 description: Triton Inference Server
 name: triton-inference-server
-version: 2.58.0
+version: 2.59.0
diff --git a/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml b/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml
index 2c55441213..36ba6b6a63 100644
--- a/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml
@@ -27,7 +27,7 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '2.58.0'
+  publishedVersion: '2.59.0'
   publishedVersionMetadata:
     releaseNote: >-
       Initial release.
diff --git a/deploy/gke-marketplace-app/server-deployer/schema.yaml b/deploy/gke-marketplace-app/server-deployer/schema.yaml
index acaafab408..629303952d 100644
--- a/deploy/gke-marketplace-app/server-deployer/schema.yaml
+++ b/deploy/gke-marketplace-app/server-deployer/schema.yaml
@@ -27,7 +27,7 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '2.58.0'
+  publishedVersion: '2.59.0'
   publishedVersionMetadata:
     releaseNote: >-
       Initial release.

From 1ea80ac3159ac854ac7388782da846eb44c0d7e2 Mon Sep 17 00:00:00 2001
From: Yingge He <157551214+yinggeh@users.noreply.github.com>
Date: Wed, 18 Jun 2025 14:00:35 -0700
Subject: [PATCH 4/6] ci: Fix L0_client_nobatch--base (#8259)

---
 qa/L0_client_nobatch/test.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/qa/L0_client_nobatch/test.sh b/qa/L0_client_nobatch/test.sh
index cb1a05d660..0c01bafb70 100755
--- a/qa/L0_client_nobatch/test.sh
+++ b/qa/L0_client_nobatch/test.sh
@@ -47,7 +47,7 @@ EXPECTED_NUM_TESTS="4"
 
 DATADIR=/data/inferenceserver/${REPO_VERSION}
 MODELDIR="${PWD}/qa_model_repository"
-rm -rf ${MODELDIR} && cp -r "${DATADIR}/qa_model_repository/onnx_*" ${MODELDIR} # Note there is a coupling in ./client_test.py
+rm -rf ${MODELDIR} && mkdir -p ${MODELDIR} && cp -r ${DATADIR}/qa_model_repository/onnx_* ${MODELDIR}/. # Note there is a coupling in ./client_test.py
 SERVER=/opt/tritonserver/bin/tritonserver
 SERVER_ARGS="--model-repository=${MODELDIR}"
 SERVER_LOG="./inference_server.log"

From ea41958968588b0d3adf68c97c1beb31e9d1cf49 Mon Sep 17 00:00:00 2001
From: Misha Chornyi <99709299+mc-nv@users.noreply.github.com>
Date: Thu, 26 Jun 2025 14:58:07 -0700
Subject: [PATCH 5/6] Update README.md for 2.59.0 / 25.06 release (#8268)

---
 README.md | 230 +++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 228 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 3acc072ce3..39de0a4222 100644
--- a/README.md
+++ b/README.md
@@ -27,5 +27,231 @@
 -->
 [![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.

From 769419cb6bf3229024e8a5d5e0a8b757b3c1762c Mon Sep 17 00:00:00 2001
From: Misha Chornyi <mchornyi@nvidia.com>
Date: Fri, 27 Jun 2025 15:59:07 -0700
Subject: [PATCH 6/6] post-25.06: Update README.md

---
 README.md | 23 ++++++++++++++---------
 1 file changed, 14 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index 39de0a4222..057e58e8f9 100644
--- a/README.md
+++ b/README.md
@@ -27,6 +27,11 @@
 -->
 [![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 [2.59.0](https://github.com/triton-inference-server/server/releases/latest)
+>and corresponds to the 25.06 container release on NVIDIA GPU Cloud (NGC).
+
 # Triton Inference Server
 
 Triton Inference Server is an open source inference serving software that
@@ -56,7 +61,7 @@ Major features include:
 - 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)
+  [Python-based backends.](https://github.com/triton-inference-server/backend/blob/main/docs/python_based_backends.md#python-based-backends)
 - Model pipelines using
   [Ensembling](docs/user_guide/architecture.md#ensemble-models) or [Business
   Logic Scripting
@@ -168,10 +173,10 @@ configuration](docs/user_guide/model_configuration.md) for the model.
   [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)
+  [Backend-Platform Support Matrix](https://github.com/triton-inference-server/backend/blob/main/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)
+  [Performance Analyzer](https://github.com/triton-inference-server/perf_analyzer/blob/main/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
@@ -185,14 +190,14 @@ 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)
+- Review client examples for [C++](https://github.com/triton-inference-server/client/blob/main/src/c%2B%2B/examples),
+  [Python](https://github.com/triton-inference-server/client/blob/main/src/python/examples),
+  and [Java](https://github.com/triton-inference-server/client/blob/main/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)
+  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
 
@@ -201,7 +206,7 @@ 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)
+  in either [C/C++](https://github.com/triton-inference-server/backend/blob/main/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
@@ -210,7 +215,7 @@ 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/r25.06/inferentia)
+   Inferentia](https://github.com/triton-inference-server/python_backend/tree/main/inferentia)
 
 ### Additional Documentation