docs(versions): update README and versions for r26.06 (#1026) (#1027)

Strip dev suffixes for the 26.06 release and align container train.

- VERSION 1.55.0dev -> 1.55.0
- Dockerfile MODEL_ANALYZER_VERSION 1.55.0dev -> 1.55.0
- Dockerfile MODEL_ANALYZER_CONTAINER_VERSION 26.06dev -> 26.06
- BASE/SDK image tags and docs/helm/config refs 26.05 -> 26.06

Part of TRI-1404.

Author: mc-nv

Dockerfile

@@ -1,8 +1,8 @@
 # SPDX-FileCopyrightText: Copyright (c) 2020-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.05-py3
-ARG TRITONSDK_BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.06-py3
+ARG TRITONSDK_BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 
 ARG MODEL_ANALYZER_VERSION=1.56.0dev
 ARG MODEL_ANALYZER_CONTAINER_VERSION=26.07dev

docs/bls_quick_start.md

@@ -38,7 +38,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -48,7 +48,7 @@ docker run -it --gpus 1 \
       --shm-size 2G \
       -v /var/run/docker.sock:/var/run/docker.sock \
       -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
-      --net=host nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 ```
 
 **Important:** The example above uses a single GPU. If you are running on multiple GPUs, you may need to increase the shared memory size accordingly<br><br>
@@ -57,7 +57,7 @@ docker run -it --gpus 1 \
 
 ---
 
-The [examples/quick-start](../examples/quick-start) directory is an example [Triton Model Repository](https://github.com/triton-inference-server/server/blob/r26.05/docs/user_guide/model_repository.md) that contains the BLS model `bls` which calculates the sum of two inputs using `add` model.
+The [examples/quick-start](../examples/quick-start) directory is an example [Triton Model Repository](https://github.com/triton-inference-server/server/blob/r26.06/docs/user_guide/model_repository.md) that contains the BLS model `bls` which calculates the sum of two inputs using `add` model.
 
 An example model analyzer YAML config that performs a BLS model search
 

docs/config.md

@@ -142,7 +142,7 @@ cpu_only_composing_models: <comma-delimited-string-list>
 [ reload_model_disable: <bool> | default: false]
 
 # Triton Docker image tag used when launching using Docker mode
-[ triton_docker_image: <string> | default: nvcr.io/nvidia/tritonserver:26.05-py3 ]
+[ triton_docker_image: <string> | default: nvcr.io/nvidia/tritonserver:26.06-py3 ]
 
 # Triton Server HTTP endpoint url used by Model Analyzer client"
 [ triton_http_endpoint: <string> | default: localhost:8000 ]

docs/ensemble_quick_start.md

@@ -38,7 +38,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -48,7 +48,7 @@ docker run -it --gpus 1 \
       --shm-size 1G \
       -v /var/run/docker.sock:/var/run/docker.sock \
       -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
-      --net=host nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 ```
 
 **Important:** The example above uses a single GPU. If you are running on multiple GPUs, you may need to increase the shared memory size accordingly<br><br>
@@ -57,7 +57,7 @@ docker run -it --gpus 1 \
 
 ---
 
-The [examples/quick-start](../examples/quick-start) directory is an example [Triton Model Repository](https://github.com/triton-inference-server/server/blob/r26.05/docs/user_guide/model_repository.md) that contains the ensemble model `ensemble_add_sub`, which calculates the sum and difference of two inputs using `add` and `sub` models.
+The [examples/quick-start](../examples/quick-start) directory is an example [Triton Model Repository](https://github.com/triton-inference-server/server/blob/r26.06/docs/user_guide/model_repository.md) that contains the ensemble model `ensemble_add_sub`, which calculates the sum and difference of two inputs using `add` and `sub` models.
 
 Run the Model Analyzer `profile` subcommand inside the container with:
 

docs/kubernetes_deploy.md

@@ -68,7 +68,7 @@ images:
 
   triton:
     image: nvcr.io/nvidia/tritonserver
-    tag: 26.05-py3
+    tag: 26.06-py3
 ```
 
 The model analyzer executable uses the config file defined in `helm-chart/templates/config-map.yaml`. This config can be modified to supply arguments to model analyzer. Only the content under the `config.yaml` section of the file should be modified.

docs/mm_quick_start.md

@@ -38,7 +38,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -47,15 +47,15 @@ docker pull nvcr.io/nvidia/tritonserver:26.05-py3-sdk
 docker run -it --gpus all \
       -v /var/run/docker.sock:/var/run/docker.sock \
       -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
-      --net=host nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 ```
 
 ## `Step 3:` Profile both models concurrently
 
 ---
 
 The [examples/quick-start](../examples/quick-start) directory is an example
-[Triton Model Repository](https://github.com/triton-inference-server/server/blob/r26.05/docs/user_guide/model_repository.md) that contains two libtorch models: `add_sub` & `resnet50_python`
+[Triton Model Repository](https://github.com/triton-inference-server/server/blob/r26.06/docs/user_guide/model_repository.md) that contains two libtorch models: `add_sub` & `resnet50_python`
 
 Run the Model Analyzer `profile` subcommand inside the container with:
 

docs/quick_start.md

@@ -38,7 +38,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -47,15 +47,15 @@ docker pull nvcr.io/nvidia/tritonserver:26.05-py3-sdk
 docker run -it --gpus all \
       -v /var/run/docker.sock:/var/run/docker.sock \
       -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
-      --net=host nvcr.io/nvidia/tritonserver:26.05-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:26.06-py3-sdk
 ```
 
 ## `Step 3:` Profile the `add_sub` model
 
 ---
 
 The [examples/quick-start](../examples/quick-start) directory is an example
-[Triton Model Repository](https://github.com/triton-inference-server/server/blob/r26.05/docs/user_guide/model_repository.md) that contains a simple libtorch model which calculates
+[Triton Model Repository](https://github.com/triton-inference-server/server/blob/r26.06/docs/user_guide/model_repository.md) that contains a simple libtorch model which calculates
 the sum and difference of two inputs.
 
 Run the Model Analyzer `profile` subcommand inside the container with:

helm-chart/values.yaml

@@ -26,4 +26,4 @@ images:
 
   triton:
     image: nvcr.io/nvidia/tritonserver
-    tag: 26.05-py3
+    tag: 26.06-py3

model_analyzer/config/input/config_defaults.py

@@ -52,7 +52,7 @@
 DEFAULT_CONCURRENCY_SWEEP_DISABLE = False
 DEFAULT_DCGM_DISABLE = False
 DEFAULT_TRITON_LAUNCH_MODE = "local"
-DEFAULT_TRITON_DOCKER_IMAGE = "nvcr.io/nvidia/tritonserver:26.05-py3"
+DEFAULT_TRITON_DOCKER_IMAGE = "nvcr.io/nvidia/tritonserver:26.06-py3"
 DEFAULT_TRITON_HTTP_ENDPOINT = "localhost:8000"
 DEFAULT_TRITON_GRPC_ENDPOINT = "localhost:8001"
 DEFAULT_TRITON_METRICS_URL = "http://localhost:8002/metrics"