post: Advance main to 26.05dev

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:26.03-py3
-ARG TRITONSDK_BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.03-py3-sdk
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.04-py3
+ARG TRITONSDK_BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.04-py3-sdk
 
 ARG MODEL_ANALYZER_VERSION=1.54.0dev
 ARG MODEL_ANALYZER_CONTAINER_VERSION=26.05dev
@@ -20,9 +20,15 @@ RUN apt update -qq && apt install -y docker.io wkhtmltopdf
 
 # Install tritonclient
 COPY --from=sdk /workspace/install/python /tmp/tritonclient
-RUN find /tmp/tritonclient -maxdepth 1 -type f -name \
-    "tritonclient-*-manylinux*.whl" | xargs printf -- '%s[all]' | \
-    xargs pip3 install --upgrade && rm -rf /tmp/tritonclient/
+
+RUN --mount=type=secret,id=triton_ci_pip_extra_values,env=TRITON_CI_PYPI_EXTRA_VALUES \
+    if [ -n "${TRITON_CI_PYPI_EXTRA_VALUES}" ]; then \
+        find /tmp/tritonclient -maxdepth 1 -type f -name \
+            "tritonclient-*-any*.whl" -exec pip3 install --upgrade ${TRITON_CI_PYPI_EXTRA_VALUES} {}[all] \; ; \
+    else \
+        find /tmp/tritonclient -maxdepth 1 -type f -name \
+            "tritonclient-*-any*.whl" -exec pip3 install --upgrade {}[all] \; ; \
+    fi
 
 WORKDIR /opt/triton-model-analyzer
 

README.md

@@ -17,14 +17,14 @@ Triton Model Analyzer is a CLI tool which can help you find a more optimal confi
 
 - [Optuna Search](docs/config_search.md#optuna-search-mode) **_-ALPHA RELEASE-_** allows you to search for every parameter that can be specified in the model configuration, using a hyperparameter optimization framework. Please see the [Optuna](https://optuna.org/) website if you are interested in specific details on how the algorithm functions.
 
-- [Quick Search](docs/config_search.md#quick-search-mode) will **sparsely** search the [Max Batch Size](https://github.com/triton-inference-server/server/blob/r26.03/docs/user_guide/model_configuration.md#maximum-batch-size),
-  [Dynamic Batching](https://github.com/triton-inference-server/server/blob/r26.03/docs/user_guide/batcher.md#dynamic-batcher), and
-  [Instance Group](https://github.com/triton-inference-server/server/blob/r26.03/docs/user_guide/model_configuration.md#instance-groups) spaces by utilizing a heuristic hill-climbing algorithm to help you quickly find a more optimal configuration
+- [Quick Search](docs/config_search.md#quick-search-mode) will **sparsely** search the [Max Batch Size](https://github.com/triton-inference-server/server/blob/r26.04/docs/user_guide/model_configuration.md#maximum-batch-size),
+  [Dynamic Batching](https://github.com/triton-inference-server/server/blob/r26.04/docs/user_guide/batcher.md#dynamic-batcher), and
+  [Instance Group](https://github.com/triton-inference-server/server/blob/r26.04/docs/user_guide/model_configuration.md#instance-groups) spaces by utilizing a heuristic hill-climbing algorithm to help you quickly find a more optimal configuration
 
 - [Automatic Brute Search](docs/config_search.md#automatic-brute-search) will **exhaustively** search the
-  [Max Batch Size](https://github.com/triton-inference-server/server/blob/r26.03/docs/user_guide/model_configuration.md#maximum-batch-size),
-  [Dynamic Batching](https://github.com/triton-inference-server/server/blob/r26.03/docs/user_guide/batcher.md#dynamic-batcher), and
-  [Instance Group](https://github.com/triton-inference-server/server/blob/r26.03/docs/user_guide/model_configuration.md#instance-groups)
+  [Max Batch Size](https://github.com/triton-inference-server/server/blob/r26.04/docs/user_guide/model_configuration.md#maximum-batch-size),
+  [Dynamic Batching](https://github.com/triton-inference-server/server/blob/r26.04/docs/user_guide/batcher.md#dynamic-batcher), and
+  [Instance Group](https://github.com/triton-inference-server/server/blob/r26.04/docs/user_guide/model_configuration.md#instance-groups)
   parameters of your model configuration
 
 - [Manual Brute Search](docs/config_search.md#manual-brute-search) allows you to create manual sweeps for every parameter that can be specified in the model configuration

docs/bls_quick_start.md

@@ -38,7 +38,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:26.03-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:26.04-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:26.03-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:26.04-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/r26.03/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/r26.04/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:26.03-py3 ]
+[ triton_docker_image: <string> | default: nvcr.io/nvidia/tritonserver:26.04-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:26.03-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:26.04-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:26.03-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:26.04-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/r26.03/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/r26.04/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: 26.03-py3
+    tag: 26.04-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:26.03-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:26.04-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -47,15 +47,15 @@ docker pull nvcr.io/nvidia/tritonserver:26.03-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:26.03-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:26.04-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/r26.03/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/r26.04/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:26.03-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:26.04-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -47,15 +47,15 @@ docker pull nvcr.io/nvidia/tritonserver:26.03-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:26.03-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:26.04-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/r26.03/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/r26.04/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: 26.03-py3
+    tag: 26.04-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:26.03-py3"
+DEFAULT_TRITON_DOCKER_IMAGE = "nvcr.io/nvidia/tritonserver:26.04-py3"
 DEFAULT_TRITON_HTTP_ENDPOINT = "localhost:8000"
 DEFAULT_TRITON_GRPC_ENDPOINT = "localhost:8001"
 DEFAULT_TRITON_METRICS_URL = "http://localhost:8002/metrics"