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update: Versions (#8459)
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Dockerfile.sdk

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#
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# Base image on the minimum Triton container
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ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:25.09-py3-min
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ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:25.10-py3-min
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ARG TRITON_CLIENT_REPO_SUBDIR=clientrepo
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ARG TRITON_REPO_ORGANIZATION=http://github.com/triton-inference-server

README.md

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>[!WARNING]
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>You are currently on the `main` branch which tracks under-development progress
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>towards the next release. The current release is version [2.61.0](https://github.com/triton-inference-server/server/releases/latest)
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>and corresponds to the 25.09 container release on NVIDIA GPU Cloud (NGC).
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>towards the next release. The current release is version [2.62.0](https://github.com/triton-inference-server/server/releases/latest)
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>and corresponds to the 25.10 container release on NVIDIA GPU Cloud (NGC).
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# Triton Inference Server
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```bash
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# Step 1: Create the example model repository
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git clone -b r25.09 https://github.com/triton-inference-server/server.git
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git clone -b r25.10 https://github.com/triton-inference-server/server.git
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cd server/docs/examples
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./fetch_models.sh
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# Step 2: Launch triton from the NGC Triton container
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docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:25.09-py3 tritonserver --model-repository=/models --model-control-mode explicit --load-model densenet_onnx
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docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:25.10-py3 tritonserver --model-repository=/models --model-control-mode explicit --load-model densenet_onnx
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# Step 3: Sending an Inference Request
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# In a separate console, launch the image_client example from the NGC Triton SDK container
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docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:25.09-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
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docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:25.10-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
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# Inference should return the following
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Image '/workspace/images/mug.jpg':

TRITON_VERSION

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2.62.0dev
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2.62.0

build.py

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#
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DEFAULT_TRITON_VERSION_MAP = {
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"release_version": "2.62.0dev",
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"triton_container_version": "25.10dev",
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"upstream_container_version": "25.09",
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"release_version": "2.62.0",
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"triton_container_version": "25.10",
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"upstream_container_version": "25.10",
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"ort_version": "1.23.1",
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"ort_openvino_version": "2025.3.0",
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"standalone_openvino_version": "2025.3.0",

deploy/aws/values.yaml

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replicaCount: 1
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image:
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imageName: nvcr.io/nvidia/tritonserver:25.09-py3
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imageName: nvcr.io/nvidia/tritonserver:25.10-py3
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pullPolicy: IfNotPresent
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modelRepositoryPath: s3://triton-inference-server-repository/model_repository
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numGpus: 1

deploy/fleetcommand/Chart.yaml

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apiVersion: v1
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# appVersion is the Triton version; update when changing release
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appVersion: 2.61.0"
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appVersion: 2.62.0"
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description: Triton Inference Server (Fleet Command)
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name: triton-inference-server
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# version is the Chart version; update when changing anything in the chart

deploy/fleetcommand/values.yaml

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replicaCount: 1
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image:
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imageName: nvcr.io/nvidia/tritonserver:25.09-py3
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imageName: nvcr.io/nvidia/tritonserver:25.10-py3
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pullPolicy: IfNotPresent
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numGpus: 1
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serverCommand: tritonserver
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#
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# To set model control mode, uncomment and configure below
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# TODO: Fix the following url, it is invalid
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# See https://github.com/triton-inference-server/server/blob/r25.09/docs/user_guide/model_management.md
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# See https://github.com/triton-inference-server/server/blob/r25.10/docs/user_guide/model_management.md
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# for more details
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#- --model-control-mode=explicit|poll|none
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#
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# Additional server args
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#
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# see https://github.com/triton-inference-server/server/blob/r25.09/README.md
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# see https://github.com/triton-inference-server/server/blob/r25.10/README.md
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# for more details
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service:

deploy/gcp/values.yaml

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replicaCount: 1
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image:
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imageName: nvcr.io/nvidia/tritonserver:25.09-py3
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imageName: nvcr.io/nvidia/tritonserver:25.10-py3
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pullPolicy: IfNotPresent
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modelRepositoryPath: gs://triton-inference-server-repository/model_repository
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numGpus: 1

deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml

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namespace: default
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spec:
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containers:
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- image: nvcr.io/nvidia/tritonserver:25.09-py3-sdk
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- image: nvcr.io/nvidia/tritonserver:25.10-py3-sdk
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imagePullPolicy: Always
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name: nv-triton-client
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securityContext:

deploy/gke-marketplace-app/server-deployer/build_and_push.sh

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export REGISTRY=gcr.io/$(gcloud config get-value project | tr ':' '/')
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export APP_NAME=tritonserver
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export MAJOR_VERSION=2.61
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export MINOR_VERSION=2.61.0
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export NGC_VERSION=25.09-py3
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export MAJOR_VERSION=2.62
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export MINOR_VERSION=2.62.0
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export NGC_VERSION=25.10-py3
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docker pull nvcr.io/nvidia/$APP_NAME:$NGC_VERSION
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