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Release: Update NGC versions post-24.08 release (#7565)
Co-authored-by: Misha Chornyi <99709299+mc-nv@users.noreply.github.com> Co-authored-by: Francesco Petrini <francescogpetrini@gmail.com>
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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:24.07-py3-min
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ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.08-py3-min
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ARG TRITON_CLIENT_REPO_SUBDIR=clientrepo
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ARG TRITON_PA_REPO_SUBDIR=perfanalyzerrepo

Dockerfile.win10.min

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#
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# Installing TensorRT
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#
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ARG TENSORRT_VERSION=10.2.0.19
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ARG TENSORRT_VERSION=10.3.0.26
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ARG TENSORRT_ZIP="TensorRT-${TENSORRT_VERSION}.Windows10.x86_64.cuda-12.5.zip"
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ARG TENSORRT_SOURCE=https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.2.0/zip/TensorRT-10.2.0.19.Windows10.x86_64.cuda-12.5.zip
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# COPY ${TENSORRT_ZIP} /tmp/${TENSORRT_ZIP}
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#
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# Installing cuDNN
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#
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ARG CUDNN_VERSION=9.2.1.18
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ARG CUDNN_VERSION=9.3.0.75
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ARG CUDNN_ZIP=cudnn-windows-x86_64-${CUDNN_VERSION}_cuda12-archive.zip
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ARG CUDNN_SOURCE=https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/windows-x86_64/cudnn-windows-x86_64-9.2.1.18_cuda12-archive.zip
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ADD ${CUDNN_SOURCE} /tmp/${CUDNN_ZIP}
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#
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# Installing Visual Studio BuildTools: VS17 2022
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#
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ARG BUILDTOOLS_VERSION=17.9.34622.214
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ARG BUILDTOOLS_VERSION=17.10.35201.131
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# Download collect.exe in case of an install failure.
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ADD https://aka.ms/vscollect.exe "C:\tmp\collect.exe"
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# Use the latest release channel. For more control, specify the location of an internal layout.
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# Download the Build Tools bootstrapper.
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# ARG BUILD_TOOLS_SOURCE=https://aka.ms/vs/17/release/vs_buildtools.exe
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ARG BUILD_TOOLS_SOURCE=https://download.visualstudio.microsoft.com/download/pr/5e7b923b-7d89-4e14-95b8-a84ab168e243/96b21d216c7954aaf606c6d7ba59a3de991884a8a86c578c767ba349c23188a9/vs_BuildTools.exe
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ARG BUILD_TOOLS_SOURCE=https://download.visualstudio.microsoft.com/download/pr/28626b4b-f88f-4b55-a0cf-f3eaa2c643fb/e6c43d4dfb36338d954cdb3ad9010ab2a479e712088f4f6b016eadcc721bab28/vs_BuildTools.exe
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ADD ${BUILD_TOOLS_SOURCE} vs_buildtools.exe
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# Install Build Tools with the Microsoft.VisualStudio.Workload.VCTools workload, including recommended.
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ARG VS_INSTALL_PATH_WP="C:\BuildTools"
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RUN setx PATH "%CUDA_INSTALL_ROOT_WP%\bin;%PATH%"
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ARG CUDNN_VERSION=9.2.1.18
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ARG CUDNN_VERSION=9.3.0.75
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ENV CUDNN_VERSION ${CUDNN_VERSION}
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COPY --from=dependency_base /cudnn /cudnn
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RUN copy cudnn\bin\cudnn*.dll "%CUDA_INSTALL_ROOT_WP%\bin\."
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RUN copy cudnn\lib\x64\cudnn*.lib "%CUDA_INSTALL_ROOT_WP%\lib\x64\."
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RUN copy cudnn\include\cudnn*.h "%CUDA_INSTALL_ROOT_WP%\include\."
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LABEL CUDNN_VERSION="${CUDNN_VERSION}"
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ARG TENSORRT_VERSION=10.2.0.19
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ARG TENSORRT_VERSION=10.3.0.26
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ENV TRT_VERSION ${TENSORRT_VERSION}
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COPY --from=dependency_base /TensorRT /TensorRT
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RUN setx PATH "c:\TensorRT\lib;%PATH%"

README.md

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##### LATEST RELEASE
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You are currently on the `main` branch which tracks under-development progress towards the next release.
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The current release is version [2.48.0](https://github.com/triton-inference-server/server/releases/latest) and corresponds to the 24.07 container release on NVIDIA GPU Cloud (NGC).
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The current release is version [2.49.0](https://github.com/triton-inference-server/server/releases/latest) and corresponds to the 24.08 container release on NVIDIA GPU Cloud (NGC).
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Triton Inference Server is an open source inference serving software that
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streamlines AI inferencing. Triton enables teams to deploy any AI model from
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```bash
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# Step 1: Create the example model repository
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git clone -b r24.07 https://github.com/triton-inference-server/server.git
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git clone -b r24.08 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:24.07-py3 tritonserver --model-repository=/models
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docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:24.08-py3 tritonserver --model-repository=/models
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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:24.07-py3-sdk
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docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:24.08-py3-sdk
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/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

TRITON_VERSION

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2.49.0dev
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2.50.0dev

build.py

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# incorrectly load the other version of the openvino libraries.
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#
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TRITON_VERSION_MAP = {
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"2.49.0dev": (
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"24.08dev", # triton container
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"24.07", # upstream container
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"2.50.0dev": (
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"24.09dev", # triton container
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"24.08", # upstream container
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"1.18.1", # ORT
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"2024.0.0", # ORT OpenVINO
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"2024.0.0", # Standalone OpenVINO
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self.comment("Exit script immediately if any command fails")
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if target_platform() == "windows":
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self._file.write("$UseStructuredOutput = $false\n")
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self.blankln()
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self._file.write("function ExitWithCode($exitcode) {\n")
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self._file.write(" $host.SetShouldExit($exitcode)\n")
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self._file.write(" exit $exitcode\n")
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"onnxruntime",
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"TRITON_BUILD_ONNXRUNTIME_VERSION",
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None,
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TRITON_VERSION_MAP[FLAGS.version][2],
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os.getenv("TRITON_BUILD_ONNXRUNTIME_VERSION")
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if os.getenv("TRITON_BUILD_ONNXRUNTIME_VERSION")
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else TRITON_VERSION_MAP[FLAGS.version][2],
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)
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]
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deploy/aws/values.yaml

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replicaCount: 1
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image:
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imageName: nvcr.io/nvidia/tritonserver:24.07-py3
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imageName: nvcr.io/nvidia/tritonserver:24.08-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: "2.48.0"
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appVersion: "2.49.0"
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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:24.07-py3
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imageName: nvcr.io/nvidia/tritonserver:24.08-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/r24.07/docs/model_management.md
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# See https://github.com/triton-inference-server/server/blob/r24.08/docs/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/r24.07/README.md
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# see https://github.com/triton-inference-server/server/blob/r24.08/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:24.07-py3
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imageName: nvcr.io/nvidia/tritonserver:24.08-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:24.07-py3-sdk
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- image: nvcr.io/nvidia/tritonserver:24.08-py3-sdk
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imagePullPolicy: Always
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name: nv-triton-client
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securityContext:

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