Merge branch 'main' of github.com:triton-inference-server/server into…

… yinggeh-DLIS-6657-client-input-byte-size-check

Dockerfile.sdk

@@ -29,7 +29,7 @@
 #
 
 # Base image on the minimum Triton container
-ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.06-py3-min
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.07-py3-min
 
 ARG TRITON_CLIENT_REPO_SUBDIR=clientrepo
 ARG TRITON_COMMON_REPO_TAG=main

Dockerfile.win10.min

@@ -1,4 +1,4 @@
-# Copyright 2021-2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2021-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -37,9 +37,9 @@ RUN choco install unzip -y
 #
 # Installing TensorRT
 #
-ARG TENSORRT_VERSION=10.0.1.6
-ARG TENSORRT_ZIP="TensorRT-${TENSORRT_VERSION}.Windows10.x86_64.cuda-12.4.zip"
-ARG TENSORRT_SOURCE=https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/10.0.1/zip/TensorRT-10.0.1.6.Windows10.win10.cuda-12.4.zip
+ARG TENSORRT_VERSION=10.2.0.19
+ARG TENSORRT_ZIP="TensorRT-${TENSORRT_VERSION}.Windows10.x86_64.cuda-12.5.zip"
+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
 # COPY ${TENSORRT_ZIP} /tmp/${TENSORRT_ZIP}
 ADD ${TENSORRT_SOURCE} /tmp/${TENSORRT_ZIP}
 RUN unzip /tmp/%TENSORRT_ZIP%
@@ -51,9 +51,9 @@ LABEL TENSORRT_VERSION="${TENSORRT_VERSION}"
 #
 # Installing cuDNN
 #
-ARG CUDNN_VERSION=9.1.0.70
+ARG CUDNN_VERSION=9.2.1.18
 ARG CUDNN_ZIP=cudnn-windows-x86_64-${CUDNN_VERSION}_cuda12-archive.zip
-ARG CUDNN_SOURCE=https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/windows-x86_64/cudnn-windows-x86_64-9.1.0.70_cuda12-archive.zip
+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
 ADD ${CUDNN_SOURCE} /tmp/${CUDNN_ZIP}
 RUN unzip /tmp/%CUDNN_ZIP%
 RUN move cudnn-* cudnn
@@ -88,7 +88,7 @@ LABEL PYTHON_VERSION=${PYTHON_VERSION}
 #
 # Installing CMake
 #
-ARG CMAKE_VERSION=3.29.3
+ARG CMAKE_VERSION=3.30.0
 RUN pip install cmake==%CMAKE_VERSION%
 
 ENV CMAKE_TOOLCHAIN_FILE /vcpkg/scripts/buildsystems/vcpkg.cmake
@@ -150,7 +150,7 @@ WORKDIR /
 #
 ARG CUDA_MAJOR=12
 ARG CUDA_MINOR=5
-ARG CUDA_PATCH=0
+ARG CUDA_PATCH=1
 ARG CUDA_VERSION=${CUDA_MAJOR}.${CUDA_MINOR}.${CUDA_PATCH}
 ARG CUDA_PACKAGES="nvcc_${CUDA_MAJOR}.${CUDA_MINOR} \
                    cudart_${CUDA_MAJOR}.${CUDA_MINOR} \
@@ -175,15 +175,15 @@ RUN copy "%CUDA_INSTALL_ROOT_WP%\extras\visual_studio_integration\MSBuildExtensi
 
 RUN setx PATH "%CUDA_INSTALL_ROOT_WP%\bin;%PATH%"
 
-ARG CUDNN_VERSION=9.1.0.70
+ARG CUDNN_VERSION=9.2.1.18
 ENV CUDNN_VERSION ${CUDNN_VERSION}
 COPY --from=dependency_base /cudnn /cudnn
 RUN copy cudnn\bin\cudnn*.dll "%CUDA_INSTALL_ROOT_WP%\bin\."
 RUN copy cudnn\lib\x64\cudnn*.lib "%CUDA_INSTALL_ROOT_WP%\lib\x64\."
 RUN copy cudnn\include\cudnn*.h "%CUDA_INSTALL_ROOT_WP%\include\."
 LABEL CUDNN_VERSION="${CUDNN_VERSION}"
 
-ARG TENSORRT_VERSION=10.0.1.6
+ARG TENSORRT_VERSION=10.2.0.19
 ENV TRT_VERSION ${TENSORRT_VERSION}
 COPY --from=dependency_base /TensorRT /TensorRT
 RUN setx PATH "c:\TensorRT\lib;%PATH%"

README.md

@@ -1,5 +1,5 @@
 <!--
-# Copyright 2018-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2018-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -30,10 +30,11 @@
 
 [![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)
 
-> [!WARNING]
-> ##### LATEST RELEASE
-> You are currently on the `main` branch which tracks under-development progress towards the next release.
-> The current release is version [2.47.0](https://github.com/triton-inference-server/server/releases/latest) and corresponds to the 24.06 container release on NVIDIA GPU Cloud (NGC).
+[!WARNING]
+
+##### LATEST RELEASE
+You are currently on the `main` branch which tracks under-development progress towards the next release.
+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).
 
 Triton Inference Server is an open source inference serving software that
 streamlines AI inferencing. Triton enables teams to deploy any AI model from
@@ -91,16 +92,16 @@ Inference Server with the
 
 ```bash
 # Step 1: Create the example model repository
-git clone -b r24.06 https://github.com/triton-inference-server/server.git
+git clone -b r24.07 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:24.06-py3 tritonserver --model-repository=/models
+docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:24.07-py3 tritonserver --model-repository=/models
 
 # 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:24.06-py3-sdk
+docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:24.07-py3-sdk
 /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
 
 # Inference should return the following

TRITON_VERSION

@@ -1 +1 @@
-2.48.0dev
+2.49.0dev

build.py

@@ -69,14 +69,14 @@
 # incorrectly load the other version of the openvino libraries.
 #
 TRITON_VERSION_MAP = {
-    "2.48.0dev": (
-        "24.06dev",  # triton container
-        "24.06",  # upstream container
+    "2.49.0dev": (
+        "24.08dev",  # triton container
+        "24.07",  # upstream container
         "1.18.1",  # ORT
         "2024.0.0",  # ORT OpenVINO
         "2024.0.0",  # Standalone OpenVINO
         "3.2.6",  # DCGM version
-        "0.5.0.post1",  # vLLM version
+        "0.5.3.post1",  # vLLM version
     )
 }
 
@@ -1086,18 +1086,23 @@ def create_dockerfile_linux(
 # Remove contents that are not needed in runtime
 # Setuptools has breaking changes in version 70.0.0, so fix it to 69.5.1
 # The generated code in grpc_service_pb2_grpc.py depends on grpcio>=1.64.0, so fix it to 1.64.0
-RUN ldconfig && \
-    ARCH="$(uname -i)" && \
-    rm -fr ${TRT_ROOT}/bin ${TRT_ROOT}/targets/${ARCH}-linux-gnu/bin ${TRT_ROOT}/data && \
-    rm -fr ${TRT_ROOT}/doc ${TRT_ROOT}/onnx_graphsurgeon ${TRT_ROOT}/python && \
-    rm -fr ${TRT_ROOT}/samples ${TRT_ROOT}/targets/${ARCH}-linux-gnu/samples && \
-    python3 -m pip install --upgrade pip && \
-    pip3 install --no-cache-dir transformers && \
-    find /usr -name libtensorrt_llm.so -exec dirname {} \; > /etc/ld.so.conf.d/tensorrt-llm.conf && \
-    find /opt/tritonserver -name libtritonserver.so -exec dirname {} \; > /etc/ld.so.conf.d/triton-tensorrtllm-worker.conf && \
-    pip3 install --no-cache-dir setuptools==69.5.1 grpcio-tools==1.64.0
-
+RUN ldconfig && \\
+    ARCH="$(uname -i)" && \\
+    rm -fr ${TRT_ROOT}/bin ${TRT_ROOT}/targets/${ARCH}-linux-gnu/bin ${TRT_ROOT}/data && \\
+    rm -fr ${TRT_ROOT}/doc ${TRT_ROOT}/onnx_graphsurgeon ${TRT_ROOT}/python && \\
+    rm -fr ${TRT_ROOT}/samples ${TRT_ROOT}/targets/${ARCH}-linux-gnu/samples && \\
+    python3 -m pip install --upgrade pip && \\
+    pip3 install --no-cache-dir transformers && \\
+    find /usr -name libtensorrt_llm.so -exec dirname {} \; > /etc/ld.so.conf.d/tensorrt-llm.conf && \\
+    find /opt/tritonserver -name libtritonserver.so -exec dirname {} \; > /etc/ld.so.conf.d/triton-tensorrtllm-worker.conf && \\
+    pip3 install --no-cache-dir  grpcio-tools==1.64.0 && \\
+    pip3 uninstall -y setuptools
 ENV LD_LIBRARY_PATH=/usr/local/tensorrt/lib/:/opt/tritonserver/backends/tensorrtllm:$LD_LIBRARY_PATH
+
+# There are some ucc issues when spawning mpi processes with ompi v4.1.7a1.
+# Downgrade to ompi v4.1.5rc2 to avoid the issue.
+RUN rm -fr /opt/hpcx/ompi
+COPY --from=nvcr.io/nvidia/tritonserver:24.02-py3-min /opt/hpcx/ompi /opt/hpcx/ompi
 """
     with open(os.path.join(ddir, dockerfile_name), "w") as dfile:
         dfile.write(df)
@@ -1229,6 +1234,14 @@ def dockerfile_prepare_container_linux(argmap, backends, enable_gpu, target_mach
             virtualenv \\
       && rm -rf /var/lib/apt/lists/*
 """
+    if "tensorrtllm" in backends:
+        df += """
+# Updating the openssh-client to fix for the CVE-2024-6387. This can be removed when trtllm uses a later CUDA container(12.5 or later)
+RUN apt-get update \\
+    && apt-get install -y --no-install-recommends \\
+        openssh-client \\
+    && rm -rf /var/lib/apt/lists/*
+    """
 
     if "vllm" in backends:
         df += """

deploy/aws/values.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:24.06-py3
+  imageName: nvcr.io/nvidia/tritonserver:24.07-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: s3://triton-inference-server-repository/model_repository
   numGpus: 1
@@ -38,4 +38,4 @@ service:
 secret:
   region: AWS_REGION
   id: AWS_SECRET_KEY_ID
-  key: AWS_SECRET_ACCESS_KEY
+  key: AWS_SECRET_ACCESS_KEY

deploy/fleetcommand/Chart.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -26,7 +26,7 @@
 
 apiVersion: v1
 # appVersion is the Triton version; update when changing release
-appVersion: "2.47.0"
+appVersion: "2.48.0"
 description: Triton Inference Server (Fleet Command)
 name: triton-inference-server
 # version is the Chart version; update when changing anything in the chart

deploy/fleetcommand/values.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:24.06-py3
+  imageName: nvcr.io/nvidia/tritonserver:24.07-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/r24.06/docs/model_management.md
+    # See https://github.com/triton-inference-server/server/blob/r24.07/docs/model_management.md
     #  for more details
     #- --model-control-mode=explicit|poll|none
     #
     # Additional server args
     #
-    # see https://github.com/triton-inference-server/server/blob/r24.06/README.md
+    # see https://github.com/triton-inference-server/server/blob/r24.07/README.md
     #  for more details
 
 service:

deploy/gcp/values.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2023, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2019-2024, NVIDIA CORPORATION. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -27,10 +27,10 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:24.06-py3
+  imageName: nvcr.io/nvidia/tritonserver:24.07-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: gs://triton-inference-server-repository/model_repository
   numGpus: 1
 
 service:
-  type: LoadBalancer
+  type: LoadBalancer

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

@@ -1,4 +1,4 @@
-# Copyright 2021-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2021-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -33,7 +33,7 @@ metadata:
   namespace: default
 spec:
   containers:
-  - image: nvcr.io/nvidia/tritonserver:24.06-py3-sdk
+  - image: nvcr.io/nvidia/tritonserver:24.07-py3-sdk
     imagePullPolicy: Always
     name: nv-triton-client
     securityContext:

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

@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright 2021-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2021-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -27,9 +27,9 @@
 
 export REGISTRY=gcr.io/$(gcloud config get-value project | tr ':' '/')
 export APP_NAME=tritonserver
-export MAJOR_VERSION=2.45
-export MINOR_VERSION=2.45.0
-export NGC_VERSION=24.06-py3
+export MAJOR_VERSION=2.48
+export MINOR_VERSION=2.48.0
+export NGC_VERSION=24.07-py3
 
 docker pull nvcr.io/nvidia/$APP_NAME:$NGC_VERSION
 

deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -25,7 +25,7 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 apiVersion: v1
-appVersion: "2.47"
+appVersion: "2.48"
 description: Triton Inference Server
 name: triton-inference-server
-version: 2.47.0
+version: 2.48.0

deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -31,14 +31,14 @@ maxReplicaCount: 3
 tritonProtocol: HTTP
 # HPA GPU utilization autoscaling target
 HPATargetAverageValue: 85
-modelRepositoryPath: gs://triton_sample_models/24.06
-publishedVersion: '2.47.0'
+modelRepositoryPath: gs://triton_sample_models/24.07
+publishedVersion: '2.48.0'
 gcpMarketplace: true
 
 image:
   registry: gcr.io
   repository: nvidia-ngc-public/tritonserver
-  tag: 24.06-py3
+  tag: 24.07-py3
   pullPolicy: IfNotPresent
   # modify the model repository here to match your GCP storage bucket
   numGpus: 1

deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -27,7 +27,7 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '2.47.0'
+  publishedVersion: '2.48.0'
   publishedVersionMetadata:
     releaseNote: >-
       Initial release.

deploy/gke-marketplace-app/server-deployer/schema.yaml

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -27,7 +27,7 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '2.47.0'
+  publishedVersion: '2.48.0'
   publishedVersionMetadata:
     releaseNote: >-
       Initial release.
@@ -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/24.06
+    default: gs://triton_sample_models/24.07
   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.