Update 'main' post-24.03 (#7051)

* Update README and versions for 2.44.0 / 24.03 (#6971) * Update README and versions for 2.44.0 / 24.03 * Mchornyi 24.03 (#6972) * Current location is dropped in 12.4 * Update Dockerfile.win10.min * Change to triton_sample_folder (#6973) --------- Co-authored-by: kyle <[email protected]> Co-authored-by: Misha Chornyi <[email protected]> * Specify path for PyTorch model extension library (#7025) * Update README.md 2.44.0 / 24.03 (#7032) * Update README.md post-24.03 --------- Co-authored-by: Kyle McGill <[email protected]> Co-authored-by: kyle <[email protected]>
triton-inference-server · Apr 1, 2024 · 6cab4bb · 6cab4bb
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diff --git a/Dockerfile.sdk b/Dockerfile.sdk
@@ -29,7 +29,7 @@
 #
 
 # Base image on the minimum Triton container
-ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.02-py3-min
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.03-py3-min
 
 ARG TRITON_CLIENT_REPO_SUBDIR=clientrepo
 ARG TRITON_COMMON_REPO_TAG=main

diff --git a/Dockerfile.win10.min b/Dockerfile.win10.min
@@ -65,18 +65,6 @@ FROM ${BASE_IMAGE} as build_base
 
 SHELL ["cmd", "/S", "/C"]
 
-ARG CUDNN_VERSION
-ENV CUDNN_VERSION ${CUDNN_VERSION}
-COPY --from=dependency_base /cudnn /cudnn
-RUN setx PATH "c:\cudnn\bin;c:\cudnn\lib\x64;c:\cudnn\include;%PATH%"
-LABEL CUDNN_VERSION="${CUDNN_VERSION}"
-
-ARG TENSORRT_VERSION
-ENV TRT_VERSION ${TENSORRT_VERSION}
-COPY --from=dependency_base /TensorRT /TensorRT
-RUN setx PATH "c:\TensorRT\lib;%PATH%"
-LABEL TENSORRT_VERSION="${TENSORRT_VERSION}"
-
 RUN mkdir c:\tmp
 WORKDIR /tmp
 
@@ -87,23 +75,31 @@ RUN choco install git docker unzip -y
 #
 # Installing python
 #
-ARG PYTHON_VERSION=3.10.11
+ARG PYTHON_VERSION=3.8.10
 ARG PYTHON_SOURCE=https://www.python.org/ftp/python/${PYTHON_VERSION}/python-${PYTHON_VERSION}-amd64.exe
 ADD ${PYTHON_SOURCE} python-${PYTHON_VERSION}-amd64.exe
 RUN python-%PYTHON_VERSION%-amd64.exe /quiet InstallAllUsers=1 PrependPath=1 Include_doc=0 TargetDir="C:\python%PYTHON_VERSION%"
 RUN mklink "C:\python%PYTHON_VERSION%\python3.exe" "C:\python%PYTHON_VERSION%\python.exe"
 RUN pip install --upgrade wheel setuptools docker
-RUN pip install grpcio-tools
+RUN pip install grpcio-tools psutil
 
 LABEL PYTHON_VERSION=${PYTHON_VERSION}
 
 #
 # Installing CMake
 #
 ARG CMAKE_VERSION=3.27.1
-RUN pip install cmake==%CMAKE_VERSION%
+ARG CMAKE_FILE=cmake-${CMAKE_VERSION}-windows-x86_64
+ARG CMAKE_SOURCE=https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/${CMAKE_FILE}.zip
+
+ADD ${CMAKE_SOURCE} ${CMAKE_FILE}.zip
+RUN unzip %CMAKE_FILE%.zip
+RUN move %CMAKE_FILE% "c:\CMake"
+RUN setx PATH "c:\CMake\bin;%PATH%"
+
 ENV CMAKE_TOOLCHAIN_FILE /vcpkg/scripts/buildsystems/vcpkg.cmake
 ENV VCPKG_TARGET_TRIPLET x64-windows
+
 LABEL CMAKE_VERSION=${CMAKE_VERSION}
 
 # Be aware that pip can interact badly with VS cmd shell so need to pip install before
@@ -190,6 +186,20 @@ RUN copy "%CUDA_INSTALL_ROOT_WP%\extras\visual_studio_integration\MSBuildExtensi
 
 RUN setx PATH "%CUDA_INSTALL_ROOT_WP%\bin;%PATH%"
 
+ARG CUDNN_VERSION
+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
+ENV TRT_VERSION ${TENSORRT_VERSION}
+COPY --from=dependency_base /TensorRT /TensorRT
+RUN setx PATH "c:\TensorRT\lib;%PATH%"
+LABEL TENSORRT_VERSION="${TENSORRT_VERSION}"
+
 LABEL CUDA_VERSION="${CUDA_VERSION}"
 # It is important that the entrypoint initialize VisualStudio
 # environment otherwise the build will fail. Also set

diff --git a/README.md b/README.md
@@ -46,7 +46,7 @@ Inference Server.
 > [!WARNING]
 > ##### LATEST RELEASE
 > You are currently on the `main` branch which tracks under-development progress towards the next release.
-> The current release is version [2.43.0](https://github.com/triton-inference-server/server/releases/latest) and corresponds to the 24.02 container release on NVIDIA GPU Cloud (NGC).
+> The current release is version [2.44.0](https://github.com/triton-inference-server/server/releases/latest) and corresponds to the 24.03 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
@@ -104,16 +104,16 @@ Inference Server with the
 
 ```bash
 # Step 1: Create the example model repository
-git clone -b r24.02 https://github.com/triton-inference-server/server.git
+git clone -b r24.03 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:24.02-py3 tritonserver --model-repository=/models
+docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:24.03-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:24.02-py3-sdk
+docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:24.03-py3-sdk
 /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
 
 # Inference should return the following

diff --git a/build.py b/build.py
@@ -72,7 +72,7 @@
 TRITON_VERSION_MAP = {
     "2.45.0dev": (
         "24.04dev",  # triton container
-        "24.02",  # upstream container
+        "24.03",  # upstream container
         "1.17.2",  # ORT
         "2023.3.0",  # ORT OpenVINO
         "2023.3.0",  # Standalone OpenVINO

diff --git a/deploy/aws/values.yaml b/deploy/aws/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:24.02-py3
+  imageName: nvcr.io/nvidia/tritonserver:24.03-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: s3://triton-inference-server-repository/model_repository
   numGpus: 1

diff --git a/deploy/fleetcommand/Chart.yaml b/deploy/fleetcommand/Chart.yaml
@@ -26,7 +26,7 @@
 
 apiVersion: v1
 # appVersion is the Triton version; update when changing release
-appVersion: "2.43.0"
+appVersion: "2.44.0"
 description: Triton Inference Server (Fleet Command)
 name: triton-inference-server
 # version is the Chart version; update when changing anything in the chart

diff --git a/deploy/fleetcommand/values.yaml b/deploy/fleetcommand/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:24.02-py3
+  imageName: nvcr.io/nvidia/tritonserver:24.03-py3
   pullPolicy: IfNotPresent
   numGpus: 1
   serverCommand: tritonserver
@@ -46,13 +46,13 @@ image:
     # Model Control Mode (Optional, default: none)
     #
     # To set model control mode, uncomment and configure below
-    # See https://github.com/triton-inference-server/server/blob/r24.02/docs/model_management.md
+    # See https://github.com/triton-inference-server/server/blob/r24.03/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/r24.02/README.md
+    # see https://github.com/triton-inference-server/server/blob/r24.03/README.md
     #  for more details
 
 service:

diff --git a/deploy/gcp/values.yaml b/deploy/gcp/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:24.02-py3
+  imageName: nvcr.io/nvidia/tritonserver:24.03-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: gs://triton-inference-server-repository/model_repository
   numGpus: 1

diff --git a/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml b/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml
@@ -33,7 +33,7 @@ metadata:
   namespace: default
 spec:
   containers:
-  - image: nvcr.io/nvidia/tritonserver:24.02-py3-sdk
+  - image: nvcr.io/nvidia/tritonserver:24.03-py3-sdk
     imagePullPolicy: Always
     name: nv-triton-client
     securityContext:

diff --git a/deploy/gke-marketplace-app/server-deployer/build_and_push.sh b/deploy/gke-marketplace-app/server-deployer/build_and_push.sh
@@ -28,8 +28,8 @@
 export REGISTRY=gcr.io/$(gcloud config get-value project | tr ':' '/')
 export APP_NAME=tritonserver
 export MAJOR_VERSION=2.41
-export MINOR_VERSION=2.43.0
-export NGC_VERSION=24.02-py3
+export MINOR_VERSION=2.44.0
+export NGC_VERSION=24.03-py3
 
 docker pull nvcr.io/nvidia/$APP_NAME:$NGC_VERSION
 

diff --git a/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml b/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml
@@ -28,4 +28,4 @@ apiVersion: v1
 appVersion: "2.41"
 description: Triton Inference Server
 name: triton-inference-server
-version: 2.43.0
+version: 2.44.0
diff --git a/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml b/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml
@@ -31,14 +31,14 @@ maxReplicaCount: 3
 tritonProtocol: HTTP
 # HPA GPU utilization autoscaling target
 HPATargetAverageValue: 85
-modelRepositoryPath: gs://triton_sample_models/23_12
-publishedVersion: '2.43.0'
+modelRepositoryPath: gs://triton_sample_models/24_03
+publishedVersion: '2.44.0'
 gcpMarketplace: true
 
 image:
   registry: gcr.io
   repository: nvidia-ngc-public/tritonserver
-  tag: 24.02-py3
+  tag: 24.03-py3
   pullPolicy: IfNotPresent
   # modify the model repository here to match your GCP storage bucket
   numGpus: 1

diff --git a/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml b/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml
@@ -27,7 +27,7 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '2.43.0'
+  publishedVersion: '2.44.0'
   publishedVersionMetadata:
     releaseNote: >-
       Initial release.

diff --git a/deploy/gke-marketplace-app/server-deployer/schema.yaml b/deploy/gke-marketplace-app/server-deployer/schema.yaml
@@ -27,7 +27,7 @@
 x-google-marketplace:
   schemaVersion: v2
   applicationApiVersion: v1beta1
-  publishedVersion: '2.43.0'
+  publishedVersion: '2.44.0'
   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/23_12
+    default: gs://triton_sample_models/24_03
   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.

diff --git a/deploy/gke-marketplace-app/trt-engine/README.md b/deploy/gke-marketplace-app/trt-engine/README.md
@@ -33,7 +33,7 @@
 ```
 docker run --gpus all -it --network host \
     --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
-    -v ~:/scripts nvcr.io/nvidia/tensorrt:24.02-py3
+    -v ~:/scripts nvcr.io/nvidia/tensorrt:24.03-py3
 
 pip install onnx six torch tf2onnx tensorflow
 
@@ -57,7 +57,7 @@ mkdir -p engines
 
 python3 builder.py -m models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/model.ckpt -o engines/bert_large_int8_bs1_s128.engine -b 1 -s 128 -c models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/ -v models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/vocab.txt --int8 --fp16 --strict --calib-num 1 -iln -imh
 
-gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/23_12/bert/1/model.plan
+gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/24_03/bert/1/model.plan
 ```
 
-For each Triton upgrade, container version used to generate the model, and the model path in GCS `gs://triton_sample_models/23_12/` should be updated accordingly with the correct version.
+For each Triton upgrade, container version used to generate the model, and the model path in GCS `gs://triton_sample_models/24_03/` should be updated accordingly with the correct version.
diff --git a/deploy/k8s-onprem/values.yaml b/deploy/k8s-onprem/values.yaml
@@ -29,7 +29,7 @@ tags:
   loadBalancing: true
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:24.02-py3
+  imageName: nvcr.io/nvidia/tritonserver:24.03-py3
   pullPolicy: IfNotPresent
   modelRepositoryServer: < Replace with the IP Address of your file server >
   modelRepositoryPath: /srv/models

diff --git a/deploy/oci/values.yaml b/deploy/oci/values.yaml
@@ -27,7 +27,7 @@
 replicaCount: 1
 
 image:
-  imageName: nvcr.io/nvidia/tritonserver:24.02-py3
+  imageName: nvcr.io/nvidia/tritonserver:24.03-py3
   pullPolicy: IfNotPresent
   modelRepositoryPath: s3://https://<OCI_NAMESPACE>.compat.objectstorage.<OCI_REGION>.oraclecloud.com:443/triton-inference-server-repository
   numGpus: 1

diff --git a/docs/customization_guide/build.md b/docs/customization_guide/build.md
@@ -173,7 +173,7 @@ $ ./build.py ... --repo-tag=common:<container tag> --repo-tag=core:<container ta
 
 If you are building on a release branch then `<container tag>` will
 default to the branch name. For example, if you are building on the
-r24.02 branch, `<container tag>` will default to r24.02. If you are
+r24.03 branch, `<container tag>` will default to r24.03. If you are
 building on any other branch (including the *main* branch) then
 `<container tag>` will default to "main". Therefore, you typically do
 not need to provide `<container tag>` at all (nor the preceding
@@ -334,8 +334,8 @@ python build.py --cmake-dir=<path/to/repo>/build --build-dir=/tmp/citritonbuild
 If you are building on *main* branch then '<container tag>' will
 default to "main". If you are building on a release branch then
 '<container tag>' will default to the branch name. For example, if you
-are building on the r24.02 branch, '<container tag>' will default to
-r24.02. Therefore, you typically do not need to provide '<container
+are building on the r24.03 branch, '<container tag>' will default to
+r24.03. Therefore, you typically do not need to provide '<container
 tag>' at all (nor the preceding colon). You can use a different
 '<container tag>' for a component to instead use the corresponding
 branch/tag in the build. For example, if you have a branch called

diff --git a/docs/customization_guide/compose.md b/docs/customization_guide/compose.md
@@ -44,8 +44,8 @@ from source to get more exact customization.
 The `compose.py` script can be found in the [server repository](https://github.com/triton-inference-server/server).
 Simply clone the repository and run `compose.py` to create a custom container.
 Note: Created container version will depend on the branch that was cloned.
-For example branch [r24.02](https://github.com/triton-inference-server/server/tree/r24.02)
-should be used to create a image based on the NGC 24.02 Triton release.
+For example branch [r24.03](https://github.com/triton-inference-server/server/tree/r24.03)
+should be used to create a image based on the NGC 24.03 Triton release.
 
 `compose.py` provides `--backend`, `--repoagent` options that allow you to
 specify which backends and repository agents to include in the custom image.
@@ -76,19 +76,19 @@ For example, running
 ```
 python3 compose.py --backend tensorflow1 --repoagent checksum
 ```
-on branch [r24.02](https://github.com/triton-inference-server/server/tree/r24.02) pulls:
-- `min` container `nvcr.io/nvidia/tritonserver:24.02-py3-min`
-- `full` container `nvcr.io/nvidia/tritonserver:24.02-py3`
+on branch [r24.03](https://github.com/triton-inference-server/server/tree/r24.03) pulls:
+- `min` container `nvcr.io/nvidia/tritonserver:24.03-py3-min`
+- `full` container `nvcr.io/nvidia/tritonserver:24.03-py3`
 
 Alternatively, users can specify the version of Triton container to pull from any branch by either:
 1. Adding flag `--container-version <container version>` to branch
 ```
-python3 compose.py --backend tensorflow1 --repoagent checksum --container-version 24.02
+python3 compose.py --backend tensorflow1 --repoagent checksum --container-version 24.03
 ```
 2. Specifying `--image min,<min container image name> --image full,<full container image name>`.
    The user is responsible for specifying compatible `min` and `full` containers.
 ```
-python3 compose.py --backend tensorflow1 --repoagent checksum --image min,nvcr.io/nvidia/tritonserver:24.02-py3-min --image full,nvcr.io/nvidia/tritonserver:24.02-py3
+python3 compose.py --backend tensorflow1 --repoagent checksum --image min,nvcr.io/nvidia/tritonserver:24.03-py3-min --image full,nvcr.io/nvidia/tritonserver:24.03-py3
 ```
 Method 1 and 2 will result in the same composed container. Furthermore, `--image` flag overrides the `--container-version` flag when both are specified.
 

diff --git a/docs/customization_guide/test.md b/docs/customization_guide/test.md
@@ -49,7 +49,7 @@ $ ./gen_qa_custom_ops
 ```
 
 This will create multiple model repositories in /tmp/<version>/qa_*
-(for example /tmp/24.02/qa_model_repository).  The TensorRT models
+(for example /tmp/24.03/qa_model_repository).  The TensorRT models
 will be created for the GPU on the system that CUDA considers device 0
 (zero). If you have multiple GPUs on your system see the documentation
 in the scripts for how to target a specific GPU.

diff --git a/docs/user_guide/custom_operations.md b/docs/user_guide/custom_operations.md
@@ -64,7 +64,7 @@ simple way to ensure you are using the correct version of TensorRT is
 to use the [NGC TensorRT
 container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorrt)
 corresponding to the Triton container. For example, if you are using
-the 24.02 version of Triton, use the 24.02 version of the TensorRT
+the 24.03 version of Triton, use the 24.03 version of the TensorRT
 container.
 
 ## TensorFlow
@@ -123,7 +123,7 @@ simple way to ensure you are using the correct version of TensorFlow
 is to use the [NGC TensorFlow
 container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow)
 corresponding to the Triton container. For example, if you are using
-the 24.02 version of Triton, use the 24.02 version of the TensorFlow
+the 24.03 version of Triton, use the 24.03 version of the TensorFlow
 container.
 
 ## PyTorch
@@ -167,7 +167,7 @@ simple way to ensure you are using the correct version of PyTorch is
 to use the [NGC PyTorch
 container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch)
 corresponding to the Triton container. For example, if you are using
-the 24.02 version of Triton, use the 24.02 version of the PyTorch
+the 24.03 version of Triton, use the 24.03 version of the PyTorch
 container.
 
 ## ONNX

diff --git a/docs/user_guide/metrics.md b/docs/user_guide/metrics.md
@@ -285,7 +285,7 @@ If building Triton locally, the `TRITON_ENABLE_METRICS_CPU` CMake build flag can
 
 ## Pinned Memory Metrics
 
-Starting in 24.02, Triton offers Pinned Memory metrics to monitor the utilization of the Pinned Memory pool.
+Starting in 24.03, Triton offers Pinned Memory metrics to monitor the utilization of the Pinned Memory pool.
 
 |Category        |Metric            |Metric Name                 |Description                                            |Granularity|Frequency    |
 |----------------|------------------|----------------------------|-------------------------------------------------------|-----------|-------------|