Update docs and examples to use Triton 23.06

.devcontainer/docker-compose.yml

@@ -20,7 +20,7 @@ services:
   triton:
     container_name: morpheus-triton
     runtime: nvidia
-    image: nvcr.io/nvidia/tritonserver:22.10-py3
+    image: nvcr.io/nvidia/tritonserver:23.06-py3
     command: tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false ${TRITON_MODEL_ARGS}
     ports:
       - 8000:8000

docs/source/basics/building_a_pipeline.md

@@ -223,7 +223,7 @@ This example shows an NLP Pipeline which uses several stages available in Morphe
 #### Launching Triton
 From the Morpheus repo root directory, run the following to launch Triton and load the `sid-minibert` model:
 ```bash
-docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:22.08-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model sid-minibert-onnx
+docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model sid-minibert-onnx
 ```
 
 #### Launching Kafka

docs/source/developer_guide/guides/2_real_world_phishing.md

@@ -194,7 +194,7 @@ From the root of the Morpheus project we will launch a Triton Docker container w
 ```shell
 docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 \
   -v $PWD/models:/models \
-  nvcr.io/nvidia/tritonserver:22.08-py3 \
+  nvcr.io/nvidia/tritonserver:23.06-py3 \
   tritonserver --model-repository=/models/triton-model-repo \
     --exit-on-error=false \
     --log-info=true \

docs/source/getting_started.md

@@ -31,7 +31,7 @@ More advanced users, or those who are interested in using the latest pre-release
 - NVIDIA driver `450.80.02` or higher
 - [Docker](https://docs.docker.com/get-docker/)
 - [The NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker)
-- [NVIDIA Triton Inference Server](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver) `22.06` or higher
+- [NVIDIA Triton Inference Server](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver) `23.06` or higher
 
 > **Note about Docker:**
 >
@@ -146,7 +146,7 @@ Many of the validation tests and example workflows require a Triton server to fu
 ```bash
 docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 \
   -v $PWD/models:/models \
-  nvcr.io/nvidia/tritonserver:22.08-py3 \
+  nvcr.io/nvidia/tritonserver:23.06-py3 \
   tritonserver --model-repository=/models/triton-model-repo \
     --exit-on-error=false \
     --log-info=true \
@@ -160,7 +160,7 @@ Note: The above command is useful for testing out Morpheus, however it does load
 ```bash
 docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 \
   -v $PWD/models:/models \
-  nvcr.io/nvidia/tritonserver:22.08-py3 \
+  nvcr.io/nvidia/tritonserver:23.06-py3 \
   tritonserver --model-repository=/models/triton-model-repo \
     --exit-on-error=false \
     --log-info=true \

examples/abp_nvsmi_detection/README.md

@@ -65,12 +65,12 @@ This example utilizes the Triton Inference Server to perform inference.
 
 Pull the Docker image for Triton:
 ```bash
-docker pull nvcr.io/nvidia/tritonserver:22.08-py3
+docker pull nvcr.io/nvidia/tritonserver:23.06-py3
 ```
 
 From the Morpheus repo root directory, run the following to launch Triton and load the `abp-nvsmi-xgb` XGBoost model:
 ```bash
-docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:22.08-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model abp-nvsmi-xgb
+docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model abp-nvsmi-xgb
 ```
 
 This will launch Triton and only load the `abp-nvsmi-xgb` model. This model has been configured with a max batch size of 32768, and to use dynamic batching for increased performance.

examples/abp_pcap_detection/README.md

@@ -23,7 +23,7 @@ To run this example, an instance of Triton Inference Server and a sample dataset
 
 ### Triton Inference Server
 ```bash
-docker pull nvcr.io/nvidia/tritonserver:22.08-py3
+docker pull nvcr.io/nvidia/tritonserver:23.06-py3
 ```
 
 ##### Deploy Triton Inference Server
@@ -35,7 +35,7 @@ From the root of the Morpheus repo, navigate to the anomalous behavior profiling
 cd examples/abp_pcap_detection
 
 # Launch the container
-docker run --rm --gpus=all -p 8000:8000 -p 8001:8001 -p 8002:8002 -v $PWD/abp-pcap-xgb:/models/abp-pcap-xgb --name tritonserver nvcr.io/nvidia/tritonserver:22.08-py3 tritonserver --model-repository=/models --exit-on-error=false
+docker run --rm --gpus=all -p 8000:8000 -p 8001:8001 -p 8002:8002 -v $PWD/abp-pcap-xgb:/models/abp-pcap-xgb --name tritonserver nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models --exit-on-error=false
 ```
 
 ##### Verify Model Deployment

examples/log_parsing/README.md

@@ -26,7 +26,7 @@ Pull Docker image from NGC (https://ngc.nvidia.com/catalog/containers/nvidia:tri
 Example:
 
 ```bash
-docker pull nvcr.io/nvidia/tritonserver:22.08-py3
+docker pull nvcr.io/nvidia/tritonserver:23.06-py3
 ```
 
 ##### Setup Env Variable
@@ -38,7 +38,7 @@ export MORPHEUS_ROOT=$(pwd)
 From the Morpheus repo root directory, run the following to launch Triton and load the `log-parsing-onnx` model:
 
 ```bash
-docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:22.08-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model log-parsing-onnx
+docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model log-parsing-onnx
 ```
 
 ##### Verify Model Deployment

examples/nlp_si_detection/README.md

@@ -77,10 +77,10 @@ This example utilizes the Triton Inference Server to perform inference. The neur
 From the Morpheus repo root directory, run the following to launch Triton and load the `sid-minibert` model:
 
 ```bash
-docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:22.08-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model sid-minibert-onnx
+docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model sid-minibert-onnx
 ```
 
-Where `22.02-py3` can be replaced with the current year and month of the Triton version to use. For example, to use May 2021, specify `nvcr.io/nvidia/tritonserver:21.05-py3`. Ensure that the version of TensorRT that is used in Triton matches the version of TensorRT elsewhere (refer to [NGC Deep Learning Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)).
+Where `23.06-py3` can be replaced with the current year and month of the Triton version to use. For example, to use May 2021, specify `nvcr.io/nvidia/tritonserver:21.05-py3`. Ensure that the version of TensorRT that is used in Triton matches the version of TensorRT elsewhere (refer to [NGC Deep Learning Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)).
 
 This will launch Triton and only load the `sid-minibert-onnx` model. This model has been configured with a max batch size of 32, and to use dynamic batching for increased performance.
 

examples/ransomware_detection/README.md

@@ -27,7 +27,7 @@ Pull Docker image from NGC (https://ngc.nvidia.com/catalog/containers/nvidia:tri
 Example:
 
 ```bash
-docker pull nvcr.io/nvidia/tritonserver:22.08-py3
+docker pull nvcr.io/nvidia/tritonserver:23.06-py3
 ```
 ##### Setup Env Variable
 ```bash
@@ -39,7 +39,7 @@ Run the following from the `examples/ransomware_detection` directory to launch T
 
 ```bash
 # Run Triton in explicit mode
-docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models/triton-model-repo nvcr.io/nvidia/tritonserver:22.08-py3 \
+docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models/triton-model-repo nvcr.io/nvidia/tritonserver:23.06-py3 \
    tritonserver --model-repository=/models/triton-model-repo \
                 --exit-on-error=false \
                 --model-control-mode=explicit \

examples/root_cause_analysis/README.md

@@ -46,10 +46,10 @@ This example utilizes the Triton Inference Server to perform inference. The bina
 From the Morpheus repo root directory, run the following to launch Triton and load the `root-cause-binary-onnx` model:
 
 ```bash
-docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:22.08-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model root-cause-binary-onnx
+docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model root-cause-binary-onnx
 ```
 
-Where `22.08-py3` can be replaced with the current year and month of the Triton version to use. For example, to use May 2021, specify `nvcr.io/nvidia/tritonserver:21.05-py3`. Ensure that the version of TensorRT that is used in Triton matches the version of TensorRT elsewhere (refer to [NGC Deep Learning Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)).
+Where `23.06-py3` can be replaced with the current year and month of the Triton version to use. For example, to use May 2021, specify `nvcr.io/nvidia/tritonserver:21.05-py3`. Ensure that the version of TensorRT that is used in Triton matches the version of TensorRT elsewhere (refer to [NGC Deep Learning Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html)).
 
 This will launch Triton and only load the model required by our example pipeline. The model has been configured with a max batch size of 32, and to use dynamic batching for increased performance.
 

examples/sid_visualization/docker-compose.yml

@@ -25,7 +25,7 @@ x-with-gpus: &with_gpus
 
 services:
   triton:
-    image: nvcr.io/nvidia/tritonserver:22.08-py3
+    image: nvcr.io/nvidia/tritonserver:23.06-py3
     <<: *with_gpus
     command: "tritonserver --exit-on-error=false --model-control-mode=explicit --load-model sid-minibert-onnx --model-repository=/models/triton-model-repo"
     environment:

scripts/validation/val-globals.sh

@@ -26,7 +26,7 @@ export e="\033[0;90m"
 export y="\033[0;33m"
 export x="\033[0m"
 
-export TRITON_IMAGE=${TRITON_IMAGE:-"nvcr.io/nvidia/tritonserver:22.08-py3"}
+export TRITON_IMAGE=${TRITON_IMAGE:-"nvcr.io/nvidia/tritonserver:23.06-py3"}
 
 # TRITON_GRPC_PORT is only used when TRITON_URL is undefined
 export TRITON_GRPC_PORT=${TRITON_GRPC_PORT:-"8001"}

scripts/validation/val-utils.sh

@@ -68,7 +68,7 @@ function wait_for_triton {
 
 function ensure_triton_running {
 
-   TRITON_IMAGE=${TRITON_IMAGE:-"nvcr.io/nvidia/tritonserver:22.08-py3"}
+   TRITON_IMAGE=${TRITON_IMAGE:-"nvcr.io/nvidia/tritonserver:23.06-py3"}
 
    IS_RUNNING=$(is_triton_running)
 

tests/benchmarks/README.md

@@ -24,14 +24,14 @@ Pull Docker image from NGC (https://ngc.nvidia.com/catalog/containers/nvidia:tri
 Example:
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:23.03-py3
+docker pull nvcr.io/nvidia/tritonserver:23.06-py3
 ```
 
 ##### Start Triton Inference Server container
 ```
 cd ${MORPHEUS_ROOT}/models
 
-docker run --gpus=1 --rm -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD:/models nvcr.io/nvidia/tritonserver:23.03-py3 tritonserver --model-repository=/models/triton-model-repo --model-control-mode=explicit --load-model sid-minibert-onnx --load-model abp-nvsmi-xgb --load-model phishing-bert-onnx
+docker run --gpus=1 --rm -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD:/models nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models/triton-model-repo --model-control-mode=explicit --load-model sid-minibert-onnx --load-model abp-nvsmi-xgb --load-model phishing-bert-onnx
 ```
 
 ##### Verify Model Deployments