Merge branch 'branch-22.09' of https://github.com/nv-morpheus/Morpheus …

…into starter-dfp-docstring

README.md

@@ -104,7 +104,7 @@ Use the following command to launch a Docker container for Triton loading all of
 ```bash
 docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 \
 	-v $PWD/models:/models \
-	nvcr.io/nvidia/tritonserver:22.06-py3 \
+	nvcr.io/nvidia/tritonserver:22.08-py3 \
 	tritonserver --model-repository=/models/triton-model-repo \
 		--exit-on-error=false \
 		--log-info=true \

cmake/dependencies.cmake

@@ -19,7 +19,6 @@ list(APPEND CMAKE_MESSAGE_CONTEXT "dep")
 set(CPM_SOURCE_CACHE "${CMAKE_SOURCE_DIR}/.cache/cpm")
 # Prevent cpm_init from trying to tell us where to put cpm.cmake
 include(get_cpm)
-rapids_cpm_init(OVERRIDE "${CMAKE_CURRENT_SOURCE_DIR}/cmake/deps/rapids_cpm_package_overrides.json")
 
 # Cant use rapids_cpm_init() for now since the `rapids_cpm_download()` creates a
 # new scope when importing CPM. Manually do the other commands and import CPM on
@@ -84,7 +83,7 @@ endif()
 
 # libcudacxx -- get an explicit lubcudacxx build, matx tries to pull a tag that doesn't exist.
 # =========
-set(LIBCUDACXX_VERSION "1.6.0" CACHE STRING "Version of libcudacxx to use")
+set(LIBCUDACXX_VERSION "1.8.0" CACHE STRING "Version of libcudacxx to use")
 include(deps/Configure_libcudacxx)
 
 # matx

docker/Dockerfile

@@ -145,6 +145,9 @@ RUN --mount=type=bind,from=conda_bld_morpheus,source=/opt/conda/conda-bld,target
     CONDA_ALWAYS_YES=true /opt/conda/bin/mamba install -n morpheus -c local -c rapidsai -c nvidia -c nvidia/label/dev -c conda-forge morpheus &&\
     # Install runtime dependencies that are pip-only
     /opt/conda/bin/mamba env update -n morpheus --file docker/conda/environments/cuda${CUDA_VER}_runtime.yml &&\
+    # Install jupyter support (e.g., DFP)
+    # TODO: this might not be the right spot to get these
+    /opt/conda/bin/mamba install -n morpheus ipywidgets jupyterlab nb_conda_kernels &&\
     # Clean and activate
     conda clean -afy
 

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

@@ -149,7 +149,7 @@ Note: This step assumes you have both [Docker](https://docs.docker.com/engine/in
 From the root of the Morpheus project we will launch a Triton Docker container with the `models` directory mounted into the container:
 
 ```shell
-docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:22.02-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --log-info=true
+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 --log-info=true
 ```
 
 Once we have Triton running, we can verify that it is healthy using [curl](https://curl.se/). The `/v2/health/live` endpoint should return a 200 status code:

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:22.02-py3
+docker pull nvcr.io/nvidia/tritonserver:22.08-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:22.02-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:22.08-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:22.02-py3
+docker pull nvcr.io/nvidia/tritonserver:22.08-py3
 ```
 
 ##### Deploy Triton Inference Server
@@ -35,7 +35,7 @@ Bind the provided `abp-pcap-xgb` directory to the docker container model repo at
 cd <MORPHEUS_ROOT>/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:22.02-py3 tritonserver --model-repository=/models --exit-on-error=false --model-control-mode=poll --repository-poll-secs=30
+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 --model-control-mode=poll --repository-poll-secs=30
 ```
 
 ##### Verify Model Deployment