Update mlflow to avoid CVE-2024-27132 and CVE-2024-27133

ci/conda/recipes/morpheus/meta.yaml

@@ -91,7 +91,7 @@ outputs:
         - grpcio # Version determined from cudf
         - libmrc
         - libwebp>=1.3.2 # Required for CVE mitigation: https://nvd.nist.gov/vuln/detail/CVE-2023-4863
-        - mlflow>=2.2.1,<3
+        - mlflow>=2.10.0,<3
         - mrc
         - networkx>=2.8
         - numpydoc =1.5.*

conda/environments/all_cuda-121_arch-x86_64.yaml

@@ -56,7 +56,7 @@ dependencies:
 - librdkafka>=1.9.2,<1.10.0a0
 - libtool
 - libwebp=1.3.2
-- mlflow=2.9.2
+- mlflow>=2.10.0,<3
 - mrc=24.03
 - myst-parser=0.18.1
 - nbsphinx

conda/environments/dev_cuda-121_arch-x86_64.yaml

@@ -45,7 +45,7 @@ dependencies:
 - isort
 - librdkafka>=1.9.2,<1.10.0a0
 - libtool
-- mlflow=2.9.2
+- mlflow>=2.10.0,<3
 - mrc=24.03
 - myst-parser=0.18.1
 - nbsphinx

conda/environments/examples_cuda-121_arch-x86_64.yaml

@@ -27,7 +27,7 @@ dependencies:
 - jsonpatch>=1.33
 - kfp
 - libwebp=1.3.2
-- mlflow=2.9.2
+- mlflow>=2.10.0,<3
 - networkx=2.8.8
 - newspaper3k=0.2
 - nodejs=18.*

conda/environments/runtime_cuda-121_arch-x86_64.yaml

@@ -16,7 +16,7 @@ dependencies:
 - elasticsearch==8.9.0
 - feedparser=6.0.10
 - grpcio=1.59
-- mlflow=2.9.2
+- mlflow>=2.10.0,<3
 - networkx=2.8.8
 - numpydoc=1.5
 - nvtabular=23.08.00

dependencies.yaml

@@ -253,7 +253,7 @@ dependencies:
           - elasticsearch==8.9.0
           - feedparser=6.0.10
           - grpcio=1.59
-          - mlflow=2.9.2
+          - mlflow>=2.10.0,<3
           - networkx=2.8.8
           - nvtabular=23.08.00
           - pydantic
@@ -301,7 +301,7 @@ dependencies:
           - dask=2023.12.1
           - distributed=2023.12.1
           - kfp
-          - mlflow=2.9.2
+          - mlflow>=2.10.0,<3
           - papermill=2.4.0
           - s3fs=2023.12.2
 

examples/digital_fingerprinting/production/conda_env.yml

@@ -27,7 +27,7 @@ dependencies:
     - distributed
     - kfp
     - librdkafka
-    - mlflow>=2.2.1,<3
+    - mlflow>=2.10.0,<3
     - nodejs=18.*
     - nvtabular=23.06
     - papermill

examples/digital_fingerprinting/production/mlflow/Dockerfile

@@ -24,7 +24,7 @@ RUN apt update && \
     rm -rf /var/cache/apt/* /var/lib/apt/lists/*
 
 # Install python packages
-RUN pip install "mlflow >=2.2.1,<3" boto3 pymysql pyyaml
+RUN pip install "mlflow >=2.10.0,<3" boto3 pymysql pyyaml
 
 # We run on port 5000
 EXPOSE 5000

models/mlflow/README.md

@@ -22,8 +22,8 @@ are included for publishing TensorRT, ONNX and FIL models to your MLflow Model R
 
 ## Requirements
 
-* MLflow (tested on 1.24.0)
-* Python (tested on 3.8)
+* MLflow (tested on 2.11.3)
+* Python (tested on 3.11)
 
 ## Install Triton Docker Image
 
@@ -89,7 +89,7 @@ Create an MLflow container with a volume mounting the Triton model repository:
 ```bash
 docker run -it -v /opt/triton_models:/triton_models \
 --env TRITON_MODEL_REPO=/triton_models \
---env MLFLOW_TRACKING_URI=localhost:5000 \
+--env MLFLOW_TRACKING_URI="http://localhost:5000" \
 --gpus '"device=0"' \
 --net=host \
 --rm \
@@ -115,29 +115,29 @@ The `publish_model_to_mlflow` script is used to publish `triton` flavor models t
 ```
 python publish_model_to_mlflow.py \
 	--model_name sid-minibert-onnx \
-	--model_directory <path-to-morpheus-models-repo>/models/triton-model-repo/sid-minibert-onnx \
+	--model_directory /triton_models/triton-model-repo/sid-minibert-onnx \
     --flavor triton
 ```
 
 ## Deployments
 
 The Triton `mlflow-triton-plugin` is installed on this container and can be used to deploy your models from MLflow to Triton Inference Server. The following are examples of how the plugin is used with the `sid-minibert-onnx` model that we published to MLflow above. For more information about the
-`mlflow-triton-plugin`, refer to Triton's [documentation](https://github.com/triton-inference-server/server/tree/r23.01/deploy/mlflow-triton-plugin)
+`mlflow-triton-plugin`, refer to Triton's [documentation](https://github.com/triton-inference-server/server/tree/r24.03/deploy/mlflow-triton-plugin)
 
 ### Create Deployment
 
 To create a deployment use the following command
 
 ##### CLI
 ```
-mlflow deployments create -t triton --flavor triton --name sid-minibert-onnx -m models:/sid-minibert-onnx/1
+mlflow deployments create -t triton --flavor triton --name sid-minibert-onnx -m "models:/sid-minibert-onnx/1"
 ```
 
 ##### Python API
 ```
 from mlflow.deployments import get_deploy_client
 client = get_deploy_client('triton')
-client.create_deployment("sid-minibert-onnx", " models:/sid-minibert-onnx/1", flavor="triton")
+client.create_deployment("sid-minibert-onnx", "models:/sid-minibert-onnx/1", flavor="triton")
 ```
 
 ### Delete Deployment
@@ -158,14 +158,14 @@ client.delete_deployment("sid-minibert-onnx")
 
 ##### CLI
 ```
-mlflow deployments update -t triton --flavor triton --name sid-minibert-onnx -m models:/sid-minibert-onnx/2
+mlflow deployments update -t triton --flavor triton --name sid-minibert-onnx -m "models:/sid-minibert-onnx/1"
 ```
 
 ##### Python API
 ```
 from mlflow.deployments import get_deploy_client
 client = get_deploy_client('triton')
-client.update_deployment("sid-minibert-onnx", "models:/sid-minibert-onnx/2", flavor="triton")
+client.update_deployment("sid-minibert-onnx", "models:/sid-minibert-onnx/1", flavor="triton")
 ```
 
 ### List Deployments

models/mlflow/docker/Dockerfile

@@ -44,7 +44,7 @@ RUN sed -i 's/conda activate base/conda activate mlflow/g' ~/.bashrc
 SHELL ["/opt/conda/bin/conda", "run", "-n", "mlflow", "/bin/bash", "-c"]
 
 ARG TRITON_DIR=/mlflow/triton-inference-server
-ARG TRITON_VER=r24.01
+ARG TRITON_VER=r24.03
 
 RUN mkdir ${TRITON_DIR} && \
     cd ${TRITON_DIR} && \