ModelMesh Runtime Adapter

This repo contains the unified puller/runtime-adapter image of the sidecar containers which run in the ModelMesh Serving model server Pods. Take a look at the main ModelMesh Serving repo for more details.

Logical subcomponents within the image:

Generate sources

The gRPC code stubs, interfaces and data access classes have to be generated by the protoc compiler from the .proto source files under internal/proto/*.

If any of the .proto files were modified, run the protoc compiler to regenerate the respective Go source code. It's recommended to use the developer image, which has all the required libraries pre-installed, by running make run proto.compile instead of make proto.compile.

make run proto.compile

Test the code changes

After making code changes, ensure all existing and new functionality still works properly by running the unit tests.

make test

Format code

Run the linter to make sure all code style rules are adhered to. The code will automatically be formatted if any code style violations are found.

It's recommended to use the developer image, which has all the required libraries pre-installed, by running make run fmt instead of make fmt.

make run fmt

Build the Docker image

Once the code changes have been tested and linted, build a new modelmesh-runtime-adapter Container image. If you need to use another builder than docker, you can specify it by using the ENGINE variable:

ENGINE=podman make build

Push the image to a container registry

Push the newly built modelmesh-runtime-adapter image to a container registry. Replace the value of the DOCKER_USER environment variable to your docker user ID and change the IMAGE_TAG to something meaningful.

export DOCKER_USER="<your-docker-userid>"
export IMAGE_TAG="dev"

docker tag kserve/modelmesh-runtime-adapter:latest \
    ${DOCKER_USER}/modelmesh-runtime-adapter:${IMAGE_TAG}

docker push ${DOCKER_USER}/modelmesh-runtime-adapter:${IMAGE_TAG}

Update the ModelMesh Serving deployment

In order to test the code changes in an existing ModelMesh Serving deployment, the newly built container image needs to be added to the model-serving-config ConfigMap.

Check existing model serving configuration

First, check if your ModelMesh Serving deployment already has an existing model-serving-config ConfigMap:

kubectl get configmap

NAME                            DATA   AGE
kube-root-ca.crt                1      12d
model-serving-config            1      12d
model-serving-config-defaults   1      12d
tc-config                       2      12d

Create a new model serving config

If you did not already have a model-serving-config ConfigMap on your cluster, you can create one. Replace the <your-docker-userid> placeholder with your Docker username. Make sure the value of the IMAGE_TAG variable matches the one that was pushed to the container registry.

export DOCKER_USER="<your-docker-userid>"
export IMAGE_NAME="${DOCKER_USER}/modelmesh-runtime-adapter"
export IMAGE_TAG="dev"

kubectl apply -f - <<EOF
---
apiVersion: v1
kind: ConfigMap
metadata:
  name: model-serving-config
data:
  config.yaml: |
    storageHelperImage:
      name: ${IMAGE_NAME}
      tag: ${IMAGE_TAG}
EOF

Update an existing model serving config

If the ConfigMap list contains model-serving-config, save the contents of your existing configuration in a local temp file:

mkdir -p temp
kubectl get configmap model-serving-config -o yaml > temp/model-serving-config.yaml

Add the storageHelperImage property to the config.yaml string property.

      storageHelperImage:
        name: your-docker-userid/modelmesh-runtime-adapter
        tag: latest

Replace the your-docker-userid placeholder with your Docker username and make sure the tag matches the one that was pushed to the container registry earlier.

The complete ConfigMap YAML file may look like this:

apiVersion: v1
kind: ConfigMap
metadata:
  name: model-serving-config
  namespace: modelmesh-serving
data:
  config.yaml: |
    podsPerRuntime: 1
    restProxy:
      enabled: true
    scaleToZero:
      enabled: false
      gracePeriodSeconds: 5
    storageHelperImage:
      name: your-docker-userid/modelmesh-runtime-adapter
      tag: dev

Apply the ConfigMap to your cluster:

kubectl apply -f temp/model-serving-config.yaml

If you are comfortable using vi, you can forgo creating a temp file and edit the ConfigMap directly in the terminal:

kubectl edit configmap model-serving-config

Verify the container images used by the model serving runtime

The modelmesh-controller watches the ConfigMap and responds to updates by automatically restarting the serving runtime pods using the newly built runtime adapter image.

You can check which container images are used by running the following command:

kubectl get pods -o jsonpath='{range .items[*]}{"\n"}{.metadata.name}{"\t"}{range .spec.containers[*]}{.image}{", "}{end}{end}' | sort | column -ts $'\t' | sed 's/, *$//g'

etcd-78ff7867d5-45svw                            quay.io/coreos/etcd:v3.5.4
minio-6ddbfc9665-gtf7x                           kserve/modelmesh-minio-examples:latest
modelmesh-controller-64f5c8d6d6-k6rzc            kserve/modelmesh-controller:latest
modelmesh-serving-mlserver-1.x-84884c6849-s8dw6  kserve/rest-proxy:latest, seldonio/mlserver:1.3.2, your-docker-userid/modelmesh-runtime-adapter:dev, kserve/modelmesh:latest
modelmesh-serving-mlserver-1.x-84884c6849-xpdw4  kserve/rest-proxy:latest, seldonio/mlserver:1.3.2, your-docker-userid/modelmesh-runtime-adapter:dev, kserve/modelmesh:latest

Konflux build system Files

This directory holds the requirements.txt file use in the Konflux builds to pre-fetch the required packages.

Here are two files that we need to keep:

requirements.txt: is the result of the pip freeze command (see example below).

How to update the requirements.txt file

Once the final container is built, you can access it:

podman run <modelmesh-runtime-adapter-container-name> sh -c 'pip freeze'

Note, for tensorflow-io-gcs-filesystem it must be pinned to 0.34.0 manually after generating the requirements.txt file.`

Then commit the changes and push.

Konflux configuration

Build configuration, it requires the build be configured as follows:

- name: prefetch-input
  value: |
    [{"type": "gomod"}, {"type": "rpm"}, {"type": "pip", "path": ".", "requirements_files": ["requirements.txt"], "allow_binary": "true"}]

opendatahub-io/modelmesh-runtime-adapter