Serving models with OCI images¶

KServe's traditional approach for model initialization involves fetching models from sources like S3 buckets or URIs. This process is adequate for small models but becomes a bottleneck for larger ones like used for large language models, significantly delaying startup times in auto-scaling scenarios.

"Modelcars" is a KServe feature designed to address these challenges. It streamlines model fetching using OCI images, offering several advantages:

• Reduced Startup Times: By avoiding repetitive downloads of large models, startup delays are significantly minimized.
• Lower Disk Space Usage: The feature decreases the need for duplicated local storage, conserving disk space.
• Enhanced Performance: Modelcars allows for advanced techniques like pre-fetching images and lazy-loading, improving efficiency.
• Compatibility and Integration: It seamlessly integrates with existing KServe infrastructure, ensuring ease of adoption.

Modelcars represents a step forward in efficient model serving, particularly beneficial for handling large models and dynamic serving environments.

Enabling Modelcars¶

Modelcars is an experimental feature in KServe and is not enabled by default. To take advantage of this new model serving method, it needs to be activated in the KServe configuration. Follow the steps below to enable Modelcars in your environment.

Modelcars can be enabled by modifying the storageInitializer configuration in the inferenceservice-config ConfigMap. This can be done manually using kubectl edit or by executing the script provided below, with the current namespace set to the namespace where the kserve-controller-manager is installed (depends on the way how KServer is installed.)

# Script to enable Modelcars
# Fetch the current storageInitializer configuration
config=$(kubectl get configmap inferenceservice-config -n kserve -o jsonpath='{.data.storageInitializer}')
# Enable modelcars and set the UID for the containers to run (required for minikube)
newValue=$(echo $config | jq -c '. + {"enableModelcar": true, "uidModelcar": 1010}')

# Create a temporary directory for the patch file
tmpdir=$(mktemp -d)
cat <<EOT > $tmpdir/patch.txt
[{
  "op": "replace",
  "path": "/data/storageInitializer",
  "value": '$newValue'
}]
EOT

# Apply the patch to the ConfigMap
kubectl patch configmap -n kserve inferenceservice-config --type=json --patch-file=$tmpdir/patch.txt

# Restart the KServe controller to apply changes
kubectl delete pod -n kserve -l control-plane=kserve-controller-manager

Prepare an OCI Image with Model Data¶

To utilize Modelcars for serving models, you need to prepare an OCI (Open Container Initiative) image containing your model data. This process involves creating a Dockerfile and building an OCI image that houses your model in a specific directory. Below are the steps and an example to guide you through this process.

1. Create a Dockerfile: Start by creating a Dockerfile that uses a base image containing the necessary commands like ln (for creating symbolic links) and sleep (for keeping the container running). The Dockerfile should also include steps to create a directory /model for your model data and copy the data into this directory. Here's an example Dockerfile where the data/ directory contains your model data. This data will later be mounted in /mnt/models by the runtime:

   FROM busybox
   RUN mkdir /models && chmod 775 /models
   COPY data/ /models/
   

2. Build and Push the Image to a Registry: Once your Dockerfile is ready, use either docker or podman to build and push the image to a container registry like Docker Hub or quay.io

   docker build -t myuser/mymodel:1.0 .
   docker push myuser/mymodel:1.0
   

By completing these steps, you'll have an OCI image ready with your model data, which can then be used with the Modelcars feature in KServe for efficient model serving.

Using Modelcars¶

With Modelcars enabled and your OCI image containing the model data prepared, integrating this setup into your InferenceService is straightforward. The key step involves specifying the storageURI with the oci:// schema in your InferenceService configuration to point to your OCI image.

Here’s an example of how an InferenceService configuration would look when using the Modelcars feature:

apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
  name: my-infeference-service
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: oci://myuser/mymodel:1.0

In order to fully leverage the local caching capabilities of OCI images in the Modelcars setup, it is crucial to use a specific tag for your model image, rather than relying on the default latest tag. For instance, in the provided example, the tag 1.0 is utilized. This approach ensures that the modelcar image is pulled with a IfNotPresent policy, allowing for efficient use of local cache. On the other hand, using the latest tag, or omitting a tag altogether, defaults to a Always pull policy. This means the image would be re-downloaded every time a Pod restarts or scales up, negating the benefits of local caching and potentially leading to increased startup times.

Example¶

Let's see how modecars work by deploying the getting started example by using an OCI image and check how it is different to the startup with a storage-initalizer init-container.

Asuming you have setup a namespace kserve-test that is KServe enabled, create an InferenceService that uses an oci:// storage URL:

kubectl apply -n kserve-test -f - <<EOF
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "sklearn-iris-oci"
spec:
  predictor:
    model:
      modelFormat:
        name: sklearn
      storageUri: "oci://rhuss/kserving-example-sklearn:1.0"
EOF

After the InferenceService has been deployed successfully, you can follow the steps of the getting started example to verify the installation.

Finally, let's have a brief look under the covers for how this feature works. Let's first check the runtime pod:

kubectl get pods

NAME                                                     READY   STATUS    RESTARTS      AGE
sklearn-iris-oci-predictor-00001-deployment-58fc6564d7   3/3     Running   1 (39m ago)   40m

As you can see, the Pod has now one additional container. This container is running the modelcar image and runs a ln -s /proc/$$/root/models /mnt/ command to create a symbolic link on a shared empty volume that is mounted on /mnt in the modelcar container and the serving runtime container. The magic here is the symbolic link over proc filesystem, which is shared among all containers. This is possible on Kubernetes for the container's of a Pod if the field .spec.shareProcessNamespace is set to true, which is the case for all storageUri that leverages the oci:// schema.

Let's jump into the runtime container and examine the mounted /mnt filesystem:

# InferenceService Pod
pod=$(kubectl get pods -l serving.kserve.io/inferenceservice=sklearn-iris-oci -o name)
# Verify that shareProcessNamespace is enabled
kubectl get $pod -o jsonpath="{.spec.shareProcessNamespace}"

# Jump into pod and check the model location
kubectl exec -it $pod -c kserve-container -- bash

sklearn-iris-oci-predictor:/$ cd /mnt
sklearn-iris-oci-predictor:/mnt$ ls -l
total 0
lrwxrwxrwx 1 1010 root 20 Jan 27 10:35 models -> /proc/38/root/models

sklearn-iris-oci-predictor:/mnt$ cd /mnt/models 
sklearn-iris-oci-predictor:/mnt/models$ ls -l
total 8
-rw-r--r-- 1 root root 5408 Jan 26 15:58 model.joblib

As you can see, the runtime can directly access the data coming from the modelcar image, without prior copying it over in another volume.

Configuration¶

Fine-tuning the behavior of Modelcars in KServe is possible through global configuration settings for inference services. These settings are located in the inferenceservice-config ConfigMap, which resides in the kserve namespace or the namespace where the KServe controller operates. This ConfigMap includes various subconfigurations, with the Modelcars configuration located under the storageInitializer entry.

To view the current configuration, use the following command:

kubectl get cm -n kserve inferenceservice-config --jsonpath "{.data.storageInitializer}"

{ 
   "image": "kserve/storage-initializer:v0.11.2",
   "memoryRequest": "100Mi",
   "memoryLimit": "1Gi",
   "cpuRequest": "100m",
   "cpuLimit": "1",
   "enableDirectPvcVolumeMount": false,
   "enableModelcar": true,
   "uidModelcar": 1010
}

The output is a JSON string representing the configuration. For Modelcars, several keys are available for customization:

References¶

• Modelcar Design document
• Original GitHub issue (discusses also some alternative solutions)
• 12-minute demo
• Code walkthrough showing the implementation of Modelcars in KServe (for background information)