1.7. Creating a ServingRuntime CR for use in MicroShift


Create a ServingRuntime custom resource (CR) based on installed manifests and release information. The included steps are an example of reusing the included microshift-ai-model-serving manifest files to re-create the OpenVINO Model Server (OVMS) model-serving runtime in the workload namespace.

注意

This approach does not require a live node, so it can be part of CI/CD automation.

Prerequisites

  • Both the microshift-ai-model-serving and microshift-ai-model-serving-release-info RPMs are installed.
  • You have root user access to your machine.
  • The OpenShift CLI (oc) is installed.

Procedure

  1. Extract the image reference of the ServingRuntime CR you want to use from the MicroShift release information file by running the following command:

    $ OVMS_IMAGE="$(jq -r '.images | with_entries(select(.key == "ovms-image")) | .[]' /usr/share/microshift/release/release-ai-model-serving-"$(uname -i)".json)" 
    1
    1
    In this example, the image reference for the OVMS model-serving runtime is extracted.
  2. Copy the original ServingRuntime YAML file by running the following command:

    $ cp /usr/lib/microshift/manifests.d/050-microshift-ai-model-serving-runtimes/ovms-kserve.yaml ./ovms-kserve.yaml
  3. Add the actual image reference to the image: parameter field value of the ServingRuntime YAML by running the following command:

    $ sed -i "s,image: ovms-image,image: ${OVMS_IMAGE}," ./ovms-kserve.yaml
  4. Create the ServingRuntime object in a custom namespace using the YAML file by running the following command:

    $ oc create -n <ai_demo> -f ./ovms-kserve.yaml 
    1
    1
    Replace <ai_demo> with the name of your namespace.
重要

If the ServingRuntime CR is part of a new manifest, set the namespace in the kustomization.yaml file, for example:

Example Kustomize manifest namespace value

apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: ai-demo
resources:
  - ovms-kserve.yaml
#...

Next steps

  • Create the InferenceService object.
  • Verify that your model is ready for inferencing.
  • Query the model.
  • Optional: examine the model metrics.
Red Hat logoGithubredditYoutubeTwitter

学习

尝试、购买和销售

社区

關於紅帽

我们提供强化的解决方案,使企业能够更轻松地跨平台和环境(从核心数据中心到网络边缘)工作。

让开源更具包容性

红帽致力于替换我们的代码、文档和 Web 属性中存在问题的语言。欲了解更多详情,请参阅红帽博客.

关于红帽文档

Legal Notice

Theme

© 2026 Red Hat
返回顶部