grdryn / ai-edge

ODH integration with AI at the Edge usecases

ODH AI Edge Use Cases

Artifacts in support of ODH Edge use cases that integration with Red Hat Advanced Cluster Management(Open Cluster Management)

Components	Version
OpenShift	4.13
Open Data Hub	2.x
Red Hat Advanced Cluster Management	2.8
OpenShift Pipelines	1.11.x
Quay Registry	2.8

Proof of Concept Edge use case with ACM

The main objective is to showcase that a user can take a trained model, use a pipeline to package it with all the dependencies and deploy it at the near edge location(s) in a centralized way.

Infrastructure Configuration

1. Provision OpenShift Cluster
2. Configure the default Identity Provider
3. Install Red Hat Advanced Cluster Management
4. Register the clusters ACM Application manifests are located in acm/registration to register and configure the target environments required for the AI at the Edge use cases. The files can be applied to the ACM hub cluster manually:

   $  oc apply -k acm/registration
   

   • Core - Cluster host the ODH Core components that will be used in the MLOps Engineer workflow to train, build and push the model. This cluster is not required to be co-located with the ACM Hub but we group them together to simplify the use case
   • Near Edge - Cluster(s) that will host the running model at the edge. This is the target environment after a new model is available for use
5. Deploy Open Data Hub to the Core cluster and register any configurations to support pushing models to the edge cluster
   • GitOps repos
   • Image repos
6. Manage the edge cluster environments to support deployment of models and support for monitoring
   • Configure ACM Observability
   • Deploy the Model container

MLOps Engineer workflows

1. Develop the model in an ODH Jupyter notebook
2. Build the model from the notebook using Data Science Pipelines
3. Push the model to the image registry accessible by the near edge cluster(s)
4. Update the GitOps config for the near edge cluster

Pipelines setup

See pipelines/README.md

Observability setup

• Core cluster
  • Login to the core cluster and run make install/observability-core to setup acm-observability on the core cluster.
• Edge cluster(s)
  • Login to edge cluster
  • Enable userWorkloadMonitoring
    • oc edit cm cluster-monitoring-config
    • Set variable enableUserWorkload to true
  • Run make install/observability-edge to create the ConfigMap required for metric whitelisting.