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The model operator launches machine learning inference services, either on the cloud or on the edge nodes, depending on the corresponding CRD specs.
It is supposed to run as a separate binary, fully decoupled from the KubeEdge platform code. It leverages the KubeEdge platform to schedule work on the edge nodes.
As of now (2021/03), the model operator only supports model inference on the edge nodes.
Ideally it should be deployed as a Kubernetes Deployment, running on the cloud, and inside a separate namespace within the cluster.
For now, the following steps just run it as a binary, wherever the kubeconfig is available.
- Provision a KubeEdge cluster with at least one edge node
- An HTTP server from where Tensorflow models can be downloaded. (S3 will be supported soon)
Note:
- The following steps can be run on any machine (bare metal or VM) with kubeconfig
- You need to modify the inferencemodel.yaml according to where your model files are located
- Clone repo
$ git clone https://github.com/futurewei-cloud/modeloperator.git- Create the model CRD and configmap
cd modeloperator
kubectl apply -f config/crd/bases/ai.kubeedge.io_inferencemodels.yaml
kubectl create configmap ai-downloadmodelfile --from-file=scripts/downloadModelFile.sh- Build and run the model operator
make
bin/manager- Open another terminal and create an inferencemodel instance
kubectl apply -f config/samples/inferencemodel.yaml