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* feat: Support customizable deployment strategy for RawDeployment mode Signed-off-by: Yuan Tang <[email protected]> * regen Signed-off-by: Yuan Tang <[email protected]> * lint Signed-off-by: Yuan Tang <[email protected]> * Correctly apply rollingupdate Signed-off-by: Yuan Tang <[email protected]> * address comments Signed-off-by: Yuan Tang <[email protected]> * Add validation Signed-off-by: Yuan Tang <[email protected]> --------- Signed-off-by: Yuan Tang <[email protected]>
Deploy InferenceService with ONNX model
Setup
- Your ~/.kube/config should point to a cluster with KServe installed.
- Your cluster's Istio Ingress gateway must be network accessible.
Create the InferenceService
Apply the CRD
kubectl apply -f onnx.yaml
Expected Output
$ inferenceservice.serving.kserve.io/style-sample configured
Run a sample inference
- Setup env vars
The first step is to determine the ingress IP and ports and set
INGRESS_HOSTandINGRESS_PORT
export MODEL_NAME=onnx-model
export ISVC_NAME=style-sample
export SERVICE_HOSTNAME=$(kubectl get inferenceservice ${ISVC_NAME} -o jsonpath='{.status.url}' | cut -d "/" -f 3)
- Verify the service is healthy
curl -v -H "Host:${SERVICE_HOSTNAME}" http://localhost:8080/v2/models/${MODEL_NAME}
- Install dependencies
pip install -r requirements.txt
- Run the sample notebook in jupyter
jupyter notebook
Uploading your own model
The sample model for the example in this readme is already uploaded and available for use. However if you would like to modify the example to use your own ONNX model, all you need to do is to upload your model as model.onnx to S3, GCS or an Azure Blob.