KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.
It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. KServe is being used across various organizations.
To learn more about KServe, how to deploy it as part of Kubeflow, how to use various supported features, and how to participate in the KServe community, please follow the KServe website documentation. Additionally, we have compiled a list of presentations and demoes to dive through various details.
Kubernetes 1.17 is the minimally recommended version, Knative Serving and Istio should be available on Kubernetes Cluster.
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Istio: v1.9.0+
- KServe currently only depends on
Istio Ingress Gatewayto route requests to inference services externally or internally. If you do not needService Mesh, we recommend turning off Istio sidecar injection.
- KServe currently only depends on
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Knative Serving: v0.19.0+
- If you are running
Service Meshmode withAuthorizationplease follow knative doc to setup the authorization policies. - If you are looking to use PodSpec fields such as
nodeSelector,affinityortolerationswhich are now supported in the v1beta1 API spec, you need to turn on the corresponding feature flags in your Knative configuration.
- If you are running
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Cert Manager: v1.3.0+
- Cert manager is needed to provision webhook certs for production grade installation, alternatively you can run our self signed certs generation script.
KServe can be installed standalone if your kubernetes cluster meets the above prerequisites and is deployed in kserve namespace.
TAG=v0.7.0-rc0
Install KServe CRD and Controller
Due to a performance issue applying deeply nested CRDs, please ensure that your kubectl version
fits into one of the following categories to ensure that you have the fix: >=1.16.14,<1.17.0 or >=1.17.11,<1.18.0 or >=1.18.8.
kubectl apply -f https://github.com/kserve/kserve/releases/download/$TAG/kserve.yamlMake sure you have kubectl installed.
- If you do not have an existing kubernetes cluster, you can create a quick kubernetes local cluster with kind.
Note that the minimal requirement for running KServe is 4 cpus and 8Gi memory, so you need to change the docker resource setting to use 4 cpus and 8Gi memory.
kind create clusteralternatively you can use Minikube
minikube start --cpus 4 --memory 8192- Install Istio lean version, Knative Serving, KServe all in one.(this takes 30s)
./hack/quick_install.shIf the default ingress gateway setup does not fit your need, you can choose to setup a custom ingress gateway
- Configure Custom Ingress Gateway
- In addition you need to update configmap to use the custom ingress gateway.
- Configure Custom Domain
- Configure HTTPS Connection
Execute the following command to determine if your kubernetes cluster is running in an environment that supports external load balancers
$ kubectl get svc istio-ingressgateway -n istio-system
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
istio-ingressgateway LoadBalancer 172.21.109.129 130.211.10.121 ... 17hIf the EXTERNAL-IP value is set, your environment has an external load balancer that you can use for the ingress gateway.
export INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}')If the EXTERNAL-IP value is none (or perpetually pending), your environment does not provide an external load balancer for the ingress gateway. In this case, you can access the gateway using the service’s node port.
# GKE
export INGRESS_HOST=worker-node-address
# Minikube
export INGRESS_HOST=
8000
$(minikube ip)
# Other environment(On Prem)
export INGRESS_HOST=$(kubectl get po -l istio=ingressgateway -n istio-system -o jsonpath='{.items[0].status.hostIP}')
export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].nodePort}')Alternatively you can do Port Forward for testing purpose
INGRESS_GATEWAY_SERVICE=$(kubectl get svc --namespace istio-system --selector="app=istio-ingressgateway" --output jsonpath='{.items[0].metadata.name}')
kubectl port-forward --namespace istio-system svc/${INGRESS_GATEWAY_SERVICE} 8080:80
# start another terminal
export INGRESS_HOST=localhost
export INGRESS_PORT=8080kubectl get po -n kserve
NAME READY STATUS RESTARTS AGE
kserve-controller-manager-0 2/2 Running 2 13mPlease follow getting started to create your first InferenceService.
# use kubectl create instead of apply because the job template is using generateName which doesn't work with kubectl apply
kubectl create -f docs/samples/${API_VERSION}/sklearn/v1/perf.yaml -n kserve-test
# wait the job to be done and check the log
kubectl logs load-test8b58n-rgfxr -n kserve-test
Requests [total, rate, throughput] 30000, 500.02, 499.99
Duration [total, attack, wait] 1m0s, 59.998s, 3.336ms
Latencies [min, mean, 50, 90, 95, 99, max] 1.743ms, 2.748ms, 2.494ms, 3.363ms, 4.091ms, 7.749ms, 46.354ms
Bytes In [total, mean] 690000, 23.00
Bytes Out [total, mean] 2460000, 82.00
Success [ratio] 100.00%
Status Codes [code:count] 200:30000
Error Set:-
Install the SDK
pip install kserve -
Check the SDK documents from here.
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Follow the example(s) here to use the KServe SDK to create, rollout, promote, and delete an InferenceService instance.
InferenceService v1beta1 API Docs
benchmark test comparing Knative and Kubernetes Deployment with HPA