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runtimes installation issue fix (kubeflow#2071)
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Signed-off-by: Suresh Nakkeran <suresh.n@ideas2it.com>
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Suresh-Nakkeran committed Mar 3, 2022
1 parent 922a9b7 commit e0bd9ed
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Showing 4 changed files with 262 additions and 262 deletions.
6 changes: 2 additions & 4 deletions hack/generate-install.sh
Original file line number Diff line number Diff line change
Expand Up @@ -50,14 +50,12 @@ fi
INSTALL_DIR=./install/$TAG
INSTALL_PATH=$INSTALL_DIR/kserve.yaml
KUBEFLOW_INSTALL_PATH=$INSTALL_DIR/kserve_kubeflow.yaml
RUNTIMES_INSTALL_PATH=$INSTALL_DIR/kserve-runtimes.yaml

mkdir -p $INSTALL_DIR
kustomize build config/default | sed s/:latest/:$TAG/ > $INSTALL_PATH
echo "---" >> $INSTALL_PATH
kustomize build config/runtimes | sed s/:latest/:$TAG/ >> $INSTALL_PATH
kustomize build config/overlays/kubeflow | sed s/:latest/:$TAG/ > $KUBEFLOW_INSTALL_PATH
echo "---" >> $KUBEFLOW_INSTALL_PATH
kustomize build config/runtimes | sed s/:latest/:$TAG/ >> $KUBEFLOW_INSTALL_PATH
kustomize build config/runtimes | sed s/:latest/:$TAG/ >> $RUNTIMES_INSTALL_PATH

# Copy CRD files to charts crds directory
cp config/crd/serving.kserve.io_clusterservingruntimes.yaml manifests/charts/crds/serving.kserve.io_clusterservingruntimes.yaml
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5 changes: 4 additions & 1 deletion hack/quick_install.sh
Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ done

export ISTIO_VERSION=1.9.0
export KNATIVE_VERSION=knative-v1.0.0
export KSERVE_VERSION=v0.8.0-rc0
export KSERVE_VERSION=v0.8.0
export CERT_MANAGER_VERSION=v1.3.0

KUBE_VERSION=$(kubectl version --short=true)
Expand Down Expand Up @@ -104,5 +104,8 @@ if [ ${KSERVE_VERSION:3:1} -gt 6 ]; then KSERVE_CONFIG=kserve.yaml; fi

# Retry inorder to handle that it may take a minute or so for the TLS assets required for the webhook to function to be provisioned
for i in 1 2 3 4 5 ; do kubectl apply -f https://github.com/kserve/kserve/releases/download/${KSERVE_VERSION}/${KSERVE_CONFIG} && break || sleep 15; done
# Install KServe built-in servingruntimes
kubectl wait --for=condition=ready pod -l control-plane=kserve-controller-manager -n kserve --timeout=300s
kubectl apply -f https://github.com/kserve/kserve/releases/download/${KSERVE_VERSION}/kserve-runtimes.yaml
# Clean up
rm -rf istio-${ISTIO_VERSION}
256 changes: 256 additions & 0 deletions install/v0.8.0/kserve-runtimes.yaml
Original file line number Diff line number Diff line change
@@ -0,0 +1,256 @@
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-lgbserver
spec:
containers:
- args:
- --model_name={{.Name}}
- --model_dir=/mnt/models
- --http_port=8080
- --nthread=1
image: kserve/lgbserver:v0.8.0
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- autoSelect: true
name: lightgbm
version: "2"
---
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-mlserver
spec:
containers:
- env:
- name: MLSERVER_MODEL_IMPLEMENTATION
value: '{{.Labels.modelClass}}'
- name: MLSERVER_HTTP_PORT
value: "8080"
- name: MLSERVER_GRPC_PORT
value: "9000"
- name: MODELS_DIR
value: /mnt/models
image: docker.io/seldonio/mlserver:0.5.3
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- name: sklearn
version: "0"
- name: xgboost
version: "1"
- name: lightgbm
version: "3"
- autoSelect: true
name: mlflow
version: "1"
---
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-paddleserver
spec:
containers:
- args:
- --model_name={{.Name}}
- --model_dir=/mnt/models
- --http_port=8080
image: kserve/paddleserver:v0.8.0
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- autoSelect: true
name: paddle
version: "2"
---
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-pmmlserver
spec:
containers:
- args:
- --model_name={{.Name}}
- --model_dir=/mnt/models
- --http_port=8080
image: kserve/pmmlserver:v0.8.0
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- autoSelect: true
name: pmml
version: "3"
- autoSelect: true
name: pmml
version: "4"
---
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-sklearnserver
spec:
containers:
- args:
- --model_name={{.Name}}
- --model_dir=/mnt/models
- --http_port=8080
image: kserve/sklearnserver:v0.8.0
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- autoSelect: true
name: sklearn
version: "1"
---
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-tensorflow-serving
spec:
containers:
- args:
- --model_name={{.Name}}
- --port=9000
- --rest_api_port=8080
- --model_base_path=/mnt/models
- --rest_api_timeout_in_ms=60000
command:
- /usr/bin/tensorflow_model_server
image: tensorflow/serving:2.6.2
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- autoSelect: true
name: tensorflow
version: "1"
- autoSelect: true
name: tensorflow
version: "2"
---
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-torchserve
spec:
containers:
- args:
- torchserve
- --start
- --model-store=/mnt/models/model-store
- --ts-config=/mnt/models/config/config.properties
env:
- name: TS_SERVICE_ENVELOPE
value: '{{.Labels.serviceEnvelope}}'
image: kserve/torchserve-kfs:0.5.3
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- autoSelect: true
name: pytorch
version: "1"
---
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-tritonserver
spec:
containers:
- args:
- tritonserver
- --model-store=/mnt/models
- --grpc-port=9000
- --http-port=8080
- --allow-grpc=true
- --allow-http=true
image: nvcr.io/nvidia/tritonserver:21.09-py3
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- name: tensorrt
version: "8"
- name: tensorflow
version: "1"
- name: tensorflow
version: "2"
- autoSelect: true
name: onnx
version: "1"
- name: pytorch
version: "1"
- autoSelect: true
name: triton
version: "2"
---
apiVersion: serving.kserve.io/v1alpha1
kind: ClusterServingRuntime
metadata:
name: kserve-xgbserver
spec:
containers:
- args:
- --model_name={{.Name}}
- --model_dir=/mnt/models
- --http_port=8080
- --nthread=1
image: kserve/xgbserver:v0.8.0
name: kserve-container
resources:
limits:
cpu: "1"
memory: 2Gi
requests:
cpu: "1"
memory: 2Gi
supportedModelFormats:
- autoSelect: true
name: xgboost
version: "1"

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