Sync kserve manifests for v0.8.0

.github/workflows/kserve_kind_test.yaml

@@ -27,9 +27,4 @@ jobs:
       run: ./tests/gh-actions/install_cert_manager.sh
 
     - name: Build & Apply manifests
-      run: |
-        cd contrib/kserve
-        kubectl create ns kubeflow
-        kustomize build kserve | kubectl apply -f -
-        kustomize build models-web-app/overlays/kubeflow | kubectl apply -f -
-        kubectl wait --for=condition=Ready pods --all --all-namespaces --timeout 180s
+      run: ./tests/gh-actions/install_kserve.sh

README.md

@@ -51,7 +51,8 @@ This repo periodically syncs all official Kubeflow components from their respect
 | Tensorboards Web App | apps/tensorboard/tensorboards-web-app/upstream | [v1.5.0](https://github.com/kubeflow/kubeflow/tree/v1.5.0/components/crud-web-apps/tensorboards/manifests) |
 | Volumes Web App | apps/volumes-web-app/upstream | [v1.5.0](https://github.com/kubeflow/kubeflow/tree/v1.5.0/components/crud-web-apps/volumes/manifests) |
 | Katib | apps/katib/upstream | [v0.14.0-rc.0](https://github.com/kubeflow/katib/tree/v0.14.0-rc.0/manifests/v1beta1) |
-| KServe | contrib/kserve/upstream | [v0.7.0](https://github.com/kserve/kserve/tree/v0.7.0) |
+| KServe | contrib/kserve/kserve | [release-0.8](https://github.com/kserve/kserve/tree/8079f375cbcedc4d45a1b4aade2e2308ea6f9ae8/install/v0.8.0) |
+| KServe Models Web App | contrib/kserve/models-web-app | [v0.8.0](https://github.com/kserve/models-web-app/tree/v0.8.0/config) |
 | Kubeflow Pipelines | apps/pipeline/upstream | [1.8.2](https://github.com/kubeflow/pipelines/tree/1.8.2/manifests/kustomize) |
 | Kubeflow Tekton Pipelines | apps/kfp-tekton/upstream | [v1.2.1](https://github.com/kubeflow/kfp-tekton/tree/v1.2.1/manifests/kustomize) |
 

contrib/kserve/kserve/kserve-runtimes.yaml

@@ -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"