├── helm-charts └── aikitcontainer │ ├── README.md │ ├── templates │ ├── NOTES.txt │ ├── tests │ │ └── test-connection.yaml │ ├── _helpers.tpl │ └── imagestream.yaml │ ├── values.yaml │ ├── .helmignore │ ├── values.schema.json │ └── Chart.yaml ├── quickstart_guide ├── images │ ├── install.png │ ├── spawner.png │ ├── dashboard.png │ ├── namespace.PNG │ └── rhods_succeeded.PNG └── Intel_oneAPI_AI_Analytics_Toolkit_Operator_README.md ├── config ├── manager │ ├── kustomization.yaml │ └── manager.yaml ├── rbac │ ├── service_account.yaml │ ├── role_binding.yaml │ ├── leader_election_role_binding.yaml │ ├── kustomization.yaml │ ├── leader_election_role.yaml │ └── role.yaml ├── scorecard │ ├── bases │ │ └── config.yaml │ ├── patches │ │ ├── basic.config.patch │ │ └── olm.config.patch │ └── kustomization.yaml ├── samples │ ├── kustomization.yaml │ └── aikit_v1alpha1_aikitoperator.yaml ├── manifests │ ├── kustomization.yaml │ └── bases │ │ └── aikit-operator.clusterserviceversion.yaml ├── crd │ ├── kustomization.yaml │ └── bases │ │ └── aikit.intel_aikitcontainers.yaml ├── kustomization.yaml └── Krmfile ├── watches.yaml ├── .gitignore ├── PROJECT ├── bundle ├── kustomization.yaml ├── metadata │ └── annotations.yaml ├── tests │ └── scorecard │ │ └── config.yaml ├── Krmfile └── manifests │ └── aikit.intel_aikitcontainers.yaml ├── README.md ├── Dockerfile ├── bundle.Dockerfile ├── oneapi-aikit-2021-2-0-d1e720a.yaml ├── Makefile └── licenses └── LICENSE /helm-charts/aikitcontainer/README.md: -------------------------------------------------------------------------------- 1 | # ai-tools-operator 2 | -------------------------------------------------------------------------------- /quickstart_guide/images/install.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/intel/aikit-operator/main/quickstart_guide/images/install.png -------------------------------------------------------------------------------- /quickstart_guide/images/spawner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/intel/aikit-operator/main/quickstart_guide/images/spawner.png -------------------------------------------------------------------------------- /quickstart_guide/images/dashboard.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/intel/aikit-operator/main/quickstart_guide/images/dashboard.png -------------------------------------------------------------------------------- /quickstart_guide/images/namespace.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/intel/aikit-operator/main/quickstart_guide/images/namespace.PNG -------------------------------------------------------------------------------- /config/manager/kustomization.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: kustomize.config.k8s.io/v1beta1 2 | kind: Kustomization 3 | resources: 4 | - manager.yaml 5 | -------------------------------------------------------------------------------- /quickstart_guide/images/rhods_succeeded.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/intel/aikit-operator/main/quickstart_guide/images/rhods_succeeded.PNG -------------------------------------------------------------------------------- /config/rbac/service_account.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: v1 2 | kind: ServiceAccount 3 | metadata: 4 | name: controller-manager 5 | namespace: system 6 | -------------------------------------------------------------------------------- /config/scorecard/bases/config.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: scorecard.operatorframework.io/v1alpha3 2 | kind: Configuration 3 | metadata: 4 | name: config 5 | stages: 6 | - parallel: true 7 | tests: [] 8 | -------------------------------------------------------------------------------- /watches.yaml: -------------------------------------------------------------------------------- 1 | # Use the 'create api' subcommand to add watches to this file. 2 | - group: aikit.intel 3 | version: v1alpha1 4 | kind: AIKitContainer 5 | chart: helm-charts/aikitcontainer 6 | #+kubebuilder:scaffold:watch 7 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | # Binaries for programs and plugins 3 | *.exe 4 | *.exe~ 5 | *.dll 6 | *.so 7 | *.dylib 8 | bin 9 | 10 | # direnv 11 | .envrc 12 | 13 | # editor and IDE paraphernalia 14 | .idea 15 | *.swp 16 | *.swo 17 | *~ 18 | -------------------------------------------------------------------------------- /config/samples/kustomization.yaml: -------------------------------------------------------------------------------- 1 | ## Append samples you want in your CSV to this file as resources ## 2 | resources: 3 | - aikit_v1alpha1_aikitcontainer.yaml 4 | #+kubebuilder:scaffold:manifestskustomizesamples 5 | namespace: redhat-ods-applications # {"$openapi":"OC_PROJECT"} 6 | -------------------------------------------------------------------------------- /config/manifests/kustomization.yaml: -------------------------------------------------------------------------------- 1 | # These resources constitute the fully configured set of manifests 2 | # used to generate the 'manifests/' directory in a bundle. 3 | resources: 4 | - bases/aikit-operator.clusterserviceversion.yaml 5 | - ../default 6 | - ../samples 7 | - ../scorecard 8 | -------------------------------------------------------------------------------- /config/scorecard/patches/basic.config.patch: -------------------------------------------------------------------------------- 1 | - op: add 2 | path: /stages/0/tests/- 3 | value: 4 | entrypoint: 5 | - scorecard-test 6 | - basic-check-spec 7 | image: quay.io/operator-framework/scorecard-test:v1.8.0 8 | labels: 9 | suite: basic 10 | test: basic-check-spec-test 11 | -------------------------------------------------------------------------------- /config/crd/kustomization.yaml: -------------------------------------------------------------------------------- 1 | # This kustomization.yaml is not intended to be run by itself, 2 | # since it depends on service name and namespace that are out of this kustomize package. 3 | # It should be run by config/default 4 | resources: 5 | - bases/aikit.intel_aikitcontainers.yaml 6 | #+kubebuilder:scaffold:crdkustomizeresource 7 | -------------------------------------------------------------------------------- /helm-charts/aikitcontainer/templates/NOTES.txt: -------------------------------------------------------------------------------- 1 | 1. Get the imagestream by running these commands: 2 | kubectl --namespace {{ $.Release.Namespace }} get imagestream intel-tensorflow 3 | kubectl --namespace {{ $.Release.Namespace }} get imagestream intel-pytorch 4 | kubectl --namespace {{ $.Release.Namespace }} get imagestream intel-ml 5 | -------------------------------------------------------------------------------- /config/rbac/role_binding.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: rbac.authorization.k8s.io/v1 2 | kind: ClusterRoleBinding 3 | metadata: 4 | name: manager-rolebinding 5 | roleRef: 6 | apiGroup: rbac.authorization.k8s.io 7 | kind: ClusterRole 8 | name: manager-role 9 | subjects: 10 | - kind: ServiceAccount 11 | name: controller-manager 12 | namespace: system 13 | -------------------------------------------------------------------------------- /config/rbac/leader_election_role_binding.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: rbac.authorization.k8s.io/v1 2 | kind: RoleBinding 3 | metadata: 4 | name: leader-election-rolebinding 5 | roleRef: 6 | apiGroup: rbac.authorization.k8s.io 7 | kind: Role 8 | name: leader-election-role 9 | subjects: 10 | - kind: ServiceAccount 11 | name: controller-manager 12 | namespace: system 13 | -------------------------------------------------------------------------------- /PROJECT: -------------------------------------------------------------------------------- 1 | domain: intel 2 | layout: 3 | - helm.sdk.operatorframework.io/v1 4 | plugins: 5 | manifests.sdk.operatorframework.io/v2: {} 6 | scorecard.sdk.operatorframework.io/v2: {} 7 | projectName: aikit-operator 8 | resources: 9 | - api: 10 | crdVersion: v1 11 | namespaced: true 12 | domain: intel 13 | group: aikit 14 | kind: AIKitContainer 15 | version: v1alpha1 16 | version: "3" 17 | -------------------------------------------------------------------------------- /helm-charts/aikitcontainer/templates/tests/test-connection.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | apiVersion: v1 3 | kind: Pod 4 | metadata: 5 | name: "tensorflow-test" 6 | annotations: 7 | "helm.sh/hook": test 8 | spec: 9 | containers: 10 | - name: echo 11 | image: rhel8/python-39:latest 12 | command: 13 | - /bin/bash 14 | args: 15 | - -c 16 | - "echo hi" 17 | restartPolicy: Never 18 | -------------------------------------------------------------------------------- /helm-charts/aikitcontainer/values.yaml: -------------------------------------------------------------------------------- 1 | # Default values for aitoolscontainer. 2 | # This is a YAML-formatted file. 3 | # Declare variables to be passed into your templates. 4 | 5 | nameOverride: "" 6 | fullnameOverride: "" 7 | registry: 8 | root: quay.io 9 | repo: opendatahub/workbench-images 10 | opendatahub: 11 | commit: "20240430" 12 | namespace: redhat-ods-applications # {"$openapi":"OC_PROJECT"} 13 | -------------------------------------------------------------------------------- /config/scorecard/kustomization.yaml: -------------------------------------------------------------------------------- 1 | resources: 2 | - bases/config.yaml 3 | patchesJson6902: 4 | - path: patches/basic.config.patch 5 | target: 6 | group: scorecard.operatorframework.io 7 | version: v1alpha3 8 | kind: Configuration 9 | name: config 10 | - path: patches/olm.config.patch 11 | target: 12 | group: scorecard.operatorframework.io 13 | version: v1alpha3 14 | kind: Configuration 15 | name: config 16 | #+kubebuilder:scaffold:patchesJson6902 17 | -------------------------------------------------------------------------------- /helm-charts/aikitcontainer/.helmignore: -------------------------------------------------------------------------------- 1 | # Patterns to ignore when building packages. 2 | # This supports shell glob matching, relative path matching, and 3 | # negation (prefixed with !). Only one pattern per line. 4 | .DS_Store 5 | # Common VCS dirs 6 | .git/ 7 | .gitignore 8 | .bzr/ 9 | .bzrignore 10 | .hg/ 11 | .hgignore 12 | .svn/ 13 | # Common backup files 14 | *.swp 15 | *.bak 16 | *.tmp 17 | *.orig 18 | *~ 19 | # Various IDEs 20 | .project 21 | .idea/ 22 | *.tmproj 23 | .vscode/ 24 | -------------------------------------------------------------------------------- /bundle/kustomization.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: kustomize.config.k8s.io/v1beta1 2 | kind: Kustomization 3 | namespace: redhat-ods-applications # {"$openapi":"OC_PROJECT"} 4 | # Value of this field is prepended to the 5 | # names of all resources, e.g. a deployment named 6 | # "wordpress" becomes "alices-wordpress". 7 | # Note that it should also match with the prefix (text before '-') of the namespace 8 | # field above. 9 | namePrefix: aikit-operator- 10 | resources: 11 | - crd 12 | - rbac 13 | - manager 14 | -------------------------------------------------------------------------------- /config/kustomization.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: kustomize.config.k8s.io/v1beta1 2 | kind: Kustomization 3 | namespace: redhat-ods-applications # {"$openapi":"OC_PROJECT"} 4 | # Value of this field is prepended to the 5 | # names of all resources, e.g. a deployment named 6 | # "wordpress" becomes "alices-wordpress". 7 | # Note that it should also match with the prefix (text before '-') of the namespace 8 | # field above. 9 | namePrefix: aikit-operator- 10 | resources: 11 | - crd 12 | - rbac 13 | - manager 14 | -------------------------------------------------------------------------------- /config/samples/aikit_v1alpha1_aikitoperator.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: aikit.intel/v1alpha1 2 | kind: AIKitContainer 3 | metadata: 4 | name: create-imagestream 5 | spec: 6 | # Default values copied from /helm-charts/aikitcontainer/values.yaml 7 | nameOverride: "" 8 | fullnameOverride: "" 9 | registry: 10 | root: quay.io 11 | repo: opendatahub/workbench-images 12 | opendatahub: 13 | commit: "20240430" 14 | namespace: redhat-ods-applications # {"$openapi":"OC_PROJECT"} 15 | -------------------------------------------------------------------------------- /config/rbac/kustomization.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: kustomize.config.k8s.io/v1beta1 2 | kind: Kustomization 3 | resources: 4 | # All RBAC will be applied under this service account in 5 | # the deployment namespace. You may comment out this resource 6 | # if your manager will use a service account that exists at 7 | # runtime. Be sure to update RoleBinding and ClusterRoleBinding 8 | # subjects if changing service account names. 9 | - service_account.yaml 10 | - role.yaml 11 | - role_binding.yaml 12 | - leader_election_role.yaml 13 | - leader_election_role_binding.yaml 14 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PROJECT NOT UNDER ACTIVE MANAGEMENT # 2 | This project will no longer be maintained by Intel. 3 | Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project. 4 | Intel no longer accepts patches to this project. 5 | If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project. 6 | 7 | # aikit-operator 8 | 9 | See `make help` 10 | -------------------------------------------------------------------------------- /config/rbac/leader_election_role.yaml: -------------------------------------------------------------------------------- 1 | # permissions to do leader election. 2 | apiVersion: rbac.authorization.k8s.io/v1 3 | kind: Role 4 | metadata: 5 | name: leader-election-role 6 | rules: 7 | - apiGroups: 8 | - "" 9 | resources: 10 | - configmaps 11 | verbs: 12 | - get 13 | - list 14 | - watch 15 | - create 16 | - update 17 | - patch 18 | - delete 19 | - apiGroups: 20 | - coordination.k8s.io 21 | resources: 22 | - leases 23 | verbs: 24 | - get 25 | - list 26 | - watch 27 | - create 28 | - update 29 | - patch 30 | - delete 31 | - apiGroups: 32 | - "" 33 | resources: 34 | - events 35 | verbs: 36 | - create 37 | - patch 38 | -------------------------------------------------------------------------------- /bundle/metadata/annotations.yaml: -------------------------------------------------------------------------------- 1 | annotations: 2 | # Core bundle annotations. 3 | operators.operatorframework.io.bundle.mediatype.v1: registry+v1 4 | operators.operatorframework.io.bundle.manifests.v1: manifests/ 5 | operators.operatorframework.io.bundle.metadata.v1: metadata/ 6 | operators.operatorframework.io.bundle.package.v1: aikit-operator 7 | operators.operatorframework.io.bundle.channels.v1: alpha 8 | operators.operatorframework.io.metrics.builder: operator-sdk-v1.9.2 9 | operators.operatorframework.io.metrics.mediatype.v1: metrics+v1 10 | operators.operatorframework.io.metrics.project_layout: helm.sdk.operatorframework.io/v1 11 | # Annotations for testing. 12 | operators.operatorframework.io.test.mediatype.v1: scorecard+v1 13 | operators.operatorframework.io.test.config.v1: tests/scorecard/ 14 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # Build the manager binary 2 | FROM registry.redhat.io/openshift4/ose-helm-operator:v4.9 3 | 4 | ### Required OpenShift Labels 5 | LABEL name="Intel\u00ae oneAPI Analytics Toolkit Operator" \ 6 | maintainer="abolfazl.shahbazi@intel.com" \ 7 | vendor="Intel Corporation" \ 8 | version="v2023.1.0" \ 9 | release="2023.1.0" \ 10 | summary="Intel\u00ae oneAPI Analytics Toolkit Operator" \ 11 | description="Intel\u00ae oneAPI Analytics Toolkit Operator" 12 | 13 | USER root 14 | RUN yum -y update-minimal --security --sec-severity=Important --sec-severity=Critical && \ 15 | yum -y update cyrus-sasl-lib 16 | USER 1001 17 | 18 | ENV HOME=/opt/helm 19 | 20 | COPY licenses /licenses 21 | COPY watches.yaml ${HOME}/watches.yaml 22 | COPY helm-charts ${HOME}/helm-charts 23 | 24 | WORKDIR ${HOME} 25 | -------------------------------------------------------------------------------- /helm-charts/aikitcontainer/templates/_helpers.tpl: -------------------------------------------------------------------------------- 1 | {{/* 2 | Expand the name of the chart. 3 | */}} 4 | {{- define "aikitcontainer.name" -}} 5 | {{- default .Chart.Name .Values.nameOverride | trunc 63 | trimSuffix "-" }} 6 | {{- end }} 7 | 8 | {{/* 9 | Create a default fully qualified app name. 10 | We truncate at 63 chars because some Kubernetes name fields are limited to this (by the DNS naming spec). 11 | If release name contains chart name it will be used as a full name. 12 | */}} 13 | {{- define "aikitcontainer.fullname" -}} 14 | {{- if .Values.fullnameOverride }} 15 | {{- .Values.fullnameOverride | trunc 63 | trimSuffix "-" }} 16 | {{- else }} 17 | {{- $name := default .Chart.Name .Values.nameOverride }} 18 | {{- if contains $name .Release.Name }} 19 | {{- .Release.Name | trunc 63 | trimSuffix "-" }} 20 | {{- else }} 21 | {{- printf "%s-%s" .Release.Name $name | trunc 63 | trimSuffix "-" }} 22 | {{- end }} 23 | {{- end }} 24 | {{- end }} 25 | 26 | {{/* 27 | Create chart name and version as used by the chart label. 28 | */}} 29 | {{- define "aikitcontainer.chart" -}} 30 | {{- printf "%s-%s" .Chart.Name .Chart.Version | replace "+" "_" | trunc 63 | trimSuffix "-" }} 31 | {{- end }} 32 | -------------------------------------------------------------------------------- /bundle.Dockerfile: -------------------------------------------------------------------------------- 1 | FROM scratch 2 | 3 | # Core bundle labels. 4 | LABEL operators.operatorframework.io.bundle.mediatype.v1=registry+v1 5 | LABEL operators.operatorframework.io.bundle.manifests.v1=manifests/ 6 | LABEL operators.operatorframework.io.bundle.metadata.v1=metadata/ 7 | LABEL operators.operatorframework.io.bundle.package.v1=aikit-operator 8 | LABEL operators.operatorframework.io.bundle.channels.v1=alpha 9 | LABEL operators.operatorframework.io.metrics.builder=operator-sdk-v1.9.2 10 | LABEL operators.operatorframework.io.metrics.mediatype.v1=metrics+v1 11 | LABEL operators.operatorframework.io.metrics.project_layout=helm.sdk.operatorframework.io/v1 12 | 13 | #Add these labels 14 | LABEL com.redhat.openshift.versions="v4.9" 15 | LABEL com.redhat.delivery.operator.bundle=true 16 | LABEL com.redhat.delivery.backport=false 17 | 18 | # Labels for testing. 19 | LABEL operators.operatorframework.io.test.mediatype.v1=scorecard+v1 20 | LABEL operators.operatorframework.io.test.config.v1=tests/scorecard/ 21 | 22 | # Copy files to locations specified by labels. 23 | COPY licenses /licenses 24 | COPY bundle/manifests /manifests/ 25 | COPY bundle/metadata /metadata/ 26 | COPY bundle/tests/scorecard /tests/scorecard/ 27 | -------------------------------------------------------------------------------- /config/scorecard/patches/olm.config.patch: -------------------------------------------------------------------------------- 1 | - op: add 2 | path: /stages/0/tests/- 3 | value: 4 | entrypoint: 5 | - scorecard-test 6 | - olm-bundle-validation 7 | image: quay.io/operator-framework/scorecard-test:v1.8.0 8 | labels: 9 | suite: olm 10 | test: olm-bundle-validation-test 11 | - op: add 12 | path: /stages/0/tests/- 13 | value: 14 | entrypoint: 15 | - scorecard-test 16 | - olm-crds-have-validation 17 | image: quay.io/operator-framework/scorecard-test:v1.8.0 18 | labels: 19 | suite: olm 20 | test: olm-crds-have-validation-test 21 | - op: add 22 | path: /stages/0/tests/- 23 | value: 24 | entrypoint: 25 | - scorecard-test 26 | - olm-crds-have-resources 27 | image: quay.io/operator-framework/scorecard-test:v1.8.0 28 | labels: 29 | suite: olm 30 | test: olm-crds-have-resources-test 31 | - op: add 32 | path: /stages/0/tests/- 33 | value: 34 | entrypoint: 35 | - scorecard-test 36 | - olm-spec-descriptors 37 | image: quay.io/operator-framework/scorecard-test:v1.8.0 38 | labels: 39 | suite: olm 40 | test: olm-spec-descriptors-test 41 | - op: add 42 | path: /stages/0/tests/- 43 | value: 44 | entrypoint: 45 | - scorecard-test 46 | - olm-status-descriptors 47 | image: quay.io/operator-framework/scorecard-test:v1.8.0 48 | labels: 49 | suite: olm 50 | test: olm-status-descriptors-test 51 | -------------------------------------------------------------------------------- /config/rbac/role.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: rbac.authorization.k8s.io/v1 2 | kind: ClusterRole 3 | metadata: 4 | name: manager-role 5 | rules: 6 | ## 7 | ## Base operator rules 8 | ## 9 | # We need to get namespaces so the operator can read namespaces to ensure they exist 10 | - apiGroups: 11 | - "" 12 | resources: 13 | - namespaces 14 | verbs: 15 | - get 16 | # We need to manage Helm release secrets 17 | - apiGroups: 18 | - "" 19 | resources: 20 | - secrets 21 | verbs: 22 | - "*" 23 | # We need to create events on CRs about things happening during reconciliation 24 | - apiGroups: 25 | - "" 26 | resources: 27 | - events 28 | verbs: 29 | - create 30 | 31 | ## 32 | ## Rules for aikit.intel/v1alpha1, Kind: AIKitContainer 33 | ## 34 | - apiGroups: 35 | - aikit.intel 36 | resources: 37 | - aikitcontainers 38 | - aikitcontainers/status 39 | - aikitcontainers/finalizers 40 | verbs: 41 | - create 42 | - delete 43 | - get 44 | - list 45 | - patch 46 | - update 47 | - watch 48 | - verbs: 49 | - "*" 50 | apiGroups: 51 | - "" 52 | resources: 53 | - "serviceaccounts" 54 | - "services" 55 | - verbs: 56 | - "*" 57 | apiGroups: 58 | - "apps" 59 | resources: 60 | - "deployments" 61 | 62 | ## 63 | ## Rules for image.openshift.io/v1, kind: ImageStream 64 | ## 65 | - apiGroups: 66 | - image.openshift.io 67 | resources: 68 | - imagestreams 69 | verbs: 70 | - "*" 71 | 72 | #+kubebuilder:scaffold:rules 73 | -------------------------------------------------------------------------------- /bundle/tests/scorecard/config.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: scorecard.operatorframework.io/v1alpha3 2 | kind: Configuration 3 | metadata: 4 | name: config 5 | stages: 6 | - parallel: true 7 | tests: 8 | - entrypoint: 9 | - scorecard-test 10 | - basic-check-spec 11 | image: quay.io/operator-framework/scorecard-test:v1.8.0 12 | labels: 13 | suite: basic 14 | test: basic-check-spec-test 15 | - entrypoint: 16 | - scorecard-test 17 | - olm-bundle-validation 18 | image: quay.io/operator-framework/scorecard-test:v1.8.0 19 | labels: 20 | suite: olm 21 | test: olm-bundle-validation-test 22 | - entrypoint: 23 | - scorecard-test 24 | - olm-crds-have-validation 25 | image: quay.io/operator-framework/scorecard-test:v1.8.0 26 | labels: 27 | suite: olm 28 | test: olm-crds-have-validation-test 29 | - entrypoint: 30 | - scorecard-test 31 | - olm-crds-have-resources 32 | image: quay.io/operator-framework/scorecard-test:v1.8.0 33 | labels: 34 | suite: olm 35 | test: olm-crds-have-resources-test 36 | - entrypoint: 37 | - scorecard-test 38 | - olm-spec-descriptors 39 | image: quay.io/operator-framework/scorecard-test:v1.8.0 40 | labels: 41 | suite: olm 42 | test: olm-spec-descriptors-test 43 | - entrypoint: 44 | - scorecard-test 45 | - olm-status-descriptors 46 | image: quay.io/operator-framework/scorecard-test:v1.8.0 47 | labels: 48 | suite: olm 49 | test: olm-status-descriptors-test 50 | -------------------------------------------------------------------------------- /config/Krmfile: -------------------------------------------------------------------------------- 1 | apiVersion: config.k8s.io/v1alpha1 2 | kind: Krmfile 3 | openAPI: 4 | definitions: 5 | io.k8s.cli.setters.OC_PROJECT: 6 | description: project name 7 | x-k8s-cli: 8 | setter: 9 | name: OC_PROJECT 10 | value: redhat-ods-applications 11 | isSet: true 12 | required: true 13 | io.k8s.cli.setters.IMAGE_TAG_BASE: 14 | description: registry name base 15 | x-k8s-cli: 16 | setter: 17 | name: IMAGE_TAG_BASE 18 | value: registry.connect.redhat.com/intel/aikit-operator 19 | isSet: true 20 | required: true 21 | io.k8s.cli.setters.IMAGE_NAME: 22 | description: image name in registry 23 | x-k8s-cli: 24 | setter: 25 | name: IMAGE_NAME 26 | value: aikit-operator 27 | isSet: true 28 | required: true 29 | io.k8s.cli.setters.VERSION: 30 | description: version 31 | x-k8s-cli: 32 | setter: 33 | name: VERSION 34 | value: "2024.1.0" 35 | isSet: true 36 | required: true 37 | io.k8s.cli.substitutions.IMAGE: 38 | x-k8s-cli: 39 | substitution: 40 | name: IMAGE 41 | pattern: ${IMAGE_TAG_BASE}/${IMAGE_NAME}:${VERSION} 42 | values: 43 | - marker: ${IMAGE_TAG_BASE} 44 | ref: '#/definitions/io.k8s.cli.setters.IMAGE_TAG_BASE' 45 | - marker: ${IMAGE_NAME} 46 | ref: '#/definitions/io.k8s.cli.setters.IMAGE_NAME' 47 | - marker: ${VERSION} 48 | ref: '#/definitions/io.k8s.cli.setters.VERSION' 49 | -------------------------------------------------------------------------------- /config/manager/manager.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: v1 2 | kind: Namespace 3 | metadata: 4 | labels: 5 | control-plane: controller-manager 6 | name: system 7 | --- 8 | apiVersion: apps/v1 9 | kind: Deployment 10 | metadata: 11 | name: controller-manager 12 | namespace: system 13 | labels: 14 | control-plane: controller-manager 15 | spec: 16 | selector: 17 | matchLabels: 18 | control-plane: controller-manager 19 | replicas: 1 20 | template: 21 | metadata: 22 | labels: 23 | control-plane: controller-manager 24 | spec: 25 | securityContext: 26 | runAsNonRoot: true 27 | containers: 28 | - args: 29 | - --enable-leader-election 30 | - --leader-election-id=aikit-operator 31 | env: 32 | - name: WATCH_NAMESPACE 33 | value: redhat-ods-applications # {"$openapi":"OC_PROJECT"} 34 | image: controller:latest 35 | imagePullPolicy: Always 36 | name: manager 37 | securityContext: 38 | allowPrivilegeEscalation: false 39 | # livenessProbe: 40 | # httpGet: 41 | # path: /healthz 42 | # port: 8081 43 | # initialDelaySeconds: 15 44 | # periodSeconds: 20 45 | # readinessProbe: 46 | # httpGet: 47 | # path: /readyz 48 | # port: 8081 49 | # initialDelaySeconds: 5 50 | # periodSeconds: 10 51 | resources: 52 | limits: 53 | cpu: 100m 54 | memory: 3000Mi 55 | requests: 56 | cpu: 100m 57 | memory: 2000Mi 58 | serviceAccountName: controller-manager 59 | terminationGracePeriodSeconds: 10 60 | -------------------------------------------------------------------------------- /bundle/Krmfile: -------------------------------------------------------------------------------- 1 | apiVersion: config.k8s.io/v1alpha1 2 | kind: Krmfile 3 | openAPI: 4 | definitions: 5 | io.k8s.cli.setters.OC_PROJECT: 6 | description: project name 7 | x-k8s-cli: 8 | setter: 9 | name: OC_PROJECT 10 | value: redhat-ods-applications 11 | isSet: true 12 | required: true 13 | io.k8s.cli.setters.IMAGE_TAG_BASE: 14 | description: registry name base 15 | x-k8s-cli: 16 | setter: 17 | name: IMAGE_TAG_BASE 18 | value: registry.connect.redhat.com/intel/aikit-operator 19 | isSet: true 20 | required: true 21 | io.k8s.cli.setters.IMAGE_NAME: 22 | description: image name in registry 23 | x-k8s-cli: 24 | setter: 25 | name: IMAGE_NAME 26 | value: aikit-operator 27 | isSet: true 28 | required: true 29 | io.k8s.cli.setters.VERSION: 30 | description: version 31 | x-k8s-cli: 32 | setter: 33 | name: VERSION 34 | value: "2024.1.0" 35 | isSet: true 36 | required: true 37 | io.k8s.cli.substitutions.IMAGE: 38 | x-k8s-cli: 39 | substitution: 40 | name: IMAGE 41 | pattern: ${IMAGE_TAG_BASE}:${VERSION} 42 | values: 43 | - marker: ${IMAGE_TAG_BASE} 44 | ref: '#/definitions/io.k8s.cli.setters.IMAGE_TAG_BASE' 45 | - marker: ${VERSION} 46 | ref: '#/definitions/io.k8s.cli.setters.VERSION' 47 | io.k8s.cli.substitutions.CSV_NAME: 48 | x-k8s-cli: 49 | substitution: 50 | name: CSV_NAME 51 | pattern: ${IMAGE_NAME}.v${VERSION} 52 | values: 53 | - marker: ${IMAGE_NAME} 54 | ref: '#/definitions/io.k8s.cli.setters.IMAGE_NAME' 55 | - marker: ${VERSION} 56 | ref: '#/definitions/io.k8s.cli.setters.VERSION' 57 | -------------------------------------------------------------------------------- /config/crd/bases/aikit.intel_aikitcontainers.yaml: -------------------------------------------------------------------------------- 1 | --- 2 | apiVersion: apiextensions.k8s.io/v1 3 | kind: CustomResourceDefinition 4 | metadata: 5 | name: aikitcontainers.aikit.intel 6 | spec: 7 | group: aikit.intel 8 | names: 9 | kind: AIKitContainer 10 | listKind: AIKitContainerList 11 | plural: aikitcontainers 12 | singular: aikitcontainer 13 | scope: Namespaced 14 | versions: 15 | - name: v1alpha1 16 | schema: 17 | openAPIV3Schema: 18 | description: AIKitContainer is the Schema for the aikitcontainers API 19 | properties: 20 | apiVersion: 21 | description: 'APIVersion defines the versioned schema of this representation 22 | of an object. Servers should convert recognized schemas to the latest 23 | internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources' 24 | type: string 25 | kind: 26 | description: 'Kind is a string value representing the REST resource this 27 | object represents. Servers may infer this from the endpoint the client 28 | submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds' 29 | type: string 30 | metadata: 31 | type: object 32 | spec: 33 | description: Spec defines the desired state of AIKitContainer 34 | type: object 35 | x-kubernetes-preserve-unknown-fields: true 36 | status: 37 | description: Status defines the observed state of AIKitContainer 38 | type: object 39 | x-kubernetes-preserve-unknown-fields: true 40 | type: object 41 | served: true 42 | storage: true 43 | subresources: 44 | status: {} 45 | -------------------------------------------------------------------------------- /oneapi-aikit-2021-2-0-d1e720a.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: image.openshift.io/v1 2 | kind: ImageStream 3 | metadata: 4 | annotations: 5 | opendatahub.io/notebook-image-desc: Jupyter notebook image with oneAPI AI Analytics 6 | Toolkit and dependencies to start experimenting with advanced AI/ML notebooks. 7 | opendatahub.io/notebook-image-name: oneAPI AI Analytics Toolkit 8 | openshift.io/image.dockerRepositoryCheck: "2021-09-09T21:50:40Z" 9 | creationTimestamp: "2021-09-09T21:50:30Z" 10 | generation: 4 11 | labels: 12 | opendatahub.io/notebook-image: "true" 13 | name: oneapi-aikit-2021-2-0-d1e720a 14 | namespace: redhat-ods-applications 15 | resourceVersion: "10655552" 16 | uid: 3248789b-63f0-4ba3-ac0b-7a73406549be 17 | spec: 18 | lookupPolicy: 19 | local: false 20 | tags: 21 | - annotations: 22 | opendatahub.io/notebook-python-dependencies: '[{"name":"JupyterLab","version":"3.0.14"},{"name":"Notebook","version":"6.1.12"}]' 23 | opendatahub.io/notebook-software: '[{"name":"Python","version":"v3.7.9"}, {"name":"Tag","version":"2021-2-0-d1e720a"}]' 24 | from: 25 | kind: DockerImage 26 | name: registry.connect.redhat.com/intel/oneapi-aikit 27 | generation: 2 28 | importPolicy: {} 29 | name: latest 30 | referencePolicy: 31 | type: Source 32 | status: 33 | dockerImageRepository: image-registry.openshift-image-registry.svc:5000/redhat-ods-applications/oneapi-aikit-2021-2-0-d1e720a 34 | publicDockerImageRepository: default-route-openshift-image-registry.apps.intel-colabo.yp9m.p1.openshiftapps.com/redhat-ods-applications/oneapi-aikit-2021-2-0-d1e720a 35 | tags: 36 | - items: 37 | - created: "2021-09-09T21:50:40Z" 38 | dockerImageReference: registry.connect.redhat.com/intel/oneapi-aikit@sha256:03e51d4638c6e24504ccfcb82bc8fc7d03ffe2f220f5d0527a274009201ccbd8 39 | generation: 2 40 | image: sha256:03e51d4638c6e24504ccfcb82bc8fc7d03ffe2f220f5d0527a274009201ccbd8 41 | tag: latest 42 | -------------------------------------------------------------------------------- /bundle/manifests/aikit.intel_aikitcontainers.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: apiextensions.k8s.io/v1 2 | kind: CustomResourceDefinition 3 | metadata: 4 | creationTimestamp: null 5 | name: aikitcontainers.aikit.intel 6 | spec: 7 | group: aikit.intel 8 | names: 9 | kind: AIKitContainer 10 | listKind: AIKitContainerList 11 | plural: aikitcontainers 12 | singular: aikitcontainer 13 | scope: Namespaced 14 | versions: 15 | - name: v1alpha1 16 | schema: 17 | openAPIV3Schema: 18 | description: AIKitContainer is the Schema for the aikitcontainers API 19 | properties: 20 | apiVersion: 21 | description: 'APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources' 22 | type: string 23 | kind: 24 | description: 'Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds' 25 | type: string 26 | metadata: 27 | type: object 28 | spec: 29 | description: Spec defines the desired state of AIKitContainer 30 | type: object 31 | x-kubernetes-preserve-unknown-fields: true 32 | status: 33 | description: Status defines the observed state of AIKitContainer 34 | type: object 35 | x-kubernetes-preserve-unknown-fields: true 36 | type: object 37 | served: true 38 | storage: true 39 | subresources: 40 | status: {} 41 | status: 42 | acceptedNames: 43 | kind: "" 44 | plural: "" 45 | conditions: null 46 | storedVersions: null 47 | -------------------------------------------------------------------------------- /helm-charts/aikitcontainer/values.schema.json: -------------------------------------------------------------------------------- 1 | { 2 | "$schema": "https://json-schema.org/draft/2019-09/schema", 3 | "$id": "http://example.com/example.json", 4 | "type": "object", 5 | "default": {}, 6 | "title": "Root Schema", 7 | "required": [ 8 | "nameOverride", 9 | "fullnameOverride", 10 | "registry", 11 | "opendatahub", 12 | "namespace" 13 | ], 14 | "properties": { 15 | "nameOverride": { 16 | "type": "string", 17 | "default": "", 18 | "title": "The nameOverride Schema", 19 | "examples": [ 20 | "" 21 | ] 22 | }, 23 | "fullnameOverride": { 24 | "type": "string", 25 | "default": "", 26 | "title": "The fullnameOverride Schema", 27 | "examples": [ 28 | "" 29 | ] 30 | }, 31 | "registry": { 32 | "type": "object", 33 | "default": {}, 34 | "title": "The registry Schema", 35 | "required": [ 36 | "root", 37 | "repo" 38 | ], 39 | "properties": { 40 | "root": { 41 | "type": "string", 42 | "default": "", 43 | "title": "The root Schema", 44 | "examples": [ 45 | "quay.io" 46 | ] 47 | }, 48 | "repo": { 49 | "type": "string", 50 | "default": "", 51 | "title": "The repo Schema", 52 | "examples": [ 53 | "opendatahub/workbench-images" 54 | ] 55 | } 56 | }, 57 | "examples": [{ 58 | "root": "quay.io", 59 | "repo": "opendatahub/workbench-images" 60 | }] 61 | }, 62 | "opendatahub": { 63 | "type": "object", 64 | "default": {}, 65 | "title": "The opendatahub Schema", 66 | "required": [ 67 | "commit" 68 | ], 69 | "properties": { 70 | "commit": { 71 | "type": "string", 72 | "default": "", 73 | "title": "The commit Schema", 74 | "examples": [ 75 | "20240430" 76 | ] 77 | } 78 | }, 79 | "examples": [{ 80 | "commit": "20240430" 81 | }] 82 | }, 83 | "namespace": { 84 | "type": "string", 85 | "default": "", 86 | "title": "The namespace Schema", 87 | "examples": [ 88 | "redhat-ods-applications" 89 | ] 90 | } 91 | }, 92 | "examples": [{ 93 | "nameOverride": "", 94 | "fullnameOverride": "", 95 | "registry": { 96 | "root": "quay.io", 97 | "repo": "opendatahub/workbench-images" 98 | }, 99 | "opendatahub": { 100 | "commit": "20240430" 101 | }, 102 | "namespace": "redhat-ods-applications" 103 | }] 104 | } -------------------------------------------------------------------------------- /helm-charts/aikitcontainer/Chart.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: v2 2 | appVersion: 1.16.0 3 | description: A Helm chart for Kubernetes 4 | name: aikitcontainer 5 | kubeversion: ">= v1.20" 6 | type: application 7 | icon: "data:image/png;base64,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" 8 | version: 0.1.0 9 | -------------------------------------------------------------------------------- /helm-charts/aikitcontainer/templates/imagestream.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: image.openshift.io/v1 2 | kind: ImageStream 3 | metadata: 4 | annotations: 5 | opendatahub.io/notebook-image-creator: 'kube:admin' 6 | opendatahub.io/notebook-image-desc: '' 7 | opendatahub.io/notebook-image-name: Intel® Optimized TensorFlow 8 | opendatahub.io/notebook-image-url: 'quay.io/opendatahub/workbench-images:jupyter-intel-tensorflow-ubi9-python-3.9-{{ $.Values.opendatahub.commit }}' 9 | opendatahub.io/recommended-accelerators: '["gpu.intel.com/i915"]' 10 | labels: 11 | opendatahub.io/dashboard: 'true' 12 | opendatahub.io/notebook-image: "true" 13 | name: intel-tensorflow 14 | namespace: {{ $.Values.namespace }} 15 | spec: 16 | lookupPolicy: 17 | local: true 18 | tags: 19 | - name: intel-tensorflow 20 | annotations: 21 | opendatahub.io/notebook-software: '[{"name":"Intel® oneAPI","version":"2024.0"},{"name":"Python","version":"v3.9"},{"name":"TensorFlow","version":"2.14"},{"name":"Intel® Extension for TensorFlow","version":"2.14"}]' 22 | opendatahub.io/notebook-python-dependencies: '[{"name":"TensorFlow","version":"2.14"},{"name":"Intel® Extension for TensorFlow","version":"2.14"},{"name":"Tensorboard","version":"2.14"},{"name":"Boto3","version":"1.34"},{"name":"Kafka-Python","version":"2.0"},{"name":"Kfp-tekton","version":"1.5"},{"name":"Matplotlib","version":"3.6"},{"name":"Numpy","version":"1.26"},{"name":"Pandas","version":"1.5"},{"name":"Scikit-learn","version":"1.4"},{"name":"Scipy","version":"1.11"},{"name":"Elyra","version":"3.15"},{"name":"PyMongo","version":"4.5"},{"name":"Pyodbc","version":"4.0"}, {"name":"Codeflare-SDK","version":"0.13"}, {"name":"Sklearn-onnx","version":"1.15"}, {"name":"Psycopg","version":"3.1"}, {"name":"MySQL Connector/Python","version":"8.0"}]' 23 | openshift.io/imported-from: 'quay.io/opendatahub/workbench-images:jupyter-intel-tensorflow-ubi9-python-3.9-{{ $.Values.opendatahub.commit }}' 24 | from: 25 | kind: DockerImage 26 | name: {{ $.Values.registry.root }}/{{ $.Values.registry.repo }}:jupyter-intel-tensorflow-ubi9-python-3.9-{{ $.Values.opendatahub.commit }} 27 | referencePolicy: 28 | type: Source 29 | --- 30 | apiVersion: image.openshift.io/v1 31 | kind: ImageStream 32 | metadata: 33 | annotations: 34 | opendatahub.io/notebook-image-creator: 'kube:admin' 35 | opendatahub.io/notebook-image-desc: 'Jupyter notebook image with Intel® optimized PyTorch libraries and dependencies to start experimenting with advanced AI/ML notebooks.' 36 | opendatahub.io/notebook-image-name: Intel® Optimized PyTorch 37 | opendatahub.io/notebook-image-url: 'quay.io/opendatahub/workbench-images:jupyter-intel-pytorch-ubi9-python-3.9-{{ $.Values.opendatahub.commit }}' 38 | opendatahub.io/recommended-accelerators: '["gpu.intel.com/i915"]' 39 | labels: 40 | opendatahub.io/notebook-image: "true" 41 | name: intel-pytorch 42 | namespace: {{ $.Values.namespace }} 43 | spec: 44 | lookupPolicy: 45 | local: true 46 | tags: 47 | - name: intel-pytorch 48 | annotations: 49 | opendatahub.io/notebook-software: '[{"name":"Intel® oneAPI","version":"2024.0"},{"name":"Python","version":"v3.9"},{"name":"PyTorch","version":"2.1.2"},{"name":"Intel® Extension for PyTorch","version":"2.1.100"}]' 50 | opendatahub.io/notebook-python-dependencies: '[{"name":"PyTorch","version":"2.1.2"},{"name":"Intel® Extension for PyTorch","version":"2.1.100"},{"name":"Tensorboard","version":"2.15"},{"name":"Boto3","version":"1.34"},{"name":"Kafka-Python","version":"2.0"},{"name":"Kfp-tekton","version":"1.5"},{"name":"Matplotlib","version":"3.6"},{"name":"Numpy","version":"1.26"},{"name":"Pandas","version":"1.5"},{"name":"Scikit-learn","version":"1.4"},{"name":"Scipy","version":"1.11"},{"name":"Elyra","version":"3.15"},{"name":"PyMongo","version":"4.5"},{"name":"Pyodbc","version":"4.0"}, {"name":"Codeflare-SDK","version":"0.13"}, {"name":"Sklearn-onnx","version":"1.15"}, {"name":"Psycopg","version":"3.1"}, {"name":"MySQL Connector/Python","version":"8.0"}]' 51 | openshift.io/imported-from: 'quay.io/opendatahub/workbench-images:jupyter-intel-pytorch-ubi9-python-3.9-{{ $.Values.opendatahub.commit }}' 52 | from: 53 | kind: DockerImage 54 | name: {{ $.Values.registry.root }}/{{ $.Values.registry.repo }}:jupyter-intel-pytorch-ubi9-python-3.9-{{ $.Values.opendatahub.commit }} 55 | referencePolicy: 56 | type: Source 57 | --- 58 | apiVersion: image.openshift.io/v1 59 | kind: ImageStream 60 | metadata: 61 | annotations: 62 | opendatahub.io/notebook-image-creator: 'kube:admin' 63 | opendatahub.io/notebook-image-desc: 'Jupyter notebook image with Intel® optimized Machine Learning libraries and dependencies to start experimenting with advanced AI/ML notebooks.' 64 | opendatahub.io/notebook-image-name: Intel® Optimized ML 65 | opendatahub.io/notebook-image-url: 'quay.io/opendatahub/workbench-images:jupyter-intel-ml-ubi9-python-3.9-{{ $.Values.opendatahub.commit }}' 66 | opendatahub.io/recommended-accelerators: '[]' 67 | labels: 68 | app.kubernetes.io/created-by: byon 69 | opendatahub.io/dashboard: 'true' 70 | opendatahub.io/notebook-image: 'true' 71 | name: intel-ml 72 | namespace: {{ $.Values.namespace }} 73 | spec: 74 | lookupPolicy: 75 | local: true 76 | tags: 77 | - name: intel-ml 78 | annotations: 79 | opendatahub.io/notebook-software: '[{"name":"Python","version":"v3.9"}]' 80 | opendatahub.io/notebook-python-dependencies: '[{"name":"Boto3","version":"1.34"},{"name":"Kafka-Python","version":"2.0"},{"name":"Kfp","version":"1.8"},{"name":"Matplotlib","version":"3.6"},{"name":"Numpy","version":"1.24"},{"name":"Pandas","version":"2.1"},{"name":"Scikit-learn","version":"1.3"},{"name":"Scipy","version":"1.11"},{"name":"Odh-Elyra","version":"3.15"},{"name":"PyMongo","version":"4.5"},{"name":"Pyodbc","version":"4.0"}, {"name":"Codeflare-SDK","version":"0.13"}, {"name":"Sklearn-onnx","version":"1.15"}, {"name":"Psycopg","version":"3.1"}, {"name":"MySQL Connector/Python","version":"8.0"}]' 81 | openshift.io/imported-from: 'quay.io/opendatahub/workbench-images:jupyter-intel-ml-ubi9-python-{{ $.Values.opendatahub.commit }}' 82 | from: 83 | kind: DockerImage 84 | name: {{ $.Values.registry.root }}/{{ $.Values.registry.repo }}:jupyter-intel-ml-ubi9-python-3.9-{{ $.Values.opendatahub.commit }} 85 | referencePolicy: 86 | type: Source 87 | -------------------------------------------------------------------------------- /quickstart_guide/Intel_oneAPI_AI_Analytics_Toolkit_Operator_README.md: -------------------------------------------------------------------------------- 1 | # Intel® oneAPI AI Analytics Toolkit Operator 2 | The Operator installs and manages development tools and production AI deployments in an OpenShift cluster. It enables easy deployment and management of AI inference services by creating a `Notebook` resource. 3 | 4 | The Operator also integrates with the JupyterHub [Spawner](https://jupyterhub.readthedocs.io/en/stable/reference/spawners.html) in [Red Hat OpenShift Data Science](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-data-science) and [Open Data Hub](https://opendatahub.io/docs.html). See [detailed instructions](#integration-with-openshift-data-science-and-open-data-hub) below. 5 | 6 | ## Install the Operator 7 | 8 | ### Operator Installation from Red Hat Marketplace 9 | 1. For information on registering your cluster and creating a namespace, see [Red Hat Marketplace Docs](https://marketplace.redhat.com/en-us/documentation/clusters). This must be done prior to operator install. 10 | 2. On the main menu, click **Workspace > My Software > Intel® oneAPI AI Analytics Toolkit > Install Operator**. 11 | 3. On the **Update Channel** section, select an option. 12 | 4. On the **Approval Strategy** section, select either Automatic or Manual. The approval strategy corresponds to how you want to process operator upgrades. 13 | 5. On the **Target Cluster** section: 14 | - Click the checkbox next to the clusters where you want to install the Operator. 15 | - For each cluster you selected, under **Namespace Scope**, on the **Select Scope** list, select an option. 16 | 6. Click **Install**. It may take several minutes for installation to complete. 17 | 7. Once installation is complete, the status will change from **installing** to **Up to date**. 18 | 8. For further information, see the [Red Hat Marketplace Operator documentation](https://marketplace.redhat.com/en-us/documentation/operators) 19 | 20 | ![install](images/install.png) 21 | 22 | ### OpenShift Web Console 23 | In the OpenShift [web console](https://docs.openshift.com/container-platform/4.7/web_console/web-console.html) navigate to OperatorHub menu. Search for "oneAPI AI" and select "Intel oneAPI AI Analytics Toolkit". Then, click the `Install` button. 24 | 25 | ## Verification of Operator Installation 26 | 1. Once status changes to Up to date, click the vertical ellipses and select Cluster Console. 27 | 2. Open the cluster where you installed the product 28 | 3. Go to **Operators > Installed Operators** 29 | 4. Select the Namespace or Project you installed on 30 | 5. Verify status for product is **Succeeded** 31 | 6. Click the product name to open details 32 | 33 | ![succeeded](images/rhods_succeeded.PNG) 34 | 35 | 36 | ## Deploy the Operator 37 | 38 | ### Prerequsite 39 | In order to deploy the AI Kit operator, the Red Hat OpenShift Data Science operator must already be installed in the same `namespace/project` that you want to deploy the operator in. Please see the [Installing OpenShift Data Science Documentation](https://access.redhat.com/documentation/en-us/red_hat_openshift_data_science/1/html/installing_openshift_data_science/index) for more information. 40 | 41 | ### From the OpenShift Console 42 | 1. Once you have installed the Operator in OpenShift, you can manage it using the web console. Navigate to the `Installed Operators` section under `Operators` in your OpenShift Dedicated console. 43 | 44 | 2. On this page, in the top left, select the `project/namespace` that you would like to deploy the AI Kit operator in. Please ensure that the prerequisite operator "Red Hat OpenShift Data Science" is already installed in the desired `project/namespace` that you would like to deploy in before going further. 45 | 46 | - In this case, we select `redhat-ods-applications` as our project namespace, which already has the Red Hat OpenShift Data Science operator installed. 47 | 48 | ![namespace](images/namespace.PNG) 49 | 50 | 3. From the list of installed operators, select and click on the Intel® oneAPI AI Analytics Toolkit operator. You can create a new instance under the `Details` section of the operator by clicking `Create Instance`. You can also click on the `AIKitContainer` section to view existing instances with AI Kit installed, and select `Create AIKitContainer` in the top right to accomplish the same functionality. 51 | 52 | 4. From here, you have options to click `Create` with the default name of the AI Kit container and ImageStream for your namespace, and also rename the the container to your specifications and add any labels you would like in the relevant fields. After this is done, click `Create`. 53 | - For further customization of the AI Kit Container metadata, including ImageStream name, please select `YAML view` and edit details there. Once this is complete, select `Create`. 54 | 55 | 5. Your operator should now be deployed in your desired namespace, as shown in the `Status` column of the `AIKitContainer` section. 56 | 57 | 58 | ## Integration with OpenShift Data Science and Open Data Hub 59 | The Operator integrates with the JupyterHub [Spawner](https://jupyterhub.readthedocs.io/en/stable/reference/spawners.html) in [Red Hat OpenShift Data Science](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-data-science) and [Open Data Hub](https://opendatahub.io/docs.html). 60 | 61 | Simply create a `Notebook` resource, which deploys a JupyterLab interface containing the AI Kit developer tools and ready-to-run Jupyter notebooks. Here's how: 62 | 63 | 1. Click The `Launch` button in the web console on the `Intel® oneAPI AI Analytics Toolkit` tile of the `Enabled` section. This will build the container image and create an ImageStream. 64 | 2. This enables selecting `Intel® oneAPI AI Analytics Toolkit` image from the Jupyter Spawner `Notebook Image` menu. Select the `Intel® oneAPI AI Analytics Toolkit` image, and any other configurations for your container, then press `Start server`. 65 | - The [`Intel® oneAPI AI Analytics Toolkit` image](https://github.com/intel-innersource/containers.orchestrators.kubernetes.operators.aikit-operator) is maintained by Intel. 66 | 67 | ![spawner](images/spawner.png) 68 | 69 | Once you spawn into the Intel® oneAPI AI Analytics Toolkit image, you should be launched into a JupyterLab dashboard similar to the image below. There is a `Welcome.ipynb` notebook as well as ready-to-run Jupyter notebook samples to help you get started. 70 | 71 | ![dashboard](images/dashboard.png) 72 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | # 2 | # Copyright (c) 2021 Intel Corporation 3 | # 4 | # Licensed under the Apache License, Version 2.0 (the "License"); 5 | # you may not use this file except in compliance with the License. 6 | # You may obtain a copy of the License at 7 | # 8 | # http://www.apache.org/licenses/LICENSE-2.0 9 | # 10 | # Unless required by applicable law or agreed to in writing, software 11 | # distributed under the License is distributed on an "AS IS" BASIS, 12 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 13 | # See the License for the specific language governing permissions and 14 | # limitations under the License. 15 | # 16 | 17 | # VERSION defines the project version for the bundle. 18 | # Update this value when you upgrade the version of your project. 19 | # To re-generate a bundle for another specific version without changing the standard setup, you can: 20 | # - use the VERSION as arg of the bundle target (e.g make bundle VERSION=2021.2.0) 21 | # - use environment variables to overwrite this value (e.g export VERSION=2021.2.0) 22 | GENERIC_VERSION ?= 2024.1 23 | # DAY_OF_YEAR ?= $(shell date +%-m%d%H) 24 | DAY_OF_YEAR ?= 0 # $(shell date +%-m%d%H) 25 | # VERSION ?= `echo ${GENERIC_VERSION} | sed "s/\(.*\)[0-9])*/\1${DAY_OF_YEAR}/"`-dev-`git rev-parse --short HEAD` 26 | # VERSION ?= `echo ${GENERIC_VERSION} | sed "s/\(.*\)[0-9])*/\1${DAY_OF_YEAR}/"` # -dev-`git rev-parse --short HEAD` 27 | VERSION ?= 2024.1.${DAY_OF_YEAR} 28 | 29 | # CHANNELS define the bundle channels used in the bundle. 30 | # Add a new line here if you would like to change its default config. (E.g CHANNELS = "preview,fast,stable") 31 | # To re-generate a bundle for other specific channels without changing the standard setup, you can: 32 | # - use the CHANNELS as arg of the bundle target (e.g make bundle CHANNELS=preview,fast,stable) 33 | # - use environment variables to overwrite this value (e.g export CHANNELS="preview,fast,stable") 34 | ifneq ($(origin CHANNELS), undefined) 35 | BUNDLE_CHANNELS := --channels=$(CHANNELS) 36 | endif 37 | 38 | # DEFAULT_CHANNEL defines the default channel used in the bundle. 39 | # Add a new line here if you would like to change its default config. (E.g DEFAULT_CHANNEL = "stable") 40 | # To re-generate a bundle for any other default channel without changing the default setup, you can: 41 | # - use the DEFAULT_CHANNEL as arg of the bundle target (e.g make bundle DEFAULT_CHANNEL=stable) 42 | # - use environment variables to overwrite this value (e.g export DEFAULT_CHANNEL="stable") 43 | ifneq ($(origin DEFAULT_CHANNEL), undefined) 44 | BUNDLE_DEFAULT_CHANNEL := --default-channel=$(DEFAULT_CHANNEL) 45 | endif 46 | BUNDLE_METADATA_OPTS ?= $(BUNDLE_CHANNELS) $(BUNDLE_DEFAULT_CHANNEL) 47 | 48 | OC_PROJECT ?= redhat-ods-applications 49 | GENERIC_IMAGE_TAG_BASE ?= registry.connect.redhat.com/intel/aikit-operator 50 | IMAGE_TAG_BASE ?= registry.connect.redhat.com/intel/aikit-operator 51 | 52 | # BUNDLE_IMG defines the image:tag used for the bundle. 53 | # You can use it as an arg. (E.g make bundle-build BUNDLE_IMG=/:) 54 | BUNDLE_IMG ?= $(IMAGE_TAG_BASE)-bundle:v$(VERSION) 55 | 56 | # Image URL to use all building/pushing image targets 57 | IMG ?= $(IMAGE_TAG_BASE):$(VERSION) 58 | 59 | version: # show version 60 | @echo ${VERSION} 61 | 62 | all: docker-build 63 | 64 | ##@ General 65 | 66 | # The help target prints out all targets with their descriptions organized 67 | # beneath their categories. The categories are represented by '##@' and the 68 | # target descriptions by '##'. The awk commands is responsible for reading the 69 | # entire set of makefiles included in this invocation, looking for lines of the 70 | # file as xyz: ## something, and then pretty-format the target and help. Then, 71 | # if there's a line with ##@ something, that gets pretty-printed as a category. 72 | # More info on the usage of ANSI control characters for terminal formatting: 73 | # https://en.wikipedia.org/wiki/ANSI_escape_code#SGR_parameters 74 | # More info on the awk command: 75 | # http://linuxcommand.org/lc3_adv_awk.php 76 | 77 | help: ## Display this help. 78 | @awk 'BEGIN {FS = ":.*##"; printf "\nUsage:\n make \033[36m\033[0m\n"} /^[a-zA-Z_0-9-]+:.*?##/ { printf " \033[36m%-25s\033[0m %s\n", $$1, $$2 } /^##@/ { printf "\n\033[1m%s\033[0m\n", substr($$0, 5) } ' $(MAKEFILE_LIST) 79 | 80 | ##@ Build 81 | docker-build: set-vars ## Calls set-vars and then builds the aikit-operator image with the manager. 82 | docker build --no-cache ${DOCKER_BUILD_ARGS} -t ${IMG} . 83 | 84 | docker-push: ## Pushes the aikit-operator image to $(IMG) and then resets OC_PROJECT, IMAGE_TAG_BASE and VERSION to their defaults. 85 | docker push ${IMG} 86 | # $(MAKE) set-defaults 87 | 88 | OS := $(shell uname -s | tr '[:upper:]' '[:lower:]') 89 | ARCH := $(shell uname -m | sed 's/x86_64/amd64/') 90 | 91 | .PHONY: kustomize 92 | KUSTOMIZE = $(shell pwd)/bin/kustomize 93 | kustomize: ## Downloads kustomize locally if necessary and install to $(pwd)/bin. 94 | ifeq (,$(wildcard $(KUSTOMIZE))) 95 | ifeq (,$(shell which kustomize 2>/dev/null)) 96 | @{ \ 97 | set -e ;\ 98 | mkdir -p $(dir $(KUSTOMIZE)) ;\ 99 | echo "Downloading kustomize ..." ;\ 100 | curl -SLo - https://github.com/kubernetes-sigs/kustomize/releases/download/kustomize/v4.3.0/kustomize_v4.3.0_$(OS)_$(ARCH).tar.gz | \ 101 | tar xzf - -C bin/ ;\ 102 | } 103 | else 104 | KUSTOMIZE = $(shell which kustomize) 105 | endif 106 | endif 107 | 108 | ##@ Prerequisite Commands 109 | .PHONY: helm-operator 110 | HELM_OPERATOR = $(shell pwd)/bin/helm-operator 111 | helm-operator: ## Downloads helm-operator locally if necessary, preferring the $(pwd)/bin path over global if both exist. 112 | ifeq (,$(wildcard $(HELM_OPERATOR))) 113 | ifeq (,$(shell which helm-operator 2>/dev/null)) 114 | @{ \ 115 | set -e ;\ 116 | mkdir -p $(dir $(HELM_OPERATOR)) ;\ 117 | echo "Downloading helm-operator ..." ;\ 118 | curl -SLo $(HELM_OPERATOR) https://github.com/operator-framework/operator-sdk/releases/download/v1.9.2/helm-operator_$(OS)_$(ARCH) ;\ 119 | chmod +x $(HELM_OPERATOR) ;\ 120 | } 121 | else 122 | HELM_OPERATOR = $(shell which helm-operator) 123 | endif 124 | endif 125 | 126 | ##@ Showing and Setting OC_PROJECT, IMAGE_TAG_BASE and VERSION values. Environment vars will override the defaults. 127 | show-vars: kustomize ## Show OC_PROJECT, IMAGE_TAG_BASE and VERSION values by calling 'kustomize cfg list-setters bundle/ --markdown -R' 128 | @echo -n 'Showing vars under directory ' 129 | @$(KUSTOMIZE) cfg list-setters bundle --markdown -R || exit 0 130 | @echo '' 131 | @echo -n 'Showing vars under directory ' 132 | @$(KUSTOMIZE) cfg list-setters config --markdown -R || exit 0 133 | 134 | set-vars: kustomize ## Set OC_PROJECT, IMAGE_TAG_BASE and VERSION values by calling 'kustomize cfg set '. Environment variables will override each var default. 135 | @$(KUSTOMIZE) cfg set config OC_PROJECT $(OC_PROJECT) -R 1>/dev/null || exit 0 136 | @$(KUSTOMIZE) cfg set config IMAGE_TAG_BASE $(IMAGE_TAG_BASE) -R 1>/dev/null || exit 0 137 | @$(KUSTOMIZE) cfg set config VERSION $(VERSION) -R 1>/dev/null || exit 0 138 | @$(KUSTOMIZE) cfg set bundle OC_PROJECT $(OC_PROJECT) -R 1>/dev/null || exit 0 139 | @$(KUSTOMIZE) cfg set bundle IMAGE_TAG_BASE $(IMAGE_TAG_BASE) -R 1>/dev/null || exit 0 140 | @$(KUSTOMIZE) cfg set bundle VERSION $(VERSION) -R 1>/dev/null || exit 0 141 | 142 | set-defaults: kustomize ## Sets OC_PROJECT, IMAGE_TAG_BASE and VERSION to their default values 143 | @unset OC_PROJECT IMAGE_TAG_BASE VERSION && $(MAKE) set-vars 144 | @$(KUSTOMIZE) cfg set config OC_PROJECT $(OC_PROJECT) -R 1>/dev/null || exit 0 145 | @$(KUSTOMIZE) cfg set config IMAGE_TAG_BASE $(GENERIC_IMAGE_TAG_BASE) -R 1>/dev/null || exit 0 146 | @$(KUSTOMIZE) cfg set config VERSION $(GENERIC_VERSION) -R 1>/dev/null || exit 0 147 | @$(KUSTOMIZE) cfg set bundle OC_PROJECT $(OC_PROJECT) -R 1>/dev/null || exit 0 148 | @$(KUSTOMIZE) cfg set bundle IMAGE_TAG_BASE $(GENERIC_IMAGE_TAG_BASE) -R 1>/dev/null || exit 0 149 | @$(KUSTOMIZE) cfg set bundle VERSION $(GENERIC_VERSION) -R 1>/dev/null || exit 0 150 | 151 | .PHONY: bundle-build 152 | bundle-build: set-vars ## Calls set-vars and then builds the bundle image $(BUNDLE_IMG). 153 | docker build --no-cache -f bundle.Dockerfile -t $(BUNDLE_IMG) . 154 | 155 | .PHONY: bundle-push 156 | bundle-push: ## Pushes the aikit-operator-bundle image to $(BUNDLE_IMG) and then resets OC_PROJECT, IMAGE_TAG_BASE and VERSION to their defaults. 157 | $(MAKE) docker-push IMG=$(BUNDLE_IMG) 158 | # $(MAKE) set-defaults 159 | 160 | .PHONY: opm 161 | OPM = ./bin/opm 162 | opm: ## Downloads opm locally if necessary and install to $(pwd)/bin. 163 | ifeq (,$(wildcard $(OPM))) 164 | ifeq (,$(shell which opm 2>/dev/null)) 165 | @{ \ 166 | set -e ;\ 167 | mkdir -p $(dir $(OPM)) ;\ 168 | echo "Downloading opm ..." ;\ 169 | curl -SLo $(OPM) https://github.com/operator-framework/operator-registry/releases/download/v1.18.0/$(OS)-$(ARCH)-opm ;\ 170 | chmod +x $(OPM) ;\ 171 | } 172 | else 173 | OPM = $(shell which opm) 174 | endif 175 | endif 176 | 177 | # A comma-separated list of bundle images (e.g. make catalog-build BUNDLE_IMGS=example.com/operator-bundle:v2021.2.0,example.com/operator-bundle:v2021.2.0). 178 | # These images MUST exist in a registry and be pull-able. 179 | BUNDLE_IMGS ?= $(BUNDLE_IMG) 180 | 181 | # The image tag given to the resulting catalog image (e.g. make catalog-build CATALOG_IMG=example.com/operator-catalog:v2021.2.0). 182 | CATALOG_IMG ?= $(IMAGE_TAG_BASE)-catalog:v$(VERSION) 183 | 184 | # Set CATALOG_BASE_IMG to an existing catalog image tag to add $BUNDLE_IMGS to that image. 185 | ifneq ($(origin CATALOG_BASE_IMG), undefined) 186 | FROM_INDEX_OPT := --from-index $(CATALOG_BASE_IMG) 187 | endif 188 | 189 | # Build a catalog image by adding bundle images to an empty catalog using the operator package manager tool, 'opm'. 190 | # This recipe invokes 'opm' in 'semver' bundle add mode. For more information on add modes, see: 191 | # https://github.com/operator-framework/community-operators/blob/7f1438c/docs/packaging-operator.md#updating-your-existing-operator 192 | .PHONY: catalog-build 193 | catalog-build: opm set-vars ## Calls set-vars and then builds the catalog image $(CATALOG_IMG). 194 | $(OPM) index add --container-tool docker --mode semver --tag $(CATALOG_IMG) --bundles $(BUNDLE_IMGS) $(FROM_INDEX_OPT) 195 | 196 | # Push the catalog image. 197 | .PHONY: catalog-push 198 | catalog-push: ## Pushes the aikit-operator-catalog`image to $(CATALOG_IMG) and then resets OC_PROJECT, IMAGE_TAG_BASE and VERSION to their defaults. 199 | $(MAKE) docker-push IMG=$(CATALOG_IMG) 200 | # $(MAKE) set-defaults 201 | 202 | ##@ Deployment 203 | deploy: set-vars ## Calls set-vars and deploys aikit-operator using operator-sdk and OLM 204 | operator-sdk run bundle -n $(OC_PROJECT) $(IMAGE_TAG_BASE)-bundle:v$(VERSION) 205 | # $(MAKE) set-defaults 206 | 207 | undeploy: set-vars ## Calls set-vars and undeploys aikit-operator deployed by operator-sdk and OLM 208 | operator-sdk cleanup aikit-operator 209 | $(MAKE) set-defaults 210 | 211 | .PHONY: bundle-validate 212 | bundle-validate: 213 | @operator-sdk bundle validate ./bundle --select-optional name=operatorhub --optional-values=k8s-version=1.20 --select-optional suite=operatorframework --optional-values=k8s-version=1.20 214 | -------------------------------------------------------------------------------- /licenses/LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. Definitions. 8 | 9 | "License" shall mean the terms and conditions for use, reproduction, 10 | and distribution as defined by Sections 1 through 9 of this document. 11 | 12 | "Licensor" shall mean the copyright owner or entity authorized by 13 | the copyright owner that is granting the License. 14 | 15 | "Legal Entity" shall mean the union of the acting entity and all 16 | other entities that control, are controlled by, or are under common 17 | control with that entity. For the purposes of this definition, 18 | "control" means (i) the power, direct or indirect, to cause the 19 | direction or management of such entity, whether by contract or 20 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 21 | outstanding shares, or (iii) beneficial ownership of such entity. 22 | 23 | "You" (or "Your") shall mean an individual or Legal Entity 24 | exercising permissions granted by this License. 25 | 26 | "Source" form shall mean the preferred form for making modifications, 27 | including but not limited to software source code, documentation 28 | source, and configuration files. 29 | 30 | "Object" form shall mean any form resulting from mechanical 31 | transformation or translation of a Source form, including but 32 | not limited to compiled object code, generated documentation, 33 | and conversions to other media types. 34 | 35 | "Work" shall mean the work of authorship, whether in Source or 36 | Object form, made available under the License, as indicated by a 37 | copyright notice that is included in or attached to the work 38 | (an example is provided in the Appendix below). 39 | 40 | "Derivative Works" shall mean any work, whether in Source or Object 41 | form, that is based on (or derived from) the Work and for which the 42 | editorial revisions, annotations, elaborations, or other modifications 43 | represent, as a whole, an original work of authorship. For the purposes 44 | of this License, Derivative Works shall not include works that remain 45 | separable from, or merely link (or bind by name) to the interfaces of, 46 | the Work and Derivative Works thereof. 47 | 48 | "Contribution" shall mean any work of authorship, including 49 | the original version of the Work and any modifications or additions 50 | to that Work or Derivative Works thereof, that is intentionally 51 | submitted to Licensor for inclusion in the Work by the copyright owner 52 | or by an individual or Legal Entity authorized to submit on behalf of 53 | the copyright owner. For the purposes of this definition, "submitted" 54 | means any form of electronic, verbal, or written communication sent 55 | to the Licensor or its representatives, including but not limited to 56 | communication on electronic mailing lists, source code control systems, 57 | and issue tracking systems that are managed by, or on behalf of, the 58 | Licensor for the purpose of discussing and improving the Work, but 59 | excluding communication that is conspicuously marked or otherwise 60 | designated in writing by the copyright owner as "Not a Contribution." 61 | 62 | "Contributor" shall mean Licensor and any individual or Legal Entity 63 | on behalf of whom a Contribution has been received by Licensor and 64 | subsequently incorporated within the Work. 65 | 66 | 2. Grant of Copyright License. Subject to the terms and conditions of 67 | this License, each Contributor hereby grants to You a perpetual, 68 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 69 | copyright license to reproduce, prepare Derivative Works of, 70 | publicly display, publicly perform, sublicense, and distribute the 71 | Work and such Derivative Works in Source or Object form. 72 | 73 | 3. Grant of Patent License. Subject to the terms and conditions of 74 | this License, each Contributor hereby grants to You a perpetual, 75 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 76 | (except as stated in this section) patent license to make, have made, 77 | use, offer to sell, sell, import, and otherwise transfer the Work, 78 | where such license applies only to those patent claims licensable 79 | by such Contributor that are necessarily infringed by their 80 | Contribution(s) alone or by combination of their Contribution(s) 81 | with the Work to which such Contribution(s) was submitted. If You 82 | institute patent litigation against any entity (including a 83 | cross-claim or counterclaim in a lawsuit) alleging that the Work 84 | or a Contribution incorporated within the Work constitutes direct 85 | or contributory patent infringement, then any patent licenses 86 | granted to You under this License for that Work shall terminate 87 | as of the date such litigation is filed. 88 | 89 | 4. Redistribution. You may reproduce and distribute copies of the 90 | Work or Derivative Works thereof in any medium, with or without 91 | modifications, and in Source or Object form, provided that You 92 | meet the following conditions: 93 | 94 | (a) You must give any other recipients of the Work or 95 | Derivative Works a copy of this License; and 96 | 97 | (b) You must cause any modified files to carry prominent notices 98 | stating that You changed the files; and 99 | 100 | (c) You must retain, in the Source form of any Derivative Works 101 | that You distribute, all copyright, patent, trademark, and 102 | attribution notices from the Source form of the Work, 103 | excluding those notices that do not pertain to any part of 104 | the Derivative Works; and 105 | 106 | (d) If the Work includes a "NOTICE" text file as part of its 107 | distribution, then any Derivative Works that You distribute must 108 | include a readable copy of the attribution notices contained 109 | within such NOTICE file, excluding those notices that do not 110 | pertain to any part of the Derivative Works, in at least one 111 | of the following places: within a NOTICE text file distributed 112 | as part of the Derivative Works; within the Source form or 113 | documentation, if provided along with the Derivative Works; or, 114 | within a display generated by the Derivative Works, if and 115 | wherever such third-party notices normally appear. The contents 116 | of the NOTICE file are for informational purposes only and 117 | do not modify the License. You may add Your own attribution 118 | notices within Derivative Works that You distribute, alongside 119 | or as an addendum to the NOTICE text from the Work, provided 120 | that such additional attribution notices cannot be construed 121 | as modifying the License. 122 | 123 | You may add Your own copyright statement to Your modifications and 124 | may provide additional or different license terms and conditions 125 | for use, reproduction, or distribution of Your modifications, or 126 | for any such Derivative Works as a whole, provided Your use, 127 | reproduction, and distribution of the Work otherwise complies with 128 | the conditions stated in this License. 129 | 130 | 5. Submission of Contributions. Unless You explicitly state otherwise, 131 | any Contribution intentionally submitted for inclusion in the Work 132 | by You to the Licensor shall be under the terms and conditions of 133 | this License, without any additional terms or conditions. 134 | Notwithstanding the above, nothing herein shall supersede or modify 135 | the terms of any separate license agreement you may have executed 136 | with Licensor regarding such Contributions. 137 | 138 | 6. Trademarks. This License does not grant permission to use the trade 139 | names, trademarks, service marks, or product names of the Licensor, 140 | except as required for reasonable and customary use in describing the 141 | origin of the Work and reproducing the content of the NOTICE file. 142 | 143 | 7. Disclaimer of Warranty. Unless required by applicable law or 144 | agreed to in writing, Licensor provides the Work (and each 145 | Contributor provides its Contributions) on an "AS IS" BASIS, 146 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 147 | implied, including, without limitation, any warranties or conditions 148 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 149 | PARTICULAR PURPOSE. You are solely responsible for determining the 150 | appropriateness of using or redistributing the Work and assume any 151 | risks associated with Your exercise of permissions under this License. 152 | 153 | 8. Limitation of Liability. In no event and under no legal theory, 154 | whether in tort (including negligence), contract, or otherwise, 155 | unless required by applicable law (such as deliberate and grossly 156 | negligent acts) or agreed to in writing, shall any Contributor be 157 | liable to You for damages, including any direct, indirect, special, 158 | incidental, or consequential damages of any character arising as a 159 | result of this License or out of the use or inability to use the 160 | Work (including but not limited to damages for loss of goodwill, 161 | work stoppage, computer failure or malfunction, or any and all 162 | other commercial damages or losses), even if such Contributor 163 | has been advised of the possibility of such damages. 164 | 165 | 9. Accepting Warranty or Additional Liability. While redistributing 166 | the Work or Derivative Works thereof, You may choose to offer, 167 | and charge a fee for, acceptance of support, warranty, indemnity, 168 | or other liability obligations and/or rights consistent with this 169 | License. However, in accepting such obligations, You may act only 170 | on Your own behalf and on Your sole responsibility, not on behalf 171 | of any other Contributor, and only if You agree to indemnify, 172 | defend, and hold each Contributor harmless for any liability 173 | incurred by, or claims asserted against, such Contributor by reason 174 | of your accepting any such warranty or additional liability. 175 | 176 | END OF TERMS AND CONDITIONS 177 | 178 | APPENDIX: How to apply the Apache License to your work. 179 | 180 | To apply the Apache License to your work, attach the following 181 | boilerplate notice, with the fields enclosed by brackets "[]" 182 | replaced with your own identifying information. (Don't include 183 | the brackets!) The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /config/manifests/bases/aikit-operator.clusterserviceversion.yaml: -------------------------------------------------------------------------------- 1 | apiVersion: operators.coreos.com/v1alpha1 2 | kind: ClusterServiceVersion 3 | metadata: 4 | annotations: 5 | capabilities: Basic Install 6 | categories: AI/Machine Learning, Big Data 7 | name: aikit-operator 8 | namespace: placeholder 9 | spec: 10 | apiservicedefinitions: {} 11 | customresourcedefinitions: 12 | owned: 13 | - description: AIKitContainer provides imagestream information 14 | displayName: AIKitContainer 15 | kind: AIKitContainer 16 | name: aikitcontainers.aikit.intel 17 | version: v1alpha1 18 | description: | 19 | AIKitContainer deploys an ImageStream and OdhDocuments to the cluster 20 | that add aikit notebook image and odh-dashboard cards. 21 | displayName: AIKitContainer 22 | icon: 23 | - base64data: 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 24 | mediatype: image/png 25 | install: 26 | spec: 27 | deployments: [] 28 | strategy: deployment 29 | installModes: 30 | - supported: false 31 | type: OwnNamespace 32 | - supported: false 33 | type: SingleNamespace 34 | - supported: false 35 | type: MultiNamespace 36 | - supported: true 37 | type: AllNamespaces 38 | keywords: 39 | - aikit 40 | - deeplearning 41 | - machinelearning 42 | links: 43 | - name: Aikit Operator 44 | url: https://aikit-operator.domain 45 | maturity: alpha 46 | minKubeVersion: 1.20.0 47 | provider: 48 | name: Intel 49 | version: 0.0.0 50 | 51 | --------------------------------------------------------------------------------