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Update Documentation for Release
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# Change Log
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# Change Log
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## [master](https://github.com/arangodb/kube-arangodb/tree/master) (N/A)
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## [master](https://github.com/arangodb/kube-arangodb/tree/master) (N/A)
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## [1.2.41](https://github.com/arangodb/kube-arangodb/tree/1.2.41) (2024-05-24)
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- (Maintenance) Bump Prometheus API Version
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- (Maintenance) Bump Prometheus API Version
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- (Bugfix) Prevent unexpected rotation in case of SecurityContext change
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- (Bugfix) Prevent unexpected rotation in case of SecurityContext change
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- (Bugfix) Ensure PDB is created
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- (Bugfix) Ensure PDB is created
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12
docs/gae-resource.md
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docs/gae-resource.md
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---
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layout: page
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parent: Custom resources overview
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title: GraphAnalyticsEngine
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---
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# GraphAnalyticsEngine Custom Resource
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#### Enterprise Edition only
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[Full CustomResourceDefinition reference ->](./api/GraphAnalyticsEngine.V1Alpha1.md)
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#### Enterprise Edition only
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#### Enterprise Edition only
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[Full CustomResourceDefinition reference ->](./api/ArangoMLExtension.V1Alpha1.md)
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[Full CustomResourceDefinition reference ->](./api/ArangoMLExtension.v1beta1.md)
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You can spin up the [ArangoML](https://github.com/arangoml) engine on existing ArangoDeployment.
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You can spin up the [ArangoML](https://github.com/arangoml) engine on existing ArangoDeployment.
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- Create Kubernetes Secret containing CA certificates to validate connection to endpoint if your Minio installation uses encrypted connection. The secret data should contain two fields: `ca.crt` and `ca.key` (both PEM-encoded).
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- Create Kubernetes Secret containing CA certificates to validate connection to endpoint if your Minio installation uses encrypted connection. The secret data should contain two fields: `ca.crt` and `ca.key` (both PEM-encoded).
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- Create ArangoMLStorage resource. Example:
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- Create ArangoMLStorage resource. Example:
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```yaml
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```yaml
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apiVersion: ml.arangodb.com/v1alpha1
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apiVersion: ml.arangodb.com/v1beta1
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kind: ArangoMLStorage
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kind: ArangoMLStorage
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metadata:
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metadata:
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name: myarangoml-storage
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name: myarangoml-storage
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3) Create `ArangoMLExtension` CR. The name of extension **must** be the same as the name of `ArangoDeployment` and it should be created in the same namespace.
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3) Create `ArangoMLExtension` CR. The name of extension **must** be the same as the name of `ArangoDeployment` and it should be created in the same namespace.
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Assuming you have ArangoDeployment with name `myarangodb`, create CR:
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Assuming you have ArangoDeployment with name `myarangodb`, create CR:
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```yaml
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```yaml
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apiVersion: ml.arangodb.com/v1alpha1
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apiVersion: ml.arangodb.com/v1beta1
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kind: ArangoMLExtension
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kind: ArangoMLExtension
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metadata:
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metadata:
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name: myarangodb
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name: myarangodb
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name: myarangoml-storage # name of the ArangoMLStorage created on the previous step
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name: myarangoml-storage # name of the ArangoMLStorage created on the previous step
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deployment:
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deployment:
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# you can add here: tolerations, nodeSelector, nodeAffinity, scheduler and many other parameters. See full CRD reference for details.
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# you can add here: tolerations, nodeSelector, nodeAffinity, scheduler and many other parameters. See full CRD reference for details.
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replicas: 1 # by default only one pod is running which contains containers for each component (prediction, training, project). You can scale it up or down.
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replicas: 1 # by default only one pod is running which contains containers for api. You can scale it up or down.
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prediction:
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image: <api-image>
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image: <prediction-image>
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# you can configure various parameters for container running this component here. See full CRD reference for details.
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project:
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image: <projects-image>
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# you can configure various parameters for container running this component here. See full CRD reference for details.
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training:
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image: <training-image>
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# you can configure various parameters for container running this component here. See full CRD reference for details.
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# you can configure various parameters for container running this component here. See full CRD reference for details.
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init: # configuration for Kubernetes Job running initial bootstrap of ArangoML for your cluster.
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init: # configuration for Kubernetes Job running initial bootstrap of ArangoML for your cluster.
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image: <init-image>
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image: <init-image>
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