Enable new SD agents by default #1252
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| {% if properties['stackdriver-kubernetes'] %} | ||
| # TODO: remove alpha when 10.2 is public. | ||
| # https://github.com/kubeflow/kubeflow/issues/821 | ||
| enableKubernetesAlpha: true | ||
| # We need 1.10.2 to support Stackdrivier GKE. | ||
| initialClusterVersion: 1.10.2-gke.0 |
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Now most recent version is 1.10.5-gke.3, and 1.10.2-gke.0 is no long available from UI.
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/retest |
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/lgtm |
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/retest |
1 similar comment
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/retest |
scripts/gke/deploy.sh
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| @@ -159,6 +159,10 @@ if ${KUBEFLOW_DEPLOY}; then | |||
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| # Install the GPU driver. It has no effect on non-GPU nodes. | |||
| kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/stable/nvidia-driver-installer/cos/daemonset-preloaded.yaml | |||
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| # Install Stackdriver Kubernetes agents. | |||
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Why do we need to do this? Aren't the stackdriver agents installed by default?
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The beta instructions don't mention installing these agents
https://cloud.google.com/monitoring/kubernetes-engine/installing?
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The instruction there are using gcloud or pantheon. We are doing manually here.
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Should we take advantage of the YAML support in ksonnet and start a package for all the YAML specs for GKE?
We could do this in a follow on a PR. I think the question is how can we make it easier to keep in sync between our web app and deploy.sh
Right now we'd have to specify the YAML spec in two different locations.
| initialClusterVersion: 1.9.6-gke.1 | ||
| {% endif %} | ||
| initialClusterVersion: 1.10.5-gke.3 | ||
| loggingService: none |
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Why don't we need to set loggingService and monitoringService anymore?
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Those are legacy ones.
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/hold |
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/lgtm |
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/hold cancel |
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/lgtm |
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[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: jlewi, kunmingg The full list of commands accepted by this bot can be found here. The pull request process is described here
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* Enable new SD agents by default * fix
* [python sdk] add v1beta1 models * upgrade version of python SDK to 0.0.3 * remove v1Alpha3 python sdk * add some python models manually: v1Time and V1UnstructuredUnstructured * bring back v1alpha3 * create separate python sdk for v1alpha3 and v1beta1 * move on * release pkg on pypi.org * remove dist files * refine
* Related to kubeflow#1252
Now that we have k8s 1.10.
/cc @kunmingg
This change is