Merge pull request #115 from GoogleCloudPlatform/develop

Change pantheon to cloud console and increase Jupyter mem/cpu

jupyter-on-gke/README.md

@@ -52,7 +52,7 @@ If not, set `enable_create_namespace` to `true` so a new k8s namespace is create
 
     **Note:**
     We allow user to set their own domains, in the `variables.tf` file. Since we are also using an Ingress Object, it is required for the Ingress to also have specifiy the name of the global static address.
-    Visit the [GCloud UI](https://pantheon.corp.google.com/security/iap) to see if it's enabled. If it's enabled, you will be able to see the page and will not prompt you to enable API:
+    Visit the [Cloud Console](https://console.cloud.google.com/security/iap) to see if it's enabled. If it's enabled, you will be able to see the page and will not prompt you to enable API:
 
     ![IAP API Screen](./images/iap_enable_api_screenshot.png)
 
@@ -106,7 +106,7 @@ To secure the Jupyter endpoint, this example enables IAP by default. It is _stro
 
 12. Re-run `terraform apply`
 
-13. Navigate to the [GCP IAP Cloud Console](https://pantheon.corp.google.com/security/iap) and select your backend-service checkbox.
+13. Navigate to the [GCP IAP Cloud Console](https://console.cloud.google.com/security/iap) and select your backend-service checkbox.
 
 14. Click on `Add Principal`, insert the new principle and select under `Cloud IAP` with role `IAP-secured Web App User`
 

jupyter-on-gke/authentication/README.MD

@@ -117,7 +117,7 @@ By default, the IAP service is not enabled on the project. If this variable is s
 
 ### `brand` and `support_email`
 
-To create an OAuth client, a brand is required. The brand is essentially the app show on the [OAuth consent screen](https://pantheon.corp.google.com/apis/credentials/consent). Ex:
+To create an OAuth client, a brand is required. The brand is essentially the app show on the [OAuth consent screen](https://console.cloud.google.com/apis/credentials/consent). Ex:
 
 ![IAP Screen](../images/brand_screenshot.png)
 
@@ -127,12 +127,12 @@ The `support_email` variable will not be used if the `brand` variable is not fil
 
 There are 2 ways to add users to the IAP allowlist so users can access Jupyterhub.
 
-1. Manually on the GCloud UI
+1. Manually on the Cloud Console
 2. Add users to the allowlist file
 
 ### Manually
 
-Navigate to [IAP](https://pantheon.corp.google.com/security/iap) and select the Backend Service associated with Jupyterhub.
+Navigate to [IAP](https://console.cloud.google.com/security/iap) and select the Backend Service associated with Jupyterhub.
 
 Click on `Add Principal`. Add the email of the user/group and select the `IAP-secured Web App User` role to give access to that user/group
 

jupyter-on-gke/jupyter_config/config-selfauth.yaml

@@ -66,11 +66,16 @@ scheduling:
 
 singleuser:
   memory:
-    limit: 1G
-    guarantee: 1G
+    limit: 32G
+    guarantee: 8G
   cpu:
-    limit: .5
-    guarantee: .5
+    limit: 8
+    guarantee: 4
+  extraResource:
+    limits:
+      ephemeral-storage: 20Gi
+    guarantees:
+      ephemeral-storage: 10Gi
   nodeSelector:
     iam.gke.io/gke-metadata-server-enabled: "true"
   image:

jupyter-on-gke/variable_definitions.md

@@ -55,7 +55,7 @@ Uses [support_email](#support_email)
 
 ### support_email
 
-Support email assocated with the [brand](#brand). Used as a point of contact for consent for the ["OAuth Consent" in Pantheon](https://pantheon.corp.google.com/apis/credentials/consent). It will not be used if brand is empty.
+Support email assocated with the [brand](#brand). Used as a point of contact for consent for the ["OAuth Consent" in Cloud Console](https://console.cloud.google.com/apis/credentials/consent). It will not be used if brand is empty.
 
 ### url_domain_addr
 

ray-on-gke/raytrain-examples/raytrain-with-gcsfusecsi/README.md

@@ -98,5 +98,5 @@ http://tensorflow-0:8888/?token=<TOKEN> :: /home/jovyan
 ```
 kubectl port-forward -n example service/example-cluster-kuberay-head-svc 8265:8265
 ```
-14. During an ongoing traing, the pod resource usage of CPU, Memory, GPU, GPU Memory can be visualized with the GKE Pantheon UI for the workloads
+14. During an ongoing traing, the pod resource usage of CPU, Memory, GPU, GPU Memory can be visualized with the GKE Cloud Console for the workloads
 example ![Ray Head resources](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/ray-on-gke/raytrain-examples/images/ray-head-resources.png) and ![Ray Worker resources](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/ray-on-gke/raytrain-examples/images/ray-worker-resources.png)