Add jupyterhub readme and profiles info

jupyter-on-gke/README.md

@@ -121,3 +121,146 @@ To see monitoring metrics:
 1. Open Cloud Console and open Metrics Explorer
 2. In "Target", select "Prometheus Target" and then "Ray".
 3. Select the metric you want to view, and then click "Apply".
+
+# Jupyterhub Profiles
+
+## Default Profiles
+
+By default, there are 3 pre-set profiles for Jupyterhub:
+
+![Profiles Page](images/image.png)
+
+As the description for each profiles explains, each profiles uses a different resource.
+
+1. First profile uses CPUs and uses the image: `jupyter/tensorflow-notebook` with tag `python-3.10`
+
+2. Second profile uses 2 T4 GPUs and the image: `jupyter/tensorflow-notebook:python-3.10` with tag `python-3.10` [^1]
+
+3. Third profile uses 2 A100 GPUs and the image: `jupyter/tensorflow-notebook:python-3.10` with tag `python-3.10` [^1]
+
+## Editting profiles
+
+You can change the image used by these profiles, change the resources, and add specific hooks to the profiles
+
+Within the [`config.yaml`](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/ray-on-gke/user/jupyterhub/jupyter_config/config.yaml), the profiles sit under the `singleuser` key:
+
+``` yaml
+singleuser:
+    cpu:
+        ...
+    memory:
+        ...
+    image:
+        name: jupyter/tensorflow-notebook
+        tag: python-3.10
+    ...
+    profileList:
+    ...
+```
+
+### Image
+
+The default image used by all three of the profiles is `jupyter/tensorflow-notebook:python-3.10`
+
+1. For profile 1, it uses the: `default: true` field. This means that all the default configs under `singleuser` are used.
+
+2. For profile 2 and 3, the images are defined under `kubespawner_override`
+
+``` yaml
+    display_name: "Profile2 name"
+        description: "description here"
+        kubespawner_override:
+            image: jupyter/tensorflow-notebook:python-3.10
+    ...
+```
+
+Kubespanwer_override is a dictionary with overrides that gets applied through the Kubespawner. [^2]
+
+More images of tensorflow can be found [here](https://hub.docker.com/r/jupyter/tensorflow-notebook)
+
+### Resources
+
+Each of the users get a part of the memory and CPU and the resources are by default:
+
+``` yaml
+    cpu:
+        limit: .5
+        guarantee: .5
+    memory:
+        limit: 1G
+        guarantee: 1G
+```
+
+The *limit* if the reousrce sets a hard limit on how much of that resource can the user have.
+The *guarantee* meaning the least amount of resource that will be available to the user at all times.
+
+Similar to overriding images, the resources can also be overwritten by using `kubesparner_override`:
+
+``` yaml
+    kubespawner_override:
+        cpu_limit: .7
+        cpu_guarantee: .7
+        mem_limit: 2G
+        mem_guarantee: 2G
+        nvidia.com/gpu: "2"
+```
+
+### Node/GPU
+
+Jupyterhub config allows the use of [nodeSelector](https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector). This is the way the profiles specify which node/GPU it wants
+
+``` yaml
+nodeSelector:
+    iam.gke.io/gke-metadata-server-enabled: "true"
+    cloud.google.com/gke-accelerator: "nvidia-tesla-t4"
+```
+
+Override using `kubespwaner_override`:
+
+``` yaml
+    kubespawner_override:
+        node_selector:
+          cloud.google.com/gke-accelerator: "nvidia-tesla-a100"
+```
+
+The possible GPUs are:
+
+1. nvidia-tesla-k80
+2. nvidia-tesla-p100
+3. nvidia-tesla-p4
+4. nvidia-tesla-v100
+5. nvidia-tesla-t4
+6. nvidia-tesla-a100
+7. nvidia-a100-80gb
+8. nvidia-l4
+
+### Example profile
+
+Example of a profile that overrides the default values:
+
+``` yaml
+  - display_name: "Learning Data Science"
+    description: "Datascience Environment with Sample Notebooks"
+    kubespawner_override:
+        cpu_limit: .5
+        cpu_guarantee: .5
+        mem_limit: 1G
+        mem_guarantee: 1G
+    image: jupyter/datascience-notebook:2343e33dec46
+    lifecycle_hooks:
+        postStart:
+        exec:
+            command:
+            - "sh"
+            - "-c"
+            - >
+                gitpuller https://github.com/data-8/materials-fa17 master materials-fa;
+```
+
+### Additional Overrides
+
+With `kubespanwer_override` there are additional overrides that could be done, including `lifecycle_hooks`, `storage_capcity`, and `storage class`
+Fields can be found [here](https://jupyterhub-kubespawner.readthedocs.io/en/latest/spawner.html)
+
+[^1]: If using Standard clusters, the cluster must have at least 2 of the GPU type ready
+[^2]: More information on Kubespawner [here](https://github.com/jupyterhub/kubespawner/blob/main/kubespawner/spawner.py)