Offer image choices for the JMTE hub

I think overall, we want to reduce the number of images we maintain
for our end users. A big part of this to use upstream images directly
wherever possible, and allow users to choose. This helps us benefit
from upstream fixes as quickly as possible, and reduces the total
amount of work done. For example, instead of specifically bumping
the version of Julia just for this one
image (pangeo-data/jupyter-earth#166),
we could instead do that upstream and benefit
everyone (jupyter/docker-stacks#1917).

Faster startup times is another benefit, as the more specific
images are smaller than a big 'all-in-one' image.

There are some features of the all-in-one image that currently don't
easily exist upstream:

- Linux desktop
- Nix
- Specific extra packages that maybe installed

We can figure these out over time, but not maintaining the all-in-one
image is a nice goal to shoot for. To this end, the JMTE image is
still the default, but marked as 'deprecated' as I don't want to
continue doing a lot of maintenance on it.

Ref 2i2c-org#2201

config/clusters/jupyter-meets-the-earth/common.values.yaml

@@ -126,8 +126,34 @@ basehub:
             mem_limit: null
             node_selector:
               node.kubernetes.io/instance-type: m5.xlarge
+          profile_options: &profile_options
+            image: 
+              display_name: Image
+              choices:
+                jmte:
+                  display_name: JMTE all-in-one image (deprecated)
+                  default: true
+                  slug: "jmte"
+                  kubespawner_override:
+                    image: "286354552638.dkr.ecr.us-west-2.amazonaws.com/jmte/user-env:9baee2d"
+                tensorflow:
+                  display_name: Pangeo Tensorflow ML Notebook
+                  slug: "tensorflow"
+                  kubespawner_override:
+                    image: "quay.io/pangeo/ml-notebook:2023.05.18"
+                pytorch:
+                  display_name: Pangeo PyTorch ML Notebook
+                  slug: "pytorch"
+                  kubespawner_override:
+                    image: "quay.io/pangeo/pytorch-notebook:2023.05.18"
+                datascience:
+                  display_name: Jupyter DockerStacks DataScience Image (Python, Julia, R)
+                  slug: "datascience"
+                  kubespawner_override:
+                    image: "jupyter/datascience-notebook:2023-06-19"
         - display_name: "4th of Medium: 1-4 CPU, 4-16 GB"
           description: "A shared machine."
+          profile_options: *profile_options
           kubespawner_override:
             cpu_guarantee: 0.875
             mem_guarantee: 3.5G
@@ -136,6 +162,7 @@ basehub:
               node.kubernetes.io/instance-type: m5.xlarge
         - display_name: "Medium: 4 CPU, 16 GB"
           description: "A dedicated machine for you."
+          profile_options: *profile_options
           kubespawner_override:
             cpu_guarantee: 3.5
             mem_guarantee: 14G
@@ -144,27 +171,31 @@ basehub:
               node.kubernetes.io/instance-type: m5.xlarge
         - display_name: "Large: 16 CPU, 64 GB"
           description: "A dedicated machine for you."
+          profile_options: *profile_options
           kubespawner_override:
             mem_guarantee: 56G
             mem_limit: null
             node_selector:
               node.kubernetes.io/instance-type: m5.4xlarge
         - display_name: "Massive: 64 CPU, 256 GB"
           description: "A dedicated machine for you."
+          profile_options: *profile_options
           kubespawner_override:
             mem_guarantee: 224G
             mem_limit: null
             node_selector:
               node.kubernetes.io/instance-type: m5.16xlarge
         - display_name: "Massive high-memory: 64 CPU, 976 GB"
           description: "A dedicated machine for you."
+          profile_options: *profile_options
           kubespawner_override:
             mem_guarantee: 900G
             mem_limit: null
             node_selector:
               node.kubernetes.io/instance-type: x1.16xlarge
         - display_name: "Medium GPU: 4 CPU, 16 GB, 1 T4 Tensor Core GPU"
           description: "A dedicated machine for you with one GPU attached."
+          profile_options: *profile_options
           kubespawner_override:
             cpu_guarantee: 3.5
             mem_guarantee: 14G
@@ -175,6 +206,7 @@ basehub:
               nvidia.com/gpu: "1"
         - display_name: "Large GPU: 16 CPU, 64 GB, 1 T4 Tensor Core GPU"
           description: "A dedicated machine for you with one GPU attached."
+          profile_options: *profile_options
           kubespawner_override:
             mem_guarantee: 56G
             mem_limit: null
@@ -184,6 +216,7 @@ basehub:
               nvidia.com/gpu: "1"
         - display_name: "Massive GPU: 64 CPU, 256 GB, 1 T4 Tensor Core GPU"
           description: "A dedicated machine for you with one GPU attached."
+          profile_options: *profile_options
           kubespawner_override:
             mem_guarantee: 200G
             mem_limit: null
@@ -193,6 +226,7 @@ basehub:
               nvidia.com/gpu: "1"
         - display_name: "16th of Medium: 0.25-4 CPU, 1-16 GB - Test of latest image"
           description: "Helps us test an image before we make it the default"
+          profile_options: *profile_options
           kubespawner_override:
             image: 286354552638.dkr.ecr.us-west-2.amazonaws.com/jmte/user-env:latest
             image_pull_policy: Always