The following provides the the template for building a Jupyter Workbench Runtime images to use with Open Data Hub ODH or Red Hat OpenShift Data Science RHODS or podman / Docker Locally.
It loads the Openad lookit and a minimal image of the UBI9 jupyter notebooks with prebuilt note books for using OpenAD.

To install podman see install instructions

To Install Docker Engine see Install Docker Engine

First of all make sure if you are using podman, you have at least 4GB of memory available to podman e.g.

podman stop
podman machine set --cpus 4 --memory 4096
podman start

To build your image and deploy on podman/docker run
podman/docker build -t openad_workbench https://github.com/acceleratedscience/openad_workbench.git#main

To run your workbench
podman/docker run -d -p 8888:8888 -p 8024-8200:8024-8200 --name my_workbench openad_workbench

To stop your workbench:
podman/docker stop my_workbench

To restart your workbench:
podman/docker stop my_workbench

To delete your workbench container you will need to stop it then run the following:
podman/docker rm my_workbench

Once the Pod is Started use the url in your browser 127.0.0.1:8888 to enter your jupyter lab

The following instruction provides an alternative to allow you to use your desktops existing workspaces as well as use exisint RXN, Deep Search and LLM credentials. podman/docker run -d -v $HOME/.openad:/opt/app-root/src/.openad:Z -p 8888:8888 -p 8024-8200:8024-8200 --name my_workbench openad_workbench

If you wish to create secrets to pre-load models and other credentials create 2 json files

First called openad_creds.json

{
    "bam": {
        "auth": {
            "api_key": "<api_key>"
        }
    },
    "rxn": {
        "auth": {
            "api_key": "<api_key>"
        }
    },
    "deepsearch": {
        "auth": {
            "username": "jon.doe@email",
            "api_key": "<api_key>"
        }
    }
}

The seconds called opend_models.json like the following example "

"auth_groups": {
        "default": "<API_key/Bearer token>"
    },
    "services": {
        "prop": {
            "host": "https://<url>:8080/proxy",
            "inference-service": "properties",
            "auth_group": "default"
        },
        "gen": {
            "host": "https://<url>:8080/proxy",
            "inference-service": "generation",
            "auth_group": "default"
        },
        "moler": {
            "host":"https://<url>:8080/proxy",
            "inference-service": "moler",
            "auth_group": "default"
        },
        "molf": {
            "host": "https://<url>>:8080/proxy",
            "inference-service": "molformer",
            "auth_group": "default"
        }
    }
 }

Then run podman secret create openad_creds openad_creds.json
and
podman secret create openad_models openad_models.json

Then run the following to run the container and access you notebooks podman run -d --secret openad_creds --secret openad_models -p 8888:8888 -p 8024-8200:8024-8200 --name my_workbench openad_workbench

Before you can interact with the toolkits, you'll need to register with each individual toolkit.