Create a Docker image to run notebooks

Currently we use `tensorflow/tensorflow:latest` or (`tensorflow/tensorflow:2.7.0` and `tensorflow/tensorflow:2.3.0`) to run our notebooks inside a Kubeflow Pipeline.

However, that image is very large and laden with many dependencies, many of which are not required for the respective notebooks. Due to those many dependencies, `apk` packages, binaries, and Python packages, the Docker image frequently fails to install the required notebook dependencies on top of it. At the moment only 4 of our 8 sample notebooks can run.

We need to find a basic Python Docker image and install only the necessary requirements like [`papermill`](https://github.com/nteract/papermill).

To show (some of) the steps required to run a notebook on Kubernetes, take a look at this script from the `katalog` repo runs notebooks outside of a cluster:

https://github.com/machine-learning-exchange/katalog/blob/7fcd5ce/tools/bash/run_notebooks.sh#L58-L65

```Bash
# TODO: find a smaller Docker image
IMAGE="tensorflow/tensorflow:latest"

docker run -i --rm --entrypoint "" "${IMAGE}" bash -c "
# download the notebook
wget -q -O notebook_in.ipynb '${NOTEBOOK_URL}' 2> /dev/null || curl -s -o notebook_in.ipynb '${NOTEBOOK_URL}'

# update pip
python3 -m pip install pip --upgrade --quiet --progress-bar=ascii

# install Elyra requirements, may not all be required beyond "papermill"
python3 -m pip install -r https://raw.githubusercontent.com/elyra-ai/elyra/master/etc/generic/requirements-elyra.txt --quiet --progress-bar on

# if the notebook has requirements, install those
[[ -n '${REQUIREMENTS}' ]] && python3 -m pip install ${REQUIREMENTS} --quiet --progress-bar=on

# show the installed package
python3 -m pip list

# run the notebook with papermill
papermill --log-level CRITICAL --report-mode notebook_in.ipynb notebook_out.ipynb
" >> "${LOG_FILE}" 2>&1 && echo OK || echo FAILED
```

**Some Considerations:**

- If we use a generic Docker image like `python:3.9` then the pip install steps for the `elyra-ai` requirements have to be repeated every time a notebook is run
- If we create a custom notebook image, or maybe several most of the pip install steps are done at the time the Docker image is built, speeding up actual notebook execution
- Although the Docker image will be bigger this way, once it has been pulled onto the cluster, it should get cached.
- The same is not true for previously downloaded Python packages inside the container running the notebook.
- And generally the increased time for downloading a bigger Docker image is a fraction of the increased time required to download pip packages and the time pip needs on top of that to resolve potential version conflicts.
- We could use several specialized images for notebooks that have similar dependencies:
- ART+AIF360
- CodeNet
- Quantum/Qiskit

**Additional Information:**

Also see this notebook runner component with sample pipeline in KFP:
- [https://github.com/kubeflow/pipelines/[…]/contrib/notebooks/Run_notebook_using_papermill/component.yaml](https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/components/contrib/notebooks/Run_notebook_using_papermill/component.yaml)
- [https://github.com/kubeflow/pipelines/[...]/components/contrib/notebooks/samples/sample_pipeline.py](https://github.com/kubeflow/pipelines/blob/74c7773ca40decfd0d4ed40dc93a6af591bbc190/components/contrib/notebooks/samples/sample_pipeline.py)