Workstation docs update

docs/platforms/linux-workstation.md

@@ -4,7 +4,7 @@ hide:
 ---
 
 ### Introduction
-The Linux Workstation platform provides a flexible [Ubuntu](https://ubuntu.com/) 20.04 cloud instance, with web-browser access to the graphical desktop or shell. Optionally, users may add an external IP address, which allows the instance to be accessed from a machine outside of the project using [SSH](https://en.wikipedia.org/wiki/Secure_Shell).
+The Linux Workstation platform provides a flexible [Ubuntu](https://ubuntu.com/) 22.04 cloud instance, with web-browser access to the graphical desktop or shell. Optionally, users may add an external IP address, which allows the instance to be accessed from a machine outside of the project using [SSH](https://en.wikipedia.org/wiki/Secure_Shell).
 
 The workstation instance has a cloud volume (or virtual disk) available at `/data`, which may be useful for working with large datasets. The capacity of this cloud volume is configurable when launching the platform.
 
@@ -23,3 +23,89 @@ The workstation instance has a cloud volume (or virtual disk) available at `/dat
 ### Advanced
 #### Platform monitoring
 A [Grafana](https://grafana.com/oss/grafana/) dashboard for system monitoring is included in the platform, and is accessible from the platforms page. General current and historical system information is visible.
+
+#### EESSI
+The [EESSI](http://www.eessi.io/docs/) software suite is included in the Workstation appliance. EESSI includes a diverse collection of toolkits and modules for research computing purposes.
+
+A collection of EESSI demo environments are available in the [EESSI demo Github repository](https://github.com/EESSI/eessi-demo), which can be cloned using the command below:
+
+```git clone https://github.com/EESSI/eessi-demo.git```
+
+The TensorFlow demo in this repository is a illustrative example of how to make use of EESSI. To run this demo, it is first neccessary to source the EESSI bash environment using the following command:
+
+```source /cvmfs/pilot.eessi-hpc.org/versions/2021.12/init/bash```
+
+This script will initialize the Lua modules for the Software layer in the EESSI stack and source the neccessary environment variables. To get started with EESSI, the ``module avail`` command will list all modules avaliable to you in the environment. A successful initialization should result in an output resembling the one below.
+
+```
+[EESSI pilot 2021.12] $ module avail
+
+------ /cvmfs/pilot.eessi-hpc.org/versions/2021.12/software/linux/x86_64/intel/haswell/modules/all ------
+   ant/1.10.8-Java-11
+   Arrow/0.17.1-foss-2020a-Python-3.8.2
+   Bazel/3.6.0-GCCcore-9.3.0
+   ...
+```
+With a functioning environment, it is possible to begin experimenting with the software included in EESSI. Enter the directory of the EESSI-demo repository we cloned earlier, then enter the TensorFlow directory. 
+
+This TensorFlow project contains a demonstration 4-layer neural network model which runs against the MNIST digits dataset. It is possible to tinker with the ``TensorFlow-2.x_mnist-test.py`` script to setup a different model architecture or leave it in the default configuration. To initialise the model, execute the ``run.sh`` script inside the directory, which should produce an evaluation output like below.
+
+```
+[EESSI pilot 2021.12] $ ./run.sh 
+2023-10-09 13:15:04.546828: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
+Epoch 1/5
+1875/1875 [==============================] - 5s 3ms/step - loss: 0.2951 - accuracy: 0.9138
+Epoch 2/5
+1875/1875 [==============================] - 14s 7ms/step - loss: 0.1445 - accuracy: 0.9568
+Epoch 3/5
+1875/1875 [==============================] - 6s 3ms/step - loss: 0.1070 - accuracy: 0.9669
+Epoch 4/5
+1875/1875 [==============================] - 3s 2ms/step - loss: 0.0886 - accuracy: 0.9732
+Epoch 5/5
+1875/1875 [==============================] - 3s 2ms/step - loss: 0.0766 - accuracy: 0.9763
+313/313 - 0s - loss: 0.0745 - accuracy: 0.9774
+
+real    0m41.002s
+user    2m24.151s
+sys     1m54.599s
+```
+Guides on how to utilise EESSI further can be found [here](http://www.eessi.io/docs/using_eessi/eessi_demos/).
+
+
+#### Podman
+
+Podman is a container framework provided in the Workstation appliance for the purpose of building and running OCI containers. It is **strongly** recommended to install any software for the Workstation appliance via Podman, as software installed via the package manager or otherwise located outside of ``/home`` will be removed during image upgrades. In-place upgrades are not supported in the Workstation appliance to avoid dependancy issues between migrations, instead the ``/home`` directory is kept as a seperate partition and re-mounted after the Workstation has been re-imaged.
+
+The Podman CLI is syntatically similar to the Docker CLI. For example, a Jupyter notebook with an accessible web interface can be deployed using Podman via the following commands.
+
+To begin, clone the Jupyter notebook docker repository and navigate to the notebook directory as below:
+
+```git clone https://github.com/jupyter/docker-stacks.git && cd docker-stacks/images/base-notebook```
+
+Build the Jupyter notebook image, and select the docker.io remote when promoted by Podman:
+
+```podman build . --tag jupyter-notebook```
+
+Query the list of locally available images to ensure the Jupyter notebook container image was built correctly:
+
+```podman image ls | grep jupyter-notebook```
+
+Start the Jupyter notebook container:
+
+```podman run --name notebook -p 8888:8888 jupyter-notebook```
+
+Once the container has started, a link providing access to the web interface will be printed to the console. Navigating to this link in a web browser should present a Jupyter notebook web interface resembling the one shown below:
+
+![Jupyter notebook web interface](/docs/assets/images/jupyter-notebook-interface.png)
+
+As this is a base notebook it won't contain many applications to explore, more notebooks to build can be found in the [docker-stacks](https://github.com/jupyter/docker-stacks) repository and may have a similar setup procedure to the base notebook.
+
+See the [Podman docs](https://docs.podman.io/en/latest/) for further information on using Podman.
+
+#### Apptainer
+
+Apptainer is another container framework commonly used in HPC applications which is also included in the Workstation appliance. It can be used in a similar way to Podman as both frameworks support OCI containers. For example, OCI images from Docker Hub can be pulled with:
+
+```apptainer run docker://jupyter/base-notebook```
+
+Similiar to the previous Podman example, the Apptainer console log should contain an access link to the notebook interface.