Merge pull request #17 from Sydney-Informatics-Hub/marimo

Add marimo tutorial

Author: jx-fan

_quarto.yml

@@ -39,8 +39,10 @@ website:
 
       - section: "Tutorials"
         contents:
-        - text: "Creating a basic Jupyter Lab workload"
+        - text: "Creating a Jupyter Lab workload"
           href: notebooks/jupyter_tutorial.html
+        - text: "Creating a Marimo Python workload"
+          href: notebooks/marimo_tutorial.html
         - text: "How to transfer data to/from the SIH GPU Cluster"
           href: notebooks/data_transfer.html
         - text: "How to run a terminal using the Command Line Interface (CLI)"

fig/workload_connect_marimo.png

@@ -0,0 +1,61 @@
+# Tutorial: Running a [Marimo Notebook](https://marimo.io) Workspace
+
+A workload is the actual job or task you want to run on the platform. This could be training an AI model, and running an inference model and exposing its endpoint, doing data preprocessing, or conducting a scientific simulation.
+
+Generally, the minimum requirements you need before creating the workload include:
+
+-   Being granted permission to an active project
+-   An [environment](environments.md) to run such job
+-   Have created a data source, e.g. a PVC, to store your input and output data
+-   Understand the compute resources you need to run the job and have the option available under "Compute Resources"
+
+In this tutorial, we will create a simple Marimo workload that allows you to run python Marimo notebooks interactively on the SIH GPU cluster.
+
+[Marimo notebook example gallery](https://marimo.io/gallery)
+
+## Step 1: Create a workload
+
+Navigate to the Workloads section of the platform and click on the "NEW WORKLOAD" button. Select "Workspace" from the dropdown menu.
+
+![New workload](../fig/workload_create_workspace.png)
+
+## Step 2: Configure the workload from scratch
+
+Define the necessary information for your workload:
+
+-   Under "Projects" select the project it will be linked to
+-   Under "Templates" select "Start from scratch" (*i.e.* do not use any existing template)
+-   Provide a descriptive name for the workload
+
+![Project and Template](../fig/workload_definition_marimo.png)
+
+-   Select an environment to create the container. The SIH team has set up the [marimo:latest container image](https://github.com/marimo-team/marimo/pkgs/container/marimo) (`ghcr.io/marimo-team/marimo:latest`) to run in [uv](https://docs.astral.sh/uv/) so that packages can be install automatically.
+
+![Software environment](../fig/workload_environment_marimo.png)
+
+-   Select the amount of compute resources to run the workload. In this tutorial, we will select the `small-fraction` option that requires 1 H200 GPU with 10% of its memory (\~14GB).
+
+![Compute resource](../fig/workload_comp_resource.png)
+
+-   Configure the [data source](./data_sources.md) to be mounted to the container. Here we select the default PVC created for the project. The mount path inside the container is set to `/scratch/<dashr_project_shortcode>`.
+
+![Data resource](../fig/workload_datasource_marimo.png)
+
+-   Lastly, Click on "CREATE WORKLOAD" to submit the workload to the cluster.
+
+## Step 3: Connect to Marimo
+
+When the status changes to "Running", you can access the Marimo interface by selecting "Marimo" under "CONNECT".
+
+![Connect to the Marimo interface](../fig/workload_connect_marimo.png)
+
+## Step 4. Create New Notebook in Marimo
+
+You're ready to go!
+![Connect to the Marimo interface](../fig/workload_marimo_create_notebook.png)
+
+## (Optional) Step 5: Inspect system logs
+
+You can review the system logs to access details about event history, workload metrics, and real-time container output. This information is especially useful for debugging issues when a workload fails to start.
+
+![Workload logs](../fig/workload_logs.png)