UAV workshop: first draft of instructions

sn_collections/_events/2024-10-09-Multispectral-UAV-workshop.md

@@ -33,4 +33,97 @@ This workshop will focus on processing multispectral imagery from unoccupied aer
 
 To register for the Multispectral UAV Imagery Workshop, [please fill out this form](https://forms.office.com/g/jQQN0iyHHT). 
 
+**Leads:**  
+  * Alexander Hernandez, Research Biologist (Computational), Forage and Range Research Unit
+  * Efrain Duarte, SCINet/AI-COE Postdoctoral Fellow, Forage and Range Research Unit
+  * Kaden Patten, Biological Science Technician, Forage and Range Research Unit
 
+-----
+
+
+## Pre-Workshop Instructions: 
+
+To help minimize technical issues and delays at the start of the workshop, please try the following three tests prior to the workshop. 
+
+* **Logging on to [Atlas Open OnDemand (OOD)](https://atlas-ood.hpc.msstate.edu/):** Please confirm you can successfully log in to Atlas OOD with your SCINet account [(see instructions here)]({{site.baseurl}}/guides/access/web-based-login). If you are successful, you will be able to see the Atlas OOD home page.
+* **Atlas Shell Access:** When on Atlas OOD, click on the top navigation bar: “Clusters” > “Atlas Shell Access”. A new tab will appear that looks like a shell terminal (e.g., like PowerShell). Please confirm you do not receive any error messages or requests to re-authenticate and that the final line looks like "[firstname.lastname@atlas-login-1 ~]$". 
+* **JupyterLab Server:** Back on the main Atlas OOD tab, click on the top or side navigation bar: "Interactive Apps" > "JupyterLab Server".  
+  * Fill the input fields with the following (input fields not listed below can be left at their default values):  
+    * Partition: atlas
+    * Number of hours: 1
+    * Number of nodes: 1
+    * Number of tasks: 1
+    * Additional Slurm Parameters: (leave empty)
+  * Click the "Launch" button. 
+  * Wait a moment for the job card to update from "Queued" to "Running". 
+  * Please confirm that clicking on the "Connect to JupyterLab Server" button opens a new tab with the JupyterLab Server interface. 
+
+
+-----
+
+## Tutorial Setup Instructions
+
+Steps to prepare for the tutorial session each day:
+
+1. **Login to Atlas Open OnDemand** at [https://atlas-ood.hpc.msstate.edu/](https://atlas-ood.hpc.msstate.edu/). For more information on login procedures for web-based SCINet access, see the [SCINet access user guide]({{site.baseurl}}/guides/access/web-based-login).
+
+1. **Open a command-line session** by clicking on “Clusters” -> “Atlas Shell Access” on the top menu. This will open a new tab with a command-line session on Atlas' login node.
+
+1. **Request resources on a compute node** to avoid using the login node for data transfers by running the following command. 
+
+    {:.copy-code}
+    ```bash
+    srun --reservation=workshop2 -A scinet_workshop2 -t 00:30:00 -n 1 --mem 8G --pty bash
+    ```
+
+1. **Create a workshop working directory** and copy the workshop materials into it by running the following commands. Note: you do not have to edit the commands with your username as it will be determined by the `$USER` variable.
+
+    {:.copy-code}
+    ```bash
+    mkdir -p /90daydata/shared/$USER/
+    cd /90daydata/shared/$USER/
+    cp -r /project/scinet_workshop2/multispectral_UAV/detect_GCPs .
+    cp -r /project/scinet_workshop2/multispectral_UAV/flight_filtering .
+    cp -r /project/scinet_workshop2/multispectral_UAV/[EXAMPLE DATA?] .
+    ```
+
+1. **Setup kernel for JupyterLab.** In the workshop project space, there is a `geospatial_conda` conda environment for the packages we will be using during the workshop tutorials. You will create a kernel called *uav_workshop* to access from JupyterLab. 
+
+    First load the necessary module:
+
+    {:.copy-code}
+    ```bash
+    module load miniconda3
+    ```
+
+    If you have not used conda on Atlas before, you will first need to run these commands:
+
+    {:.copy-code}
+    ```bash
+    conda init
+    conda config --set auto_activate_base false
+    source ~/.bashrc
+    ```
+
+    For everyone, you will create a new kernelspec from the conda environment with these commands:
+
+    {:.copy-code}
+    ```bash
+    conda activate /project/scinet_workshop2/multispectral_UAV/geospatial_conda
+    ipython kernel install --name "uav_workshop" --user
+    ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+    * Account: scinet_workshop2
+    * Partition: atlas
+    * QOS: normal 14-00:00:00
+    * Number of hours: 4
+    * Number of nodes: 1
+    * Number of tasks: ??
+    * Additional Slurm Parameters: \-\-mem=??G \-\-reservation=workshop2
+    * Home Directory: /90daydata/shared/${USER}
+
+    Click *Launch*. The screen will update to the *Interactive Sessions* page. When your Jupyter session is ready, the top card will update from *Queued* to *Running* and a *Connect to Jupyter* button will appear. Click *Connect to Jupyter*.
+
+1. For each notebook used in the tutorial, select the *uav_workshop* kernel.