uav workshop: pre-workshop instructions

remove setup instructions for now

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

@@ -57,73 +57,4 @@ To help minimize technical issues and delays at the start of the workshop, pleas
   * 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.
+