Merge pull request #775 from HeatherSavoy-USDA/computer_vision

Computer vision workshop instructions

sn_collections/_workshops/2024-ai-user-forum/21-computer-vision-2.md

@@ -16,3 +16,71 @@ prerequisites:
 In this workshop, participants will learn the key concepts and techniques needed to use modern, deep learning-based computer vision methods for object detection and semantic segmentation. Learners will practice training and evaluating state-of-the-art computer vision models on custom image datasets. 
 
 This workshop is intended as a continuation of “Computer vision I: introduction and image classification”, but participants do not need to take the earlier workshop if they already have a basic knowledge of machine learning and computer vision concepts.<!--excerpt--> 
+
+
+## Tutorial setup instruction
+
+Steps to prepare for the tutorial:
+
+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's login node.
+
+1. **Request resources on a compute node** by running the following command: 
+
+    {:.copy-code}
+    ```bash
+    srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash 
+    ```
+
+1. **Create and/or update your workshop working directory** and copy the tutorial 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/computer_vision2
+    cd /90daydata/shared/$USER/computer_vision2
+    cp -r /project/ai_forum/computer_vision2/computer_vision_2.ipynb .
+    cp -r /project/ai_forum/computer_vision2/*.py .
+    ```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *computer_vision_2_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+    {:.copy-code}
+    ```bash
+    source /project/ai_forum/computer_vision2/computer_vision_2_env/bin/activate
+    ipython kernel install --name "computer_vision_2_env" --user
+    ```
+
+1. **Stop the interactive job** on the compute node by running the command:
+
+    {:.copy-code}
+    ```bash
+    exit
+    ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+    * Account: scinet_workshop1
+    * Partition: gpu-a100-mig7
+    * QOS: normal 14-00:00:00
+    * Number of hours: 4
+    * Number of nodes: 1
+    * Number of tasks: 2
+    * Additional Slurm Parameters: 
+        
+        {: .copy-code }
+        ```
+--reservation=forum-gpu --gres=gpu:1 --mem=32G --ntasks-per-node=2
+```
+    * Working Directory: 
+        
+        {: .copy-code }
+        ```
+/90daydata/shared/${USER}/computer_vision2
+```
+  
+    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 JupyterLab Server* button will appear. Click *Connect to JupyterLab Server*.
+
+1. **Open the `computer_vision_2.ipynb` notebook.**
+  
+1. **Select the `computer_vision_2_env` kernel** for the notebook.

sn_collections/_workshops/2024-ai-user-forum/21-computer-vision.md

@@ -14,3 +14,70 @@ prerequisites:
 ---
 
 This workshop will teach participants the concepts and tools they need to begin applying modern, deep learning-based computer vision methods to their own scientific research. This will be an interactive, hands-on workshop that will offer plenty of opportunities for practice and experiential learning. By the end of the session, participants will have trained and evaluated a state-of-the-art image classification model on a custom image dataset.<!--excerpt--> 
+
+## Tutorial setup instruction
+
+Steps to prepare for the tutorial:
+
+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's login node.
+
+1. **Request resources on a compute node** by running the following command: 
+
+    {:.copy-code}
+    ```bash
+    srun --reservation=forum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash 
+    ```
+
+1. **Create and/or update your workshop working directory** and copy the tutorial 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/computer_vision1
+    cd /90daydata/shared/$USER/computer_vision1
+    cp -r /project/ai_forum/computer_vision1/computer_vision_1.ipynb .
+    cp -r /project/ai_forum/computer_vision1/*.py .
+    ```
+
+1. **Setup the kernel for JupyterLab.** You will create a kernel called *computer_vision_1_env* to access from JupyterLab Server. Run the following commands to activate the workshop's virtual environment and create a new kernelspec from it:
+
+    {:.copy-code}
+    ```bash
+    source /project/ai_forum/computer_vision1/computer_vision_1_env/bin/activate
+    ipython kernel install --name "computer_vision_1_env" --user
+    ```
+
+1. **Stop the interactive job** on the compute node by running the command:
+
+    {:.copy-code}
+    ```bash
+    exit
+    ```
+
+1. **Launch a JupyterLab Server session.** Under the *Interactive Apps* menu, select *JupyterLab Server*. Specify the following input values on the page:
+
+    * Account: scinet_workshop1
+    * Partition: gpu-a100-mig7
+    * QOS: normal 14-00:00:00
+    * Number of hours: 4
+    * Number of nodes: 1
+    * Number of tasks: 2
+    * Additional Slurm Parameters: 
+        
+        {: .copy-code }
+        ```
+--reservation=forum-gpu --gres=gpu:1 --mem=32G --ntasks-per-node=2
+```
+    * Working Directory: 
+        
+        {: .copy-code }
+        ```
+/90daydata/shared/${USER}/computer_vision1
+```
+  
+    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 JupyterLab Server* button will appear. Click *Connect to JupyterLab Server*.
+
+1. **Open the `computer_vision_1.ipynb` notebook.**
+  
+1. **Select the `computer_vision_1_env` kernel** for the notebook.