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Introduction to Jupyter Notebooks and Python

In this workshop, which assumes no prior coding experience, you will begin to learn the tools and techniques that are fundamental for building, testing, and deploying AI models in Jupyter Notebooks with Python. Participants will explore Jupyter Notebooks as an interactive platform for coding and data analysis. The workshop will introduce participants to Python, including popular data exploration and visualization libraries such as pandas and plotnine.

Learning Objectives:

By the end of this workshop, participants will be able to:

  • Use Jupyter Notebooks for coding Python.
  • Understand the basics of Python code.
  • Begin troubleshooting common coding errors.
  • Practice data manipulation and visualization using Python libraries.

Tutorial setup instruction

Steps to prepare for the tutorial:

  1. Login to Atlas Open OnDemand at https://atlas-ood.hpc.msstate.edu/. For more information on login procedures for web-based SCINet access, see the SCINet access user guide.

  2. 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.

  3. Request resources on a compute node by running the following command:

     srun --reservation=preforum -A scinet_workshop1 -t 00:30:00 -n 1 --mem 8G --pty bash

  4. 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.

     mkdir -p /90daydata/shared/$USER/intro_python
 cd /90daydata/shared/$USER/intro_python
 cp -r /project/ai_forum/intro_python/python-crash-course-ars/* .

  5. Setup the kernel for JupyterLab. You will create a kernel called intro_python_env to access from JupyterLab Server. Run the following commands to activate the workshop’s virtual environment and create a new kernelspec from it:

     source /project/ai_forum/intro_python/intro_python_env/bin/activate
 ipython kernel install --name "intro_python_env" --user

  6. Stop the interactive job on the compute node by running the command:

     exit

  7. 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: atlas
    • QOS: normal 14-00:00:00
    • Number of hours: 4
    • Number of nodes: 1
    • Number of tasks: 1
    • Additional Slurm Parameters: --reservation=preforum --mem=16G
    • Working 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 JupyterLab Server button will appear. Click Connect to JupyterLab Server.

  8. Select the intro_python_env kernel for each notebook.