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An introduction to machine learning for science

    • Wednesday, November 20, 2024, 1-4 pm CT
      • Lead: ARS SCINet Office
      • Prerequisites:
        • Familiarity with basic Python concepts and Jupyter notebooks. We will offer virtual training for these skills before the Forum begins.

Machine learning underlies the vast majority of modern AI methods, including the ever-expanding applications of deep learning and generative AI. This workshop will give participants a hands-on introduction to the basic concepts and techniques needed to understand machine learning and to apply machine learning methods to scientific research.

Participants will learn how to train, evaluate, and use a variety of machine learning models for data analysis tasks. This session will also help participants critically evaluate the use and application of machine learning in science.

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=forum -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/$USER/intro_ml
cd /90daydata/shared/$USER/intro_ml
cp -r /project/ai_forum/intro_ml/intro_ml.ipynb .

  5. Setup the kernel for JupyterLab. You will create a kernel called intro_ml_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_ml/intro_ml_env/bin/activate
ipython kernel install --name "intro_ml_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: 6

    • Additional Slurm Parameters:

      --reservation=forum --mem=32G

    • Working Directory:

      /90daydata/shared/${USER}/intro_ml

    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. Open the intro_ml.ipynb notebook.

  9. Select the intro_ml_env kernel for the notebook.