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An Introduction to Machine Learning for Science

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.

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): Please confirm you can successfully log in to Atlas OOD with your SCINet account (see instructions here). 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 each day of the workshop:

  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. If it is the first day of the workshop, go to step 2. For the second or third day of the workshop, go to step 7.

  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=ml_workshop -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/scinet_workshop1/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/scinet_workshop1/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=ml_workshop --mem=32G

    • Working Directory:

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

  8. If this is the first day, then follow along with the notebook available in JupyterLab. If this is the second day, open up a terminal in JupyterLab and run the following command to retrieve the second notebook:

    cd /90daydata/shared/${USER}/intro_ml
cp /project/scinet_workshop1/intro_ml/intro_ml-2.ipynb .

    If this is the third day, open up a terminal in JupyterLab and run the following command to retrieve the third notebook:

    cd /90daydata/shared/${USER}/intro_ml
cp /project/scinet_workshop1/intro_ml/intro_ml-3.ipynb .