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Spatial modeling with machine learning

    • Thursday, November 21, 2024, 1:30-4:30pm CT
      • Lead: ARS SCINet Office
      • Prerequisites:
        • Familiarity with basic machine learning concepts. The workshop on November 20 will provide this background, if needed.
        • Familiarity with basic Python concepts and Jupyter notebooks. We will offer virtual training for these skills before the Forum begins.

This workshop will explore examples of spatial modeling tasks (e.g., spatial interpolation from point data to gridded data) with machine learning methods. The content of the session will primarily focus on the spatial component (e.g., how to include spatial proximity as a predictor) although machine learning concepts will be discussed as relevant.

The goals of this session are to 1) introduce key concepts about incorporating spatial data in machine learning and 2) provide examples in Python on how to manipulate spatial datasets to use in machine learning functions, compare the performance of machine learning approaches for spatial prediction, and visualize observed spatial data and the prediction results.

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/$USER/spatial_modeling
 cd /90daydata/shared/$USER/spatial_modeling
 cp -r /project/ai_forum/spatial_modeling/spatial_modeling.ipynb .
 cp -r /project/ai_forum/spatial_modeling/data .

  5. Setup the kernel for JupyterLab. You will create a kernel called spatial_modeling_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/spatial_modeling/spatial_modeling_env/bin/activate
 ipython kernel install --name "spatial_modeling_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: 16

    • Additional Slurm Parameters:

      --reservation=forum --mem=32G

    • Working Directory:

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

    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 spatial_modeling_env kernel for the spatial_modeling.ipynb notebook.