Running Mortgage Benchmark

In the previous step you should have updated the and file with the correct IP address for your LaunchPad instance. If you have not done this refer to Setting up the environment.

  1. First cd to /home/spark and execute the /home/spark/ or /home/spark/ in the System Console.

  2. In the left menu open up the Desktop link and click the VNC connect button.


  3. Open the web browser in the Linux desktop.


  4. Browse to


  5. You should see the list above.

  6. Click the lp-mortgageETL.ipynb link and this should start the Jupyter notebook.


  7. Validate the creation of the Mortgage Benchmark pods with the following command.


    kubectl get pods | grep app-name

    • The output should look similar to this.


    app-name-79d837808b2d2ba5-exec-1 1/1 Running 0 31m app-name-79d837808b2d2ba5-exec-2 1/1 Running 0 31m app-name-79d837808b2d2ba5-exec-3 1/1 Running 0 31m

  8. Create two directories for the Mortgage Dataset in the console session from the previous step.


    mkdir -p /data/mortgage/input mkdir -p /data/mortgage/output chmod 777 /data/mortgage/output

  9. From the LaunchPad Desktop download the input dataset from the Fannie Mae website.

    • Go to Single-Family Loan Performance Data page.

      • Login or Register as a new user.

    • Select HP.

      • Click on Download Data and choose Single-Family Loan Performance Data. You will find a tabular list of Acquisition and Performance files sorted based on year and quarter. Click on the file to download. Eg:

      • Unzip the downloaded file to extract the csv file: Eg: 2017Q1.csv

      • Copy the csv files to the GPU node.


      scp 2017Q1.csv nvidia@

  10. Run the notebook by clicking Cell -> Run All.


  11. Note the timing for the benchmark so you can compare to your CPU run time.


  12. Stop the notebook you started in step 1 by pressing ctrl-c in the System Console window that you started the notebook. Answer Y when asked if you want to “Shutdown this notebook server?”.

  13. Run the same Mortgage Benchmark using only CPUs.

    • Execute the script.

  14. Compare the differences between the two outputs.

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