Deep Learning in Pytorch#

Instructor#

  • Instructor: Carl Ehrett

  • Office: 2105 Barre Hall, Clemson University

  • Email: cehrett AT clemson DOT edu

Workshop Description#

This workshop series introduces the essential concepts in deep learning and walks through the common steps in a deep learning workflow from data loading and preprocessing to training and model evaluation. Throughout the sessions, students participate in writing and executing simple deep learning programs using Pytorch – a popular Python library for developing, training, and deploying deep learning models.

Prerequisites#

All students should have a Palmetto Cluster account. If you do not already have an account, you can visit our getting started page. Students should be familiar with the Python programming language. This requirement could be fulfilled by personal projects, coursework, or completion of the Introduction to Python Programming workshop series. Experience in numeric computing with the numpy library is helpful but not required.

Accessing Workshop Files#

You can download the notebooks and their contents as follows. In the terminal, create or navigate to an empty folder. Run the following command: wget https://raw.githubusercontent.com/clemsonciti/rcde_workshops/master/pytorch/download.sh This copies to your drivespace a script download.sh that, when run, will copy the full workshop files to your drivespace. So now that you have that script, run the command: bash download.sh. You should now have a folder, pytorch, which contains this notebook and the rest of the workshop.

Environment#

To run the code in this workshop, you will need a python environment with the appropriate libraries installed. You can create such an environment as follows. In the terminal (and not in JupyterLab), get an interactive session using salloc --mem=12GB. Use module load miniforge3 to load miniforge. In the directory where the workshop contents are stored, run bash create_pytorch_env.sh. Running that script will create a conda environment named PytorchWorkshop. (This will take a while; up to 20 minutes.) You can then use that environment as the Jupyter kernel to run the notebooks in this environment. (Notebook 8 contains some code which will require you to have a free WandB account.)