Advanced Deep Learning in Pytorch#
Instructor#
Instructor: D. Hudson Smith
Office: 2105 Barre Hall, Clemson University
Email: dane2 AT clemson DOT edu
Workshop Description#
Welcome to the advanced pytorch workshop series. In this series we will start with a simple deep learning example and then iteratively add in more advanced approaches and tooling. We will work through the following techniques:
Downloading and fine-tuning pre-trained models
Script-based development workflow
Organizing model code with Pytorch Ligtning
Checkpointing
Experiment tracking with Weights & Biases
Using multiple-GPUs
Using pytorch syntax
In Pytorch Lightning
Profiling GPU usage
Reproducible research with Pytorch
Version control
Setting random seeds
Logging results
Hyperparameter tuning
Bash scripting
Using Sweeps
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 must have basic familiarity with Python programming and a good grasp of Pytorch fundamentals. This requirement is satisified by taking the the introductory pytorch series or other experience using Pytorch.