Image Classification and CNN
Building a CNN Classifier with PyTorch: Part 1
Building a CNN Classifier with PyTorch: Part 2
Introduction to DDP with PyTorch
Single Node Multi-GPU Training with torchrun
Multi-node Distributed Training with PyTorch
Lab Setup

Packages and Parameters

Let’s get started by importing the modules we need for this notebook as well as set a few hyperparameters needed throughout the notebook. Then, we will dive into the basics of dataset loaders and transforms.

Tips:

This notebook will use the following hyperparameters:

  • Learning Rate (lr): how much model parameters are updated at each batch/epoch
  • Batch Size: number of data points used to estimate gradients at each iteration
  • Epochs: Number of times to iterate over our entire dataset in optimization process

These hyperparameters will be used throughout the notebook.

Back
 
Next
© Chishiki-AI  |   Cornell University    |   Center for Advanced Computing    |   Copyright Statement    |   Access Statement
CVW material development is supported by NSF OAC awards 1854828, 2321040, 2323116 (UT Austin) and 2005506 (Indiana University)