Train Neural Networks without Draining your Pocket: Implementing PyTorch DDP Module

Find out how to train your model using PyTorch’s Distributed Training (DDP) module in a single-machine multi-GPU setup. Code implementation and deep-dive.

In the previous parts we’ve covered,

• The different categories of distribution primitives in PyTorch
• The concept behind DistributedDataParallel (DDP)

Today we will look at how to run distributed training using DDP.

QUICK NOTE ON DATASET AND NOTEBOOK

Keeping it consistent with the rest of the blogs in the series “Train Neural Networks without draining your pocket” we will be using Kaggle’s Brain Tumor MRI Dataset with a classifier architecture. The code I’ll use will be similar to the demo for Profiling PyTorch models using Lightning (LINK). You can check out the code in Kaggle and GitHub .

The code for running DDP is similar to the profiling code — just that we can remove the sections explicitly meant for profiling and modify it for distributed training. Let’s see how that works.

DDP WITH PYTORCH

The code is almost identical to how you’d create a PyTorch training pipeline with a few modification which we’ll discuss once we are done with the code…