DistributedDataParallel PyTorch Template

Introduction

This template provides a ready-to-use implementation of PyTorch's DistributedDataParallel (DDP) feature. It is designed to facilitate easy and efficient parallelism across multiple GPUs, minimizing the need for extensive code modifications.

Prerequisites

• PyTorch
• Tensorboard

Structure

• main.py: The main script for DDP implementation. It is recommended not to modify this file.
• model.py: Place your model architecture here.
• util.py: Define parameters, optimizers, and other utilities.
• dataloader.py: Contains the DataLoader and Dataset definitions.

Usage

To use this template, follow these steps:

1. Model Setup: Update your model architecture in model.py.

2. Parameter Configuration: Specify your training parameters, optimizer, scheduler, etc., in util.py.

3. Data Preparation: Modify dataloader.py to suit your dataset and data loading strategy.

4. Run the Script: Use the following command to run the training script:

   sbatch main.slurm

5. Tensor broad: Use the following command to run tensorborad

   ./bd.sh