Distributed Training in PyTorch on ImageNette

This repository contains working code to train on ImageNette using DISTRIBUTED DATA PARALLEL (DDP) in PyTorch and Hugging Face Accelerate.

🤗 Accelerate - DOCS | GitHub

For a deep-dive into the HF Accelerate package, refer to Inside Hugging Face's Accelerate!.

To be able to run the scripts, please run the following commands first from the root directory of this repository to download the data:

mkdir data && cd data 
wget https://s3.amazonaws.com/fast-ai-imageclas/imagenette2-160.tgz
tar -xvf imagenette2-160.tgz

Now you should have a data directory in the repository whose folder structure looks like:

data/
└── imagenette2-160
    ├── train
    │   ├── n01440764
    │   ├── n02102040
    │   ├── n02979186
    │   ├── n03000684
    │   ├── n03028079
    │   ├── n03394916
    │   ├── n03417042
    │   ├── n03425413
    │   ├── n03445777
    │   └── n03888257
    └── val
        ├── n01440764
        ├── n02102040
        ├── n02979186
        ├── n03000684
        ├── n03028079
        ├── n03394916
        ├── n03417042
        ├── n03425413
        ├── n03445777
        └── n03888257

Launch training using PyTorch DDP

To launch training using PyTorch DDP, run the following command from the src folder of this repository:

./ddp.sh <number-of-gpus>

Launch training using Huggingface Accelerate

To launch training using Huggingface Accelerate, run the following command from the src folder of this repository:

accelerate launch train_accelerate.py