ImageNet training in PyTorch

This implements training of popular model architectures, such as ResNet, AlexNet, and VGG on the ImageNet dataset.

This version has been modified to use DALI. It assumes that the dataset is raw JPEGs from the ImageNet dataset. If offers CPU and GPU based pipeline for DALI - use dali_cpu switch to enable CPU one. For heavy GPU networks (like RN50) CPU based one is faster, for some lighter where CPU is the bottleneck like RN18 GPU is. This version has been modified to use the DistributedDataParallel module in APEx instead of the one in upstream PyTorch. Please install APEx from here.

To run use the following commands

ln -s /path/to/train/jpeg/ train
ln -s /path/to/validation/jpeg/ val
python -m torch.distributed.launch --nproc_per_node=NUM_GPUS -a resnet50 --dali_cpu --fp16 --b 128 --static-loss-scale 128.0 --workers 4 --lr=0.4 ./


  • APEx - optional, required for fp16 mode or distributed (multi-GPU) operation

  • Install PyTorch from source, master branch of PyTorch on github

  • pip install -r requirements.txt

  • Download the ImageNet dataset and move validation images to labeled subfolders

    • To do this, you can use the following script


To train a model, run docs/examples/use_cases/pytorch/resnet50/ with the desired model architecture and the path to the ImageNet dataset:

python -a resnet18 [imagenet-folder with train and val folders]

The default learning rate schedule starts at 0.1 and decays by a factor of 10 every 30 epochs. This is appropriate for ResNet and models with batch normalization, but too high for AlexNet and VGG. Use 0.01 as the initial learning rate for AlexNet or VGG:

python -a alexnet --lr 0.01 [imagenet-folder with train and val folders]

Usage [-h] [--arch ARCH] [-j N] [--epochs N] [--start-epoch N] [-b N] [--lr LR] [--momentum M] [--weight-decay W] [--print-freq N] [--resume PATH] [-e] [--pretrained] DIR

PyTorch ImageNet Training

positional arguments:
DIR                         path(s) to dataset (if one path is provided, it is assumed to have subdirectories named "train" and "val"; alternatively, train and val paths can be specified directly by providing both paths as arguments)

optional arguments (for the full list please check `Apex ImageNet example <>`_)
-h, --help                  show this help message and exit
--arch ARCH, -a ARCH        model architecture: alexnet | resnet | resnet101 | resnet152 | resnet18 | resnet34 | resnet50 | vgg | vgg11 | vgg11_bn | vgg13 | vgg13_bn | vgg16 | vgg16_bn | vgg19 | vgg19_bn (default: resnet18)
-j N, --workers N           number of data loading workers (default: 4)
--epochs N                  number of total epochs to run
--start-epoch N             manual epoch number (useful on restarts)
-b N, --batch-size N        mini-batch size (default: 256)
--lr LR, --learning-rate LR initial learning rate
--momentum M                momentum
--weight-decay W, --wd W    weight decay (default: 1e-4)
--print-freq N, -p N        print frequency (default: 10)
--resume PATH               path to latest checkpoint (default: none)
-e, --evaluate              evaluate model on validation set
--pretrained                use pre-trained model
--dali_cpu                  use CPU based pipeline for DALI, for heavy GPU networks it may work better, for IO bottlenecked one like RN18 GPU default should be faster