PyTorch DataLoaders with DALI

PyTorch DataLoaders implemented with nvidia-dali, we've implemented CIFAR-10 and ImageNet dataloaders, more dataloaders will be added in the future.

With 2 processors of Intel(R) Xeon(R) Gold 6154 CPU, 1 Tesla V100 GPU and all dataset in memory disk, we can extremely accelerate image preprocessing with DALI.

Iter Training Data Cost(bs=256)	CIFAR-10	ImageNet
DALI	1.4s(2 processors)	625s(8 processors)
torchvision	280.1s(2 processors)	13400s(8 processors)

In CIFAR-10 training, we can reduce tranining time from 1 day to 1 hour with our hardware setting.

Requirements

You only need to install nvidia-dali package and version should be >= 0.12, we've tested version 0.11 and it didn't work

#for cuda9.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/cuda/9.0 nvidia-dali
#for cuda10.0
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/cuda/10.0 nvidia-dali

More details and documents can be found here

Usage

You can use these dataloaders easily as the following example

from base import DALIDataloader
from cifar10 import HybridTrainPipe_CIFAR
pip_train = HybridTrainPipe_CIFAR(batch_size=TRAIN_BS,
                                  num_threads=NUM_WORKERS,
                                  device_id=0, 
                                  data_dir=IMG_DIR, 
                                  crop=CROP_SIZE, 
                                  world_size=1, 
                                  local_rank=0, 
                                  cutout=0)
train_loader = DALIDataloader(pipeline=pip_train,
                              size=CIFAR_IMAGES_NUM_TRAIN, 
                              batch_size=TRAIN_BS, 
                              onehot_label=True)
for i, data in enumerate(train_loader): # Using it just like PyTorch dataloader
    images = data[0].cuda(non_blocking=True)
    labels = data[1].cuda(non_blocking=True)

If you have large enough memory for storing dataset, we strongly recommend you to mount a memory disk and put the whole dataset in it to accelerate I/O, like this

mount  -t tmpfs -o size=20g  tmpfs /userhome/memory_data

It's noteworthy that 20g above is a ceiling but not occupying 20g memory at the moment you mount the tmpfs, memories are occupied as you putting dataset in it. Compressed files should not be extracted before you've copied them into memory, otherwise it could be much slower.