NVIDIA DALI Documentation — NVIDIA DALI (original) (raw)

The NVIDIA Data Loading Library (DALI) is a GPU-accelerated library for data loading and pre-processing to accelerate deep learning applications. It provides a collection of highly optimized building blocks for loading and processing image, video and audio data. It can be used as a portable drop-in replacement for built in data loaders and data iterators in popular deep learning frameworks.

Deep learning applications require complex, multi-stage data processing pipelines that include loading, decoding, cropping, resizing, and many other augmentations. These data processing pipelines, which are currently executed on the CPU, have become a bottleneck, limiting the performance and scalability of training and inference.

DALI addresses the problem of the CPU bottleneck by offloading data preprocessing to the GPU. Additionally, DALI relies on its own execution engine, built to maximize the throughput of the input pipeline. Features such as prefetching, parallel execution, and batch processing are handled transparently for the user.

In addition, the deep learning frameworks have multiple data pre-processing implementations, resulting in challenges such as portability of training and inference workflows, and code maintainability. Data processing pipelines implemented using DALI are portable because they can easily be retargeted to TensorFlow, PyTorch, and PaddlePaddle.

DALI Diagram

DALI in action:

from nvidia.dali.pipeline import pipeline_def import nvidia.dali.types as types import nvidia.dali.fn as fn from nvidia.dali.plugin.pytorch import DALIGenericIterator import os

To run with different data, see documentation of nvidia.dali.fn.readers.file

points to https://github.com/NVIDIA/DALI_extra

data_root_dir = os.environ['DALI_EXTRA_PATH'] images_dir = os.path.join(data_root_dir, 'db', 'single', 'jpeg')

def loss_func(pred, y): pass

def model(x): pass

def backward(loss, model): pass

@pipeline_def(num_threads=4, device_id=0) def get_dali_pipeline(): images, labels = fn.readers.file( file_root=images_dir, random_shuffle=True, name="Reader") # decode data on the GPU images = fn.decoders.image_random_crop( images, device="mixed", output_type=types.RGB) # the rest of processing happens on the GPU as well images = fn.resize(images, resize_x=256, resize_y=256) images = fn.crop_mirror_normalize( images, crop_h=224, crop_w=224, mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], std=[0.229 * 255, 0.224 * 255, 0.225 * 255], mirror=fn.random.coin_flip()) return images, labels

train_data = DALIGenericIterator( [get_dali_pipeline(batch_size=16)], ['data', 'label'], reader_name='Reader' )

for i, data in enumerate(train_data): x, y = data[0]['data'], data[0]['label'] pred = model(x) loss = loss_func(pred, y) backward(loss, model)

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