Naifan Li
Tags Categories Archives About
Naifan Li

Contents

NVIDIA Data Loading Library (DALI)

Published on Updated on 2 minutes
Contents

Installtion

  • loading

  • decoding

  • augmentation for the image, video, and audio

  • GPU Acceleration

  • Data Support: Get support for multiple data formats—LMDB,, TFRecord, COCO, JPEG, PNG, TIFF, JPEG2k, wav, flac, ogg, H.26, HEVC, and more.

  • Custom Pipelines: Use flexible graphs to create custom pipelines and add custom audio, image, and video processing operators.

  • a high-preformance alternative to built-in data loaders and data iterators in popular deep learning frameworks.

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.

At the core of data processing with DALI lies the concept of a data processing pipeline. It is composed of multiple operations connected in a directed graph and contained in an object of class class nvidia.dali.Pipeline. This class provides functions necessary for defining, building and running data processing pipelines.

Augmentation Gallery Brightness Contrast Example Color Space Conversion Image Decoder examples HSV Example Using HSV to implement Random Grayscale Operation Interpolation Methods Resize Operator WarpAffine 3D Transforms

It is a single library, that can be easily integrated into different deep learning training and inference applications.

Videos and Webinars Additional Resources

DALI Key Visual

https://docs.nvidia.com/deeplearning/dali/user-guide/docs/installation.html

https://github.com/NVIDIA/DALI

Data loaders dali: Leverages a DALI pipeline along with DALI’s PyTorch iterator for data loading, preprocessing, and augmentation. dali_proxy: Uses a DALI pipeline for preprocessing and augmentation while relying on PyTorch’s data loader. DALI Proxy facilitates the transfer of data to DALI for processing. pytorch: Employs the native PyTorch data loader for data preprocessing and augmentation.

Pipeline

  • DataNode: an output of a DALI operator

  • ‘cpu’: operators that accept CPU inputs and produce CPU outputs.

  • ‘mixed’: operators that accept CPU inputs and produce GPU outputs, for example nvidia.dali.fn.decoders.image().

  • ‘gpu’: operators that accept GPU inputs and produce GPU outputs.

Data produced by a CPU operator may be explicitly copied to the GPU by calling .gpu() on a DataNode

Pipeline Decorator

nvidia.dali.pipeline_def

Decorator that converts a graph definition function into a DALI pipeline factory.

ws how to use DALI to load numpy files directly to GPU memory, thanks to NVIDIA GPUDirect Storage, and how to use the region-of-interest (ROI) API to load regions of the array.

https://docs.nvidia.com/deeplearning/dali/user-guide/docs/operations/nvidia.dali.fn.readers.html