Skip to main content

NVIDIA DALI nightly for CUDA 11.0. Git SHA: 408c18bb0d8a7c1b300e02fd7f6bb58369fdf4c6

Project description

The NVIDIA Data Loading Library (DALI) is a 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, MXNet and PaddlePaddle.

For more details please check the latest DALI Documentation.

DALI Diagram

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

File details

Details for the file nvidia_dali_nightly_cuda110-1.42.0.dev20240913.tar.gz.

File metadata

File hashes

Hashes for nvidia_dali_nightly_cuda110-1.42.0.dev20240913.tar.gz
Algorithm Hash digest
SHA256 a7551add5ed599b8e83e2cf0cd0fa621fe75ec12f57346202d288293d9b9fb0d
MD5 8070e8c6f2eb0419312e5ec8d944553a
BLAKE2b-256 69721fa47ba6330d8cd249bd83b8d2008ec6f9db325975eda77977d0789f7d89

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page