Skip to main content

NVIDIA DALI nightly for CUDA 12.0. Git SHA: 55de733b9f5d26aa0f602ecea0bf5acbb9620b23

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_cuda120-1.44.0.dev20241022.tar.gz.

File metadata

File hashes

Hashes for nvidia_dali_nightly_cuda120-1.44.0.dev20241022.tar.gz
Algorithm Hash digest
SHA256 dc875bbc23f9d433c2257ea0d9181f58b7fd05fd343b87d8e5e1fe9ef4fbd92f
MD5 e05c9f0773b9e890c59e682fed7da0d6
BLAKE2b-256 5d24431ec6d6709ac51d6ef57d6e5432590214cc3ee8cff714bf8184f001dee9

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