Skip to main content

NVIDIA DALI nightly for CUDA 12.0. Git SHA: 3db39b15308932afc8d77ed2a82922556e61e41e

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.dev20241016.tar.gz.

File metadata

File hashes

Hashes for nvidia_dali_nightly_cuda120-1.44.0.dev20241016.tar.gz
Algorithm Hash digest
SHA256 f560a7b94d8eaacb4d855f3246e5ca0ed9ea9c40a9fd04f26dba41fc5b149fd0
MD5 8af0fe32d05ac6f70af9e603e536795c
BLAKE2b-256 2824789cb620119e3de317ac619c84eec3be07c1e10ea8c9241a63323aaf778e

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