Skip to main content

NVIDIA DALI weekly TensorFlow plugin for CUDA 12.0. Git SHA: 408c18bb0d8a7c1b300e02fd7f6bb58369fdf4c6

Project description

The TensorFlow plugin enables usage of DALI with TensorFlow.

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


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_tf_plugin_weekly_cuda120-1.42.0.dev20240915.tar.gz.

File metadata

File hashes

Hashes for nvidia_dali_tf_plugin_weekly_cuda120-1.42.0.dev20240915.tar.gz
Algorithm Hash digest
SHA256 b78ff020c5b98f8db34b03cdbd2c666ae35ed798700aa6ddf06845170c1155f7
MD5 74a30c77d7fe18755fdde740279ec500
BLAKE2b-256 80e2e27ab2f03457021787abf53c5c32114bc71830986168c9768df8e43dcc18

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