semvec/pypi-packages huggingface dataset:
nvidia-dali-cuda130 1.53.0
NVIDIA DALI  for CUDA 13.0. Git SHA: 55113c6cd54624aeebd7e3c0d93b4c4a68a34034
License: Apache License 2.0
Requires Python: <3.14,>=3.9

NVIDIA DALI
===========

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 <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html>`_.

.. image:: https://raw.githubusercontent.com/NVIDIA/DALI/main/dali.png
    :width: 800
    :align: center
    :alt: DALI Diagram
nvidia-dali-cuda120 1.53.0
NVIDIA DALI  for CUDA 12.0. Git SHA: 55113c6cd54624aeebd7e3c0d93b4c4a68a34034
License: Apache License 2.0
Requires Python: <3.14,>=3.9

NVIDIA DALI
===========

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 <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html>`_.

.. image:: https://raw.githubusercontent.com/NVIDIA/DALI/main/dali.png
    :width: 800
    :align: center
    :alt: DALI Diagram