|License| |Documentation| |Format|

NVIDIA DALI

.. overview-begin-marker-do-not-remove

The NVIDIA Data Loading Library (DALI) is a GPU-accelerated 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, and PaddlePaddle.

.. image:: /dali.png :width: 800 :align: center :alt: DALI Diagram

.. github display off

.. GitHub discards custom styles when rendering Markdown. We can use it to our advantage to hide the GitHub-style admonition in the sphinx-rendered documentation

.. only:: html

.. raw:: html

    <style>
    .github-only {
        display: none !important;
    }
    </style>

.. rst-class:: github-only .. pull-quote::

[!TIP]

The `dali-dynamic-mode <https://github.com/NVIDIA/skills/blob/main/skills/dali-dynamic-mode/SKILL.md>`_ skill
provides AI agents with guidance on the Dynamic Mode API and best practices. It can be installed as follows:

.. code-block:: sh

    npx skills add nvidia/skills --skill dali-dynamic-mode

For more information, see the `NVIDIA/skills <https://github.com/NVIDIA/skills>`_ GitHub repository.

DALI in action

.. container:: dali-tabs

Pipeline mode:

.. code-block:: python

  from nvidia.dali.pipeline import pipeline_def
  import nvidia.dali.types as types
  import nvidia.dali.fn as fn
  from nvidia.dali.plugin.pytorch import DALIGenericIterator
  import os

  # To run with different data, see documentation of nvidia.dali.fn.readers.file
  # points to https://github.com/NVIDIA/DALI_extra
  data_root_dir = os.environ['DALI_EXTRA_PATH']
  images_dir = os.path.join(data_root_dir, 'db', 'single', 'jpeg')


  def loss_func(pred, y):
      pass


  def model(x):
      pass


  def backward(loss, model):
      pass


  @pipeline_def(num_threads=4, device_id=0)
  def get_dali_pipeline():
      images, labels = fn.readers.file(
          file_root=images_dir, random_shuffle=True, name="Reader")
      # decode data on the GPU
      images = fn.decoders.image_random_crop(
          images, device="mixed", output_type=types.RGB)
      # the rest of processing happens on the GPU as well
      images = fn.resize(images, resize_x=256, resize_y=256)
      images = fn.crop_mirror_normalize(
          images,
          crop_h=224,
          crop_w=224,
          mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
          std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
          mirror=fn.random.coin_flip())
      return images, labels


  train_data = DALIGenericIterator(
      [get_dali_pipeline(batch_size=16)],
      ['data', 'label'],
      reader_name='Reader'
  )


  for i, data in enumerate(train_data):
      x, y = data[0]['data'], data[0]['label']
      pred = model(x)
      loss = loss_func(pred, y)
      backward(loss, model)

Dynamic mode:

.. code-block:: python

  import os
  import nvidia.dali.types as types
  import nvidia.dali.experimental.dynamic as ndd
  import torch

  # To run with different data, see documentation of ndd.readers.File
  # points to https://github.com/NVIDIA/DALI_extra
  data_root_dir = os.environ['DALI_EXTRA_PATH']
  images_dir = os.path.join(data_root_dir, 'db', 'single', 'jpeg')


  def loss_func(pred, y):
      pass


  def model(x):
      pass


  def backward(loss, model):
      pass


  reader = ndd.readers.File(file_root=images_dir, random_shuffle=True)

  for images, labels in reader.next_epoch(batch_size=16):
      images = ndd.decoders.image_random_crop(images, device="gpu", output_type=types.RGB)
      # the rest of processing happens on the GPU as well
      images = ndd.resize(images, resize_x=256, resize_y=256)
      images = ndd.crop_mirror_normalize(
          images,
          crop_h=224,
          crop_w=224,
          mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
          std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
          mirror=ndd.random.coin_flip(),
      )

      x = torch.as_tensor(images)
      y = torch.as_tensor(labels.gpu())

      pred = model(x)
      loss = loss_func(pred, y)
      backward(loss, model)

This is the beginning of the README. Read the rest at the original. Read the full README ↗