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NVIDIA / repository
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
|License| |Documentation| |Format|
.. 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
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[!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.
.. 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)
GPUDirect Storage <https://developer.nvidia.com/gpudirect-storage>__.NVIDIA Triton Inference Server <https://developer.nvidia.com/nvidia-triton-inference-server>__
with DALI TRITON Backend <https://github.com/triton-inference-server/dali_backend>__... overview-end-marker-do-not-remove
During Kaggle computer vision competitions <https://www.kaggle.com/code/theoviel/rsna-breast-baseline-faster-inference-with-dali>:
"DALI is one of the best things I have learned in this competition" <https://www.kaggle.com/competitions/rsna-breast-cancer-detection/discussion/391059>Lightning Pose - state of the art pose estimation research model <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168383/>__To improve the resource utilization in Advanced Computing Infrastructure <https://arcwiki.rs.gsu.edu/en/dali/using_nvidia_dali_loader>__MLPerf - the industry standard for benchmarking compute and deep learning hardware and software <https://developer.nvidia.com/blog/mlperf-hpc-v1-0-deep-dive-into-optimizations-leading-to-record-setting-nvidia-performance/>__"we optimized major models inside eBay with the DALI framework" <https://www.nvidia.com/en-us/on-demand/session/gtc24-s62578/>__The following issue represents <https://github.com/NVIDIA/DALI/issues/5320>__ a high-level overview of our 2024 plan. You should be aware that this
roadmap may change at any time and the order of its items does not reflect any type of priority.
We strongly encourage you to comment on our roadmap and provide us feedback on the mentioned GitHub issue.
To install the latest DALI release for the latest CUDA version (12.x)::
pip install nvidia-dali-cuda120
# or
pip install --extra-index-url https://pypi.nvidia.com --upgrade nvidia-dali-cuda120
DALI requires NVIDIA driver <https://www.nvidia.com/drivers>__ supporting the appropriate CUDA version.
In case of DALI based on CUDA 12, it requires CUDA Toolkit <https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html>__
to be installed.
DALI comes preinstalled in the TensorFlow <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow>,
PyTorch <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch>,
and PaddlePaddle <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/paddlepaddle>__
containers on NVIDIA GPU Cloud <https://ngc.nvidia.com>__.
For other installation paths (TensorFlow plugin, older CUDA version, nightly and weekly builds, etc),
and specific requirements please refer to the Installation Guide <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/installation.html>__.
To build DALI from source, please refer to the Compilation Guide <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/compilation.html>__.
An introduction to DALI can be found in the Getting Started <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/examples/getting_started.html>__ page.
More advanced examples can be found in the Examples and Tutorials <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/examples/index.html>__ page.
For an interactive version (Jupyter notebook) of the examples, go to the docs/examples <https://github.com/NVIDIA/DALI/blob/main/docs/examples>__
directory.
Note: Select the Latest Release Documentation <https://docs.nvidia.com/deeplearning/dali/user-guide/docs/index.html>__
or the Nightly Release Documentation <https://docs.nvidia.com/deeplearning/dali/main-user-guide/docs/index.html>__, which stays in sync with the main branch,
depending on your version.
event <https://www.nvidia.com/en-us/on-demand/session/gtc24-s62578/>__event <https://www.nvidia.com/en-us/on-demand/session/gtcspring23-se52140/>__.event <https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51796/>__.event <https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41442/>__.event <https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41443/>__.event <https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31298/>__.recording <https://developer.nvidia.com/gtc/2020/video/s21139>__.slides <https://developer.download.nvidia.com/video/gputechconf/gtc/2019/presentation/s9925-fast-ai-data-pre-processing-with-nvidia-dali.pdf>,
recording <https://developer.nvidia.com/gtc/2019/video/S9925/video>.