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Today’s deep learning applications include complex, multi-stage pre-processing data pipelines that include compute-intensive steps mainly carried out on the CPU. For instance, steps such as load data from disk, decode, crop, random resize, color and spatial augmentations and format conversions are carried out on the CPUs, limiting the performance and scalability of training and inference tasks. In addition, the deep learning frameworks today have multiple data pre-processing implementations, resulting in challenges such as portability of training and inference workflows and code maintainability.

NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks and an execution engine to accelerate input data pre-processing for deep learning applications. DALI provides both performance and flexibility of accelerating different data pipelines, as a single library, that can be easily integrated into different deep learning training and inference applications.

Key highlights of DALI include:

  • Full data pipeline accelerated from reading from disk to getting ready for training/inference
  • Flexibility through configurable graphs and custom operators
  • Support for image classification and segmentation workloads
  • Ease of integration through direct framework plugins and open source bindings
  • Portable training workflows with multiple input formats - JPEG, LMDB, RecordIO, TFRecord
  • Extensible for user specific needs through open source license

Installing prebuilt DALI packages



pip install --extra-index-url nvidia-dali

Compiling DALI from source



TensorFlow installation is required to build the TensorFlow plugin for DALI


Items marked "unofficial" are community contributions that are believed to work but not officially tested or maintained by NVIDIA.

Get the DALI source

git clone --recursive
cd dali

Make the build directory

mkdir build
cd build

Compile DALI

To build DALI without LMDB support:

cmake ..
make -j"$(nproc)" install

To build DALI with LMDB support:

cmake -DBUILD_LMDB=ON ..
make -j"$(nproc)" install

Optional CMake build parameters:

  • BUILD_PYTHON - build Python bindings (default: ON)
  • BUILD_TEST - include building test suite (default: ON)
  • BUILD_BENCHMARK - include building benchmarks (default: ON)
  • BUILD_LMDB - build with support for LMDB (default: OFF)
  • BUILD_NVTX - build with NVTX profiling enabled (default: OFF)
  • BUILD_TENSORFLOW - build TensorFlow plugin (default: OFF)
  • (Unofficial) BUILD_JPEG_TURBO - build with libjpeg-turbo (default: ON)
  • (Unofficial) BUILD_NVJPEG - build with nvJPEG (default: ON)

Install Python bindings

pip install dali/python

Getting started

docs/examples directory contains a series of examples (in the form of Jupyter notebooks) of different features of DALI. It also contains examples of how to use DALI to interface with DL frameworks.

Contributing to DALI

Contributions to DALI are more than welcome. To make the pull request process smooth, please follow these guidelines.


DALI was built with major contributions from Trevor Gale, Przemek Tredak, Simon Layton, Andrei Ivanov, Serge Panev

Latest Releases
DALI v0.1.1
 Jun. 29 2018
Initial public release
 Jun. 28 2018
 Jun. 18 2018