DALI is preinstalled in the NVIDIA GPU Cloud TensorFlow, PyTorch, and MXNet containers in versions 18.07 and later.

Installing prebuilt DALI packages


  1. Linux x64.

  2. NVIDIA Driver supporting CUDA 9.0 or later (i.e., 384.xx or later driver releases).

  3. One or more of the following deep learning frameworks:


Execute the below command CUDA 9.0 based build:

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

Starting DALI 0.8.0 for CUDA 10.0 based build use:

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


Since 0.11.0 nvidia-dali package doesn’t contain prebuilt versions of the DALI TensorFlow plugin, DALI TensorFlow plugin needs to be installed explicitly for the currently present version of TensorFlow:

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

Starting DALI 0.8.0 for CUDA 10.0 based build execute:

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


Due to a known issue with installing dependent packages), DALI needs to be installed before installing nvidia-dali-tf-plugin (in a separate pip install call). The package tensorflow-gpu must be installed before attempting to install nvidia-dali-tf-plugin.


The package nvidia-dali-tf-plugin has a strict requirement with nvidia-dali as its exact same version. Thus, installing nvidia-dali-tf-plugin at its latest version will replace any older nvidia-dali versions already installed with the latest. To work with older versions of DALI, provide the version explicitly to the pip install command.

pip install --extra-index-url nvidia-dali-tf-plugin==$OLDER_VERSION

Nightly and weekly release channels


While binaries available to download from nightly and weekly builds include most recent changes available in the GitHub some functionalities may not work or provide inferior performance comparing to the official releases. Those builds are meant for the early adopters seeking for the most recent version available and being ready to boldly go where no man has gone before.


It is recommended to uninstall regular DALI and TensorFlow plugin before installing nvidia-dali-nightly or nvidia-dali-weekly as they are installed in the same path

Nightly builds

To access most recent nightly builds please use flowing release channel:

  • for CUDA9

pip install --extra-index-url nvidia-dali-nightly
pip install --extra-index-url nvidia-dali-tf-plugin-nightly
  • for CUDA10

pip install --extra-index-url nvidia-dali-nightly
pip install --extra-index-url nvidia-dali-tf-plugin-nightly
Weekly builds

Also, there is a weekly release channel with more thorough testing (only CUDA10 builds are provided there):

pip install --extra-index-url nvidia-dali-weekly
pip install --extra-index-url nvidia-dali-tf-plugin-weekly

Compiling DALI from source (bare metal)


Required Component


Linux x64

GCC 4.9.2 or later

Boost 1.66 or later

Modules: preprocessor.


CUDA 8.0 compatibility is provided unofficially.

nvJPEG library

This can be unofficially disabled. See below.


Version 2 or later
(Version 3 or later is required for TensorFlow TFRecord file format support).

CMake 3.11 or later

libjpeg-turbo 1.5.x or later

This can be unofficially disabled. See below.

FFmpeg 3.4.2 or later

We recommend using version 3.4.2 compiled following the instructions below.

OpenCV 3 or later

Supported version: 3.4

(Optional) liblmdb 0.9.x or later

One or more of the following Deep Learning frameworks:


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.


This software uses the FFmpeg licensed code under the LGPLv2.1. Its source can be downloaded from here.

FFmpeg was compiled using the following command line:

./configure \
 --prefix=/usr/local \
 --disable-static \
 --disable-all \
 --disable-autodetect \
 --disable-iconv \
 --enable-shared \
 --enable-avformat \
 --enable-avcodec \
 --enable-avfilter \
 --enable-protocol=file \
 --enable-demuxer=mov,matroska \
 --enable-bsf=h264_mp4toannexb,hevc_mp4toannexb && \

Get the DALI source

git clone --recursive
cd dali

Make the build directory

mkdir build
cd build

Compile DALI

Building DALI without LMDB support:

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

Building DALI with LMDB support:

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

Building DALI using Clang (experimental):


This build is experimental. It is neither maintained nor tested. It is not guaranteed to work. We recommend using GCC for production builds.

make -j"$(nproc)"

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)

  • BUILD_NVJPEG - build with nvJPEG support (default: ON)

  • BUILD_NVOF - build with NVIDIA OPTICAL FLOW SDK support (default: ON)

  • BUILD_NVDEC - build with NVIDIA NVDEC support (default: ON)

  • BUILD_NVML - build with NVIDIA Management Library (NVML) support (default: ON)

  • WERROR - treat all build warnings as errors (default: OFF)

  • DALI_BUILD_FLAVOR - Allow to specify custom name sufix (i.e. ‘nightly’) for nvidia-dali whl package

  • (Unofficial) BUILD_JPEG_TURBO - build with libjpeg-turbo (default: ON)


DALI release packages are built with the options listed above set to ON and NVTX turned OFF. Testing is done with the same configuration. We ensure that DALI compiles with all of those options turned OFF, but there may exist cross-dependencies between some of those features.

Following CMake parameters could be helpful in setting the right paths:

  • FFMPEG_ROOT_DIR - path to installed FFmpeg

  • NVJPEG_ROOT_DIR - where nvJPEG can be found (from CUDA 10.0 it is shipped with the CUDA toolkit so this option is not needed there)

  • libjpeg-turbo options can be obtained from libjpeg CMake docs page

  • protobuf options can be obtained from protobuf CMake docs page

Install Python bindings

pip install dali/python

Cross-compiling DALI C++ API for aarch64 Linux (Docker)


Support for aarch64 Linux platform is experimental. Some of the features are available only for x86-64 target and they are turned off in this build. There is no support for DALI Python library on aarch64 yet. Some Operators may not work as intended due to x86-64 specific implementations.

Build the aarch64 Linux Build Container

docker build -t dali_builder:aarch64-linux -f .


From the root of the DALI source tree

docker run -v $(pwd):/dali dali_builder:aarch64-linux

The relevant artifacts will be in build/install and build/dali/python/nvidia/dali