Installation¶

DALI and NGC¶

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

Installing prebuilt DALI packages¶

Prerequisites¶

Linux x64.
NVIDIA Driver supporting CUDA 9.0 or later (i.e., 384.xx or later driver releases).
One or more of the following deep learning frameworks:

MXNet 1.3 mxnet-cu90 or later.

PyTorch 0.4 or later.

TensorFlow 1.7 or later.

Installation¶

Execute the below command CUDA 9.0 based build:

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/cuda/9.0 nvidia-dali

Starting DALI 0.8.0 for CUDA 10.0 based build use:

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/cuda/10.0 nvidia-dali

Note

The nvidia-dali package contains prebuilt versions of the DALI TensorFlow plugin for several versions of TensorFlow. Starting DALI 0.6.1 you can also install DALI TensorFlow plugin for the currently installed version of TensorFlow, thus allowing forward compatibility:

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/cuda/9.0 nvidia-dali-tf-plugin

Starting DALI 0.8.0 for CUDA 10.0 based build execute:

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/cuda/10.0 nvidia-dali-tf-plugin

Installing this package will install nvidia-dali and its dependencies, if these dependencies are not already installed. The package tensorflow-gpu must be installed before attempting to install nvidia-dali-tf-plugin.

Note

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.

OLDER_VERSION=0.6.1
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist nvidia-dali-tf-plugin==$OLDER_VERSION

Nightly and weekly release channels¶

Note

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.

Note

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 https://developer.download.nvidia.com/compute/redist/nightly/cuda/9.0 nvidia-dali-nightly
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/nightly/cuda/9.0 nvidia-dali-tf-plugin-nightly

for CUDA10

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/nightly/cuda/10.0 nvidia-dali-nightly
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/nightly/cuda/10.0 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 https://developer.download.nvidia.com/compute/redist/weekly/cuda/10.0 nvidia-dali-weekly
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/weekly/cuda/10.0 nvidia-dali-tf-plugin-weekly

Compiling DALI from source (using Docker builder) - recommended¶

Following these steps, it is possible to recreate Python wheels in a similar fashion as we provide as an official prebuild binary.

Prerequisites¶

Linux x64
Docker	Follow installation guide and manual at the link (version 17.05 or later is required).

Building Python wheel and (optionally) Docker image¶

Change directory (cd) into Docker directory and run ./build.sh. If needed, set the following environment variables:

PYVER - Python version. Default is 2.7.
CUDA_VERSION - CUDA toolkit version (9.0 or 10.0). Default is 10.
NVIDIA_BUILD_ID - Custom ID of the build. Default is 1234.
CREATE_WHL - Create a standalone wheel. Default is YES.
CREATE_RUNNER - Create Docker image with cuDNN, CUDA and DALI installed inside. It will create the Docker_run_cuda image, which needs to be run using nvidia-docker and DALI wheel in the wheelhouse directory under$
DALI_BUILD_FLAVOR - adds a suffix to DALI package name and put a note about it in the whl package description, i.e. nightly will result in the nvidia-dali-nightly

Compiling DALI from source (bare metal)¶

Prerequisites¶

Required Component	Notes
Linux x64
GCC 4.9.2 or later
Boost 1.66 or later	Modules: preprocessor.
NVIDIA CUDA 9.0	CUDA 8.0 compatibility is provided unofficially.
nvJPEG library	This can be unofficially disabled. See below.
protobuf	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: MXNet 1.3 `mxnet-cu90` or later PyTorch 0.4 TensorFlow 1.7 or later

Note

TensorFlow installation is required to build the TensorFlow plugin for DALI.

Note

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

Note

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 && \
 make

Get the DALI source¶

git clone --recursive https://github.com/NVIDIA/dali
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):¶

Note

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

cmake -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_C_COMPILER=clang  ..
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)

Note

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.

Install Python bindings¶

pip install dali/python