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==$OLDER_VERSION
Compiling DALI from source (using Docker builder) - recommended¶
Following these steps, it is possible to recreate Python wheels in a similar fashion as we provide as an official prebuild binary.
Prerequisites¶
Linux x64 |
|
Follow installation guide and manual at the link (version 17.05 or later is required). |
Building Python wheel and (optionally) Docker image¶
Change directory (cd
) into Docker directory and run ./build.sh
. If needed, set the following environment variables:
PYVER - Python version. Default is
2.7
.CUDA_VERSION - CUDA toolkit version (9.0 or 10.0). Default is
10
.NVIDIA_BUILD_ID - Custom ID of the build. Default is
1234
.CREATE_WHL - Create a standalone wheel. Default is
YES
.CREATE_RUNNER - Create Docker image with cuDNN, CUDA and DALI installed inside. It will create the
Docker_run_cuda
image, which needs to be run usingnvidia-docker
and DALI wheel in thewheelhouse
directory under$
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. |
CUDA 8.0 compatibility is provided unofficially. |
|
This can be unofficially disabled. See below. |
|
Version 2 or later
(Version 3 or later is required for TensorFlow TFRecord file format support).
|
|
CMake 3.5 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 |
|
|
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)WERROR
- treat all build warnings as errors (default: OFF)(Unofficial)
BUILD_JPEG_TURBO
- build withlibjpeg-turbo
(default: ON)(Unofficial)
BUILD_NVJPEG
- build withnvJPEG
(default: ON)
Install Python bindings¶
pip install dali/python