Deep learning applications require complex, multi-stage pre-processing data pipelines. Such data pipelines involve compute-intensive operations that are carried out on the CPU. For example, tasks such as: load data from disk, decode, crop, random resize, color and spatial augmentations and format conversions, are mainly carried out on the CPUs, limiting the performance and scalability of training and inference.
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.
NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks, and an execution engine, to accelerate the pre-processing of the input data for deep learning applications. DALI provides both the performance and the flexibility for accelerating different data pipelines as a single library. This single library can then be easily integrated into different deep learning training and inference applications.
Highlights of DALI are:
- Full data pipeline--accelerated from reading the disk to getting ready for training and 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, PNG (fallback to CPU), TIFF (fallback to CPU), BMP (fallback to CPU), raw formats, LMDB, RecordIO, TFRecord.
- Extensible for user-specific needs through open source license.
DALI is preinstalled in the NVIDIA GPU Cloud TensorFlow, PyTorch, and MXNet containers in versions 18.07 and later.
- 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-cu90or later.- PyTorch 0.4
- TensorFlow 1.7 or later.
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-daliStarting 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-daliNote
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-pluginStarting 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-pluginInstalling 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==$OLDER_VERSIONFollowing these steps, it is possible to recreate Python wheels in a similar fashion as we provide as an official prebuild binary.
| Linux x64 | |
| Docker | Follow installation guide and manual at the link (version 17.05 or later is required). |
Change directory (cd) into Docker directory and run ./build.sh. If needed, set the following environment variables:
- PYVER - Python version. Default is
2.7. - CUDA_VERSION - CUDA toolkit version (9.0 or 10.0). Default is
10. - NVIDIA_BUILD_ID - Custom ID of the build. Default is
1234. - CREATE_WHL - Create a standalone wheel. Default is
YES. - CREATE_RUNNER - Create Docker image with cuDNN, CUDA and DALI installed inside. It will create the
Docker_run_cudaimage, which needs to be run usingnvidia-dockerand DALI wheel in thewheelhousedirectory under$
| 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 3.5 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 | We recommend using version 3.4+. However previous versions are also compatible.
OpenCV 2.x compatibility is provided unofficially.
|
| (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 && \
makegit clone --recursive https://github.com/NVIDIA/dali
cd dalimkdir build
cd buildcmake ..
make -j"$(nproc)"cmake -DBUILD_LMDB=ON ..
make -j"$(nproc)"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)
pip install dali/pythonThe docs/examples directory contains a few examples (in the form of Jupyter notebooks) highlighting different features of DALI and how to use DALI to interface with deep learning frameworks.
Also note:
- Documentation for the latest stable release is available here, and
- Nightly version of the documentation that stays in sync with the master branch is available here.
- GPU Technology Conference 2018 presentation about DALI, T. Gale, S. Layton and P. Tredak: slides, recording.
We welcome contributions to DALI. To contribute to DALI and make pull requests, follow the guidelines outlined in the Contributing document.
We appreciate feedback, questions or bug reports. When you need help with the code, follow the process outlined in the Stack Overflow (https://stackoverflow.com/help/mcve) document. Ensure that the posted examples are:
- minimal: Use as little code as possible that still produces the same problem.
- complete: Provide all parts needed to reproduce the problem. Check if you can strip external dependency and still show the problem. The less time we spend on reproducing the problems, the more time we can dedicate to the fixes.
- verifiable: Test the code you are about to provide, to make sure that it reproduces the problem. Remove all other problems that are not related to your request.
DALI was built with major contributions from Trevor Gale, Przemek Tredak, Simon Layton, Andrei Ivanov, Serge Panev.