Abstract

This NVIDIA Data Loading Library (DALI) 0.2 Quick Start Guide provides the installation requirements and step-by-step instructions for installing DALI as a beta release. The guide demonstrates how to get compatible MXNet, TensorFlow, and PyTorch frameworks, and install DALI from a binary or GitHub installation. This guide also provides a sample for running a DALI accelerated pre-configured ResNet-50 model on MXNet, TensorFlow, or PyTorch for image classification training.

For previously released DALI documentation, see DALI Archives.

1. Overview

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 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, PNG (fallback to CPU), raw formats, LMDB, RecordIO, TFRecord
  • Extensible for user specific needs through open source license

2. DALI And NGC

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

3. Installing DALI

DALI can be installed either directly using a pre-built binary or by compiling the sources from GitHub.

3.1. Installing Prebuilt DALI Packages

3.1.1. Prerequisites

Ensure you meet the following minimum requirements:
  • Linux x64

  • NVIDIA Driver (384.xx or later driver releases) supporting CUDA 9.0 or later

  • One or more of the following deep learning frameworks:
    MXNet 1.3 beta or later
    Version 1.3 beta from the Python package with the following command:
    pip install mxnet-cu90==1.3.0b20180612

    PyTorch 0.4
    • If you have Python version 2.7, issue the following commands:
      pip install 
      http://download.pytorch.org/whl/cu90/torch-0.4.0-cp27-cp27mu-linux_x86_64.whl 
      pip install torchvision
      
    • If you have Python version 3.5, issue the following commands:
      pip3 install 
      http://download.pytorch.org/whl/cu90/torch-0.4.0-cp35-cp35m-linux_x86_64.whl 
      pip3 install torchvision
      

    TensorFlow 1.7 or later
    Issue the following command:
    pip install tensorflow-gpu

3.1.2. Binary Installation

Install DALI using pip.
pip install --extra-index-url 
https://developer.download.nvidia.com/compute/redist nvidia-dali

3.2. Compiling DALI From Source

3.2.1. Prerequisites

Ensure you meet the following minimum requirements:
  • Linux x64

  • NVIDIA CUDA 9.0 (CUDA 8.0 compatibility is provided unofficially1)

  • nvJPEG library (This can be unofficially2 disabled)

  • 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 unofficially3 disabled)

  • OpenCV 3 or later (OpenCV 2.x compatibility is provided unofficially4)

  • liblmdb 0.9.x or later

  • One or more of the following deep learning frameworks:
    MXNet 1.3 beta or later
    Version 1.3 beta from the Python package with the following command:
    pip install mxnet-cu90==1.3.0b20180612

    PyTorch 0.4
    • If you have Python version 2.7, issue the following commands:
      pip install 
      http://download.pytorch.org/whl/cu90/torch-0.4.0-cp27-cp27mu-linux_x86_64.whl 
      pip install torchvision
      
    • If you have Python version 3.5, issue the following commands:
      pip3 install 
      http://download.pytorch.org/whl/cu90/torch-0.4.0-cp35-cp35m-linux_x86_64.whl 
      pip3 install torchvision
      

    TensorFlow 1.7 or later
    Issue the following command:
    pip install tensorflow-gpu
    Note: TensorFlow installation is required to build the TensorFlow plugin for DALI.

3.2.2. GitHub Installation

  1. Download the DALI source package from GitHub.
    git clone --recursive https://github.com/NVIDIA/dali
    cd dali
    
  2. Create the build directory.
    mkdir build
    cd build
    
  3. Compile DALI.
    1. To build DALI without LMDB support, issue the following command:
      cmake ..
      make -j"$(nproc)" install
    2. To build DALI with LMDB support, issue the following command:
      cmake -DBUILD_LMDB=ON ..
      make -j"$(nproc)" install

3.2.2.1. CMake Build Parameters

Use the following optional CMake build parameters when configuring DALI:
BUILD_PYTHON
Use this parameter to build Python bindings. The default is ON.
BUILD_TEST
Use this parameter to include building the test suite. The default is ON.
BUILD_BENCHMARK
Use this parameter to include building benchmarks. The default is ON.
BUILD_LMDB
Use this parameter to build with support for LMDB. The default is OFF.
BUILD_NVTX
Use this parameter to build with NVTX profiling enabled. The default is OFF.
BUILD_TENSORFLOW
Use this parameter to build the TensorFlow plugin. The default is OFF.
BUILD_JPEG_TURBO(unofficial)
Use this parameter to build with libjpeg-turbo. The default is ON.5
BUILD_NVJPEG(unofficial)
Use this parameter to build with nvJPEG. The default is ON.6

3.2.3. Installing Python Bindings

Issue the pip install dali/python command to install Python bindings.

4. Executing ResNet-50 Input Pipeline

After you’ve installed DALI, you can run a pre-configured, ResNet-50 model accelerated by DALI, on MXNet, PyTorch, and TensorFlow frameworks for image classification training. Each of the following samples offload image loading and augmentation operations onto GPUs.

You can use Python toolchain from the command shell or Jupyter notebook to start the ResNet-50 training session.

The DALI integrated ResNet-50 Python samples are located:

5. Uninstalling DALI

Uninstall DALI.
pip uninstall -y nvidia-dali

Notices

Notice

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Trademarks

NVIDIA, the NVIDIA logo, and cuBLAS, CUDA, cuDNN, cuFFT, cuSPARSE, DALI, DIGITS, DGX, DGX-1, Jetson, Kepler, NVIDIA Maxwell, NCCL, NVLink, Pascal, Tegra, TensorRT, and Tesla are trademarks and/or registered trademarks of NVIDIA Corporation in the Unites States and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.

1 Items marked unofficial are community contributions that are believed to work but not officially tested or maintained by NVIDIA.
2 Items marked unofficial are community contributions that are believed to work but not officially tested or maintained by NVIDIA.
3 Items marked unofficial are community contributions that are believed to work but not officially tested or maintained by NVIDIA.
4 Items marked unofficial are community contributions that are believed to work but not officially tested or maintained by NVIDIA.
5 Items marked unofficial are community contributions that are believed to work but not officially tested or maintained by NVIDIA.
6 Items marked unofficial are community contributions that are believed to work but not officially tested or maintained by NVIDIA.