Deep Learning software packages

PowerAI release 3.4 provides software packages for several Deep Learning frameworks, supporting libraries, and tools:

All the packages are intended for use with Ubuntu 16.04 on POWER with NVIDIA CUDA 8.0 and cuDNN v5.1 packages.

More information about PowerAI is available at https://ibm.biz/powerai. Developer resources can be found at http://ibm.biz/poweraideveloper

System set up

Operating System

The Deep Learning packages require Ubuntu 16.04 for IBM POWER8. Ubuntu installation images can be downloaded from:

http://www.ubuntu.com/download/server/power8

NOTE: After installing Ubuntu 16.04 update the libc6 package to version 2.23-0ubuntu5 or higher. That version fixes problems affecting Torch and TensorFlow.

NOTE: PowerAI Release 3.4 requires the version 4.4 linux kernel. Ubuntu 16.04 supports two different kernel versions: the base kernel (version 4.4), and the Hardware Enablement kernel (currently version 4.8; see https://wiki.ubuntu.com/Kernel/RollingLTSEnablementStack). Be sure to install the base 4.4 kernel for PowerAI.

NVIDIA components

The Deep Learning packages require NVIDIA CUDA 8.0 and cuDNN 5.1, which can be installed as follows:

  1. Download and install NVIDIA CUDA 8.0 from https://developer.nvidia.com/cuda-downloads
  2. Download NVIDIA cuDNN 5.1 for CUDA 8.0 Power8 Deb packages from https://developer.nvidia.com/cudnn (Registration in NVIDIA's Accelerated Computing Developer Program is required)

  3. Install the cuDNN v5.1 packages

       $ sudo dpkg -i libcudnn5*deb

The required and recommended versions of these components are:

NOTE: PowerAI Release 3.4 requires a version 361 GPU driver. Version 375 GPU drivers are available for download from NVIDIA (see below) but are not yet tested or supported with PowerAI.

NVIDIA GPU driver update

NVIDIA driver updates for POWER8 are available from https://www.nvidia.com (select DRIVERS then All NVIDIA Drivers).

Installing the Deep Learning Frameworks

Software repository Setup

The PowerAI Deep Learning packages are provided via two different installation methods:

These packages are mutually exclusive. Choose one or the other for your systems.

Software repository setup is similar for either method:

  1. Download the desired repository package (.deb file) from https://public.dhe.ibm.com/software/server/POWER/Linux/mldl/ubuntu/

  2. Install the repository package:

       $ sudo dpkg -i mldl-repo-*.deb

  3. Update the package cache

       $ sudo apt-get update

Installing all frameworks at once

All the Deep Learning frameworks can be installed at once using the power-mldl meta-package:

    $ sudo apt-get install power-mldl

Installing frameworks individually

The Deep Learning frameworks can be installed individually if preferred. The framework packages are:

Each can be installed with:

    $ sudo apt-get install <framework>

Installation note for DIGITS

The digits and python-socketio-server packages conflict with Ubuntu's older python-socketio package. Please uninstall the python-socketio package before installing DIGITS.

Upgrading from a previous release

To upgrade from a previous release:

  1. Install the new repository package

  2. Update the repository meta-data

       $ sudo apt-get update

  3. Upgrade the packages, as desired:

Tuning recommendations

Recommended settings for optimal Deep Learning performance on the S822LC for High Performance Computing are:

Getting started with MLDL Frameworks

General setup

Each framework package provides a shell script to simplify environmental setup.

We recommend that users update their shell rc file (e.g. .bashrc) to source the desired setup scripts. For example:

    source /opt/DL/<framework>/bin/<framework>-activate

Each frame also provides a test script to verify basic function:

    $ <framework>-test

Note about python setuptools / easy_install

The python easy_install utility may interfere with the proper function of some of the PowerAI framework packages, including TensorFlow and Chainer.

The PowerAI packages include local copies of python modules such as protobuf (TensorFlow) and pillow (Chainer) because they require versions newer than those provided by Canonical / Ubuntu. The <framework>-activate scripts set up the pathing needed to make that work (they set PYTHONPATH to give the local copies priority over the system default versions).

easy_install adds a script that may cause the system's default paths to be searched ahead of PYTHONPATH entries. This may result in protobuf or pillow related failures in TensorFlow and Chainer.

If easy-install is run as root, the problematic script may be found in:

    /usr/local/lib/python2.7/dist-packages/easy-install.pth

Getting started with Caffe

Caffe alternatives

Packages are provided for upstream BVLC Caffe (/opt/DL/caffe-bvlc), IBM optimized BVLC Caffe (/opt/DL/caffe-ibm), and NVIDIA's Caffe (/opt/DL/caffe-nv). The system default Caffe (/opt/DL/caffe) can be selected using Ubuntu's alternatives system:

    $ sudo update-alternatives --config caffe
    There are 3 choices for the alternative caffe (providing /opt/DL/caffe).

      Selection    Path                Priority   Status
    ------------------------------------------------------------
    * 0            /opt/DL/caffe-ibm    100       auto mode
      1            /opt/DL/caffe-bvlc   50        manual mode
      2            /opt/DL/caffe-ibm    100       manual mode
      3            /opt/DL/caffe-nv     75        manual mode

    Press <enter> to keep the current choice[*], or type selection number:

Users can activate the system default caffe:

    source /opt/DL/caffe/bin/caffe-activate

Or they can activate a specific variant. For example:

    source /opt/DL/caffe-bvlc/bin/caffe-activate

Attempting to activate multiple Caffe packages in a single login session will cause unpredictable behavior.

Caffe samples and examples

Each Caffe package includes example scripts and sample models, etc. A script is provided to copy the sample content into a specified directory:

    $ caffe-install-samples <somedir>

Optimizations in IBM Caffe

The IBM Caffe package (caffe-ibm) in PowerAI is based on upstream BVLC Caffe commit b2982c7 (https://github.com/BVLC/caffe/tree/b2982c7eef65a1b94db6f22fb8bb7caa986e6f29).

Optimization

Our optimizations aim to reduce the running time of a multiple-GPU training by utilizing CPUs. In particular, gradient accumulation is offloaded to CPUs and done in parallel with the training. To gain the best performance with IBM Caffe, please close unnecessary applications that consume a high percentage of CPU.

If using a single GPU, IBM Caffe and BVLC Caffe will have similar performance.

The optimizations in IBM Caffe do not change the convergence of a neural network during training. IBM Caffe and BVLC Caffe should produce the same convergence results.

Command line options

IBM Caffe has the same set of options as BVLC Caffe except two additional ones:

Verifying the performance of IBM Caffe

This section shows how to compare IBM Caffe and BVLC Caffe in training Alexnet with a batch size 256 per GPU.

NVIDIA Caffe versions

This PowerAI release includes packages for both NV Caffe 0.14.5 and 0.15.14. The versions may behave differently (e.g. in performance or convergence) with different models.

NV Caffe 0.15.14 is NCCL enabled and will be installed by default. Version 0.14.5 can be installed as follows:

More info

Visit Caffe's website (http://caffe.berkeleyvision.org/) for tutorials and example programs that you can run to get started.

Here are links to a couple of the example programs:

Getting started with Chainer

The Chainer home page at http://chainer.org/ includes documentation for the Chainer project, including a Quick Start example.

Getting started with Tensorflow

The TensorFlow homepage (https://www.tensorflow.org/) has a variety of information, including Tutorials, How Tos, and a Getting Started guide.

Additional tutorials and examples are available from the community, for example:

API changes and sample models

Note that the TensorFlow API is updated in version 1.0, so programs written for earlier versions of TensorFlow may need to be updated. The TensorFlow v1.0.0 release notes describe the changes and link to a conversion tool. See: https://github.com/tensorflow/tensorflow/releases/tag/v1.0.0

The TensorFlow team provides example models on GitHub at https://github.com/tensorflow/models Some of the example models may not be updated for the new API.

For the inception/imagenet_train example:

Additional features

The PowerAI TensorFlow packages include TensorBoard. See: https://www.tensorflow.org/get_started/summaries_and_tensorboard

The TensorFlow 1.0.1 package includes support for additional features:

TensorFlow versions

This PowerAI release includes packages for both TensorFlow version 1.0.1 and 0.12. The versions may behave differently (e.g. in performance or convergence) with different models.

TensorFlow version 1.0.1 will be installed by default. TensorFlow 0.12 can be installed as follows:

Getting started with Torch

The Torch Cheatsheet contains lots of info for people new to Torch, including tutorials and examples.

The Torch project has a demos repository at https://github.com/torch/demos

Tutorials can be found at https://github.com/torch/tutorials

Visit Torch's website for the latest from Torch.

Torch samples and examples

The Torch package includes example scripts and samples models. A script is provided to copy the sample content into a specified directory:

    $ torch-install-samples <somedir>

Among these are the Imagenet examples from https://github.com/soumith/imagenet-multiGPU.torch with a few modifications.

Extending Torch with additional Lua rocks

The Torch package includes several Lua rocks useful for creating Deep Learning applications. Additional Lua rocks can be installed locally to extend functionality. For example a rock providing NCCL bindings can be installed by:

    $ source /opt/DL/torch/bin/torch-activate
    $ source /opt/DL/nccl/bin/nccl-activate

    $ luarocks install --local --deps-mode=all "https://raw.githubusercontent.com/ngimel/nccl.torch/master/nccl-scm-1.rockspec"
    ...
    nccl scm-1 is now built and installed in /home/user/.luarocks/ (license: BSD)

    $ luajit
    LuaJIT 2.1.0-beta1 -- Copyright (C) 2005-2015 Mike Pall. http://luajit.org/
    JIT: OFF
    > require 'torch'
    > require 'nccl'
    >

Getting started with Theano

Here are some links to help you get started with Theano:

Visit Theano's website for the latest from Theano.

Theano 0.9.0 deprecates support for the old GPU backend (e.g. THEANO_FLAGS=device=gpu) and adds support for the gpuarray backend (e.g. THEANO_FLAGS=device=cuda0). The old GPU backend will likely be removed in a future Theano update.

Getting started with DIGITS

The first time it's run digits-activate will create a .digits subdirectory containing the DIGITS jobs directory, as well as the digits.log file

Multiple instances of the DIGITS server can be run at once, including by different users, but users may need to set the network port number to avoid conflicts.

To start DIGITS server with default port (5000):

    $ digits-devserver

To start DIGITS server with specific port

    $ digits-devserver -p <port_num>

NVIDIA's DIGITS site has more information about DIGITS.

The DIGITS Getting Started guide describes how to train a network model to classify the MNIST hand-written digits dataset.

Additional DIGITS examples are available at https://github.com/NVIDIA/DIGITS/tree/master/examples

The PowerAI Torch package is updated to work with DIGITS. Manual installation of individual lua rocks is no longer required.

Installing DIGITS plugins

The DIGITS package supports the use of python installed plugins to provide additional features when using the DIGITS server page. These plugins are included in the PowerAI distribution and can be installed in one of two ways.

Install Plugins under the current user

    $ pip install /opt/DL/digits/plugins/data/imageGradients
    $ pip install /opt/DL/digits/plugins/view/imageGradients

Install Plugins for all users

    $ sudo pip install /opt/DL/digits/plugins/data/imageGradients
    $ sudo pip install /opt/DL/digits/plugins/view/imageGradients

Examples using DIGITS plugins can be found in DIGITS examples folder

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