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IBM PowerAI developer portal

Learn about deep learning and PowerAI. Create something amazing.

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PowerAI Enterprise provides a complete environment for “data science as a service”, enabling SIs to accelerate the build of more accurate AI applications for clients. Read this blog to learn more.

TensorFlow Large Model Support (TFLMS) is a Python module that provides an approach to training large models and data that cannot normally be fit in to GPU memory. Read this blog to learn more.

Get access to PowerAI on IBM® Power Systems with NVIDIA GPUs on the IBM Cloud. This service is offered in partnership with Nimbix and provides users with three pricing plans to choose from.

Intro to deep learning and PowerAI


Bring more value to your organization’s data by developing with an entirely new approach to problems: deep learning, machine learning, and artificial intelligence. Unlock hidden potential and patterns in data organically — without you having to know the patterns, networks, or be an algorithmic expert. IBM is making deep learning easier and more performant for you with an enterprise software distribution with the most popular open frameworks, in PowerAI.

What is deep learning?

Deep Learning consists of algorithms that permit software to train itself— by exposing multilayered neural networks to vast amounts of data. It is most frequently used to perform tasks like speech and image recognition.

The intelligence in the process sits within the deep learning software frameworks themselves— which develop that neural model of understanding by building weights and connections between many, many data points— often millions in a training data set.

Why use deep learning?

Deep learning thrives when other traditional techniques for solving your problem fail: where you want derive insightful or complex relationships from vast data, custom programming is impossible, or on visual or auditory data.

Deep learning does require a larger data set in order to produce results commensurate with machine learning techniques. However, the amount of interpretive work done by data scientists is minimized in deep learning scenarios. Enter IBM PowerAI.

PowerAI Frameworks Summary

What is IBM PowerAI?

PowerAI is an enterprise software distribution that combines popular open source deep learning frameworks, efficient AI development tools, and accelerated IBM® Power Systems™ servers to take your deep learning projects to the next level.

Which PowerAI option is right for me?


The PowerAI product line includes three different options:

  • PowerAI: Open source software well suited to developers and data scientists just getting started with their development efforts and prototypes.
  • PowerAI Enterprise: Fully supported suite of deep learning frameworks intended for Enterprises looking to rapidly scale their AI applications.
  • PowerAI Vision: Fully supported enterprise-grade suite of tools for labeling raw datasets for training, creating, and deploying deep learning-based vision models.

Continue reading for information about each of the different offerings.

Develop with PowerAI

PowerAI is an open source software distribution of popular open source deep learning frameworks, such as, Tensorflow, Keras, and Caffe.

  • Deploy in hours, not months, through a binary download of the key open source frameworks
  • Available paid support
  • Performance for faster training times
  • Tools for ease of development
    • Reduce data preparation time by an order of magnitude, with upcoming tools
    • Automated hyper-parameter tuning and optimization to make your models faster and more accurate

PowerAI includes all necessary dependencies and removes the time, effort, and difficulty associated with getting a deep learning environment operational and performing optimally.

Deploy with PowerAI Enterprise

For enterprises looking to rapidly scale their deep-learning applications, PowerAI Enterprise combines the PowerAI features above with additional functionality to optimize and speed up the completion of your training, testing, and validation. PowerAI Enterprise truly shines when you are looking to expand into distributed deep learning with more than four nodes.

Spectrum Conductor, included in PowerAI Enterprise, masters job scheduling across thousands of nodes, improving system performance by 40% when compared to YARN for Spark. This reduces training time and increases the efficiency of your nodes by ensuring you’re using as close to 100% of your node capacity as possible.

Analyze images and video with PowerAI Vision

PowerAI Vision provides tools and interfaces for business analysts, subject matter experts, and developers without any skills in deep learning technologies to begin using deep learning. This enterprise-grade software provides a complete ecosystem to label raw data sets for training, creating, and deploying deep learning-based models. It can help train highly accurate models to classify images and detect objects in images and videos.

The tools assist users to focus on rapidly identifying datasets and labeling them. They can then train and validate a model in a GUI interface to build customized solutions for image classification and object detection. PowerAI Vision is available as an add-on to PowerAI Enterprise.

Learn how others are using deep learning and PowerAI

The University of Michigan is applying deep learning to perform better HPC simulation.

Elinar Oy is using PowerAI to rapidly augment its content management solution with new deep learning capability.

A large electric utility is revolutionizing their power line inspections with deep learning, PowerAI, and drones.

Louisiana State University is supplementing their genomics pipelines with deep learning.

IBM Research is using Power Systems to build accelerated machine learning libraries for Spark developers.

Julia Computing is helping customers marry deep learning, mathematics, and analytics across their organization.

Technology previews

PowerAI R5.2 and PowerAI Enterprise both include the following technology previews:


Snap ML

Snap ML is a new framework for lightning-fast training of classical machine learning models such as logistic regression, support vector machines and linear regression. It enables agile development, fine-grained hyper-parameter tuning, frequent re-training of models and scaling to massive datasets consisting of billions of examples and millions of features.

Key features that distinguish Snap ML include:

  • GPU acceleration: New algorithms that leverage the massive parallelism offered by modern GPU architectures.
  • Distributed training: Communication-efficient, data-parallel learning for datasets that are too big to fit inside the memory of a single node.
  • Out-of-core learning: CPU to GPU data transfer overheads can be effectively hidden behind compute times, enabling maximal GPU utilization.
  • Sparse/dense data support: Support for dense, sparse and compressed data formats.

Snap ML currently offers two APIs:

  • snap-ml-local: Single-node training for small to medium-scale datasets via a scikit-learn-compatible Python interface.
  • snap-ml-mpi: Multi-node, distributed training for large-scale datasets via a Python interface.

For more information about Snap ML and the IBM Research team that developed it, go here: https://www.zurich.ibm.com/snapml/


PyTorch

PyTorch is an open source library for Python that builds on the popular Torch deep learning framework. It allows Python developers to use familiar tools such as, numpy or scipy, while experimenting and creating prototypes.

PowerAI R5.2 includes a Technology Preview of the latest stable release of PyTorch, 0.4.0.

More information, including documentation and tutorials, can be found on the PyTorch project page at https://pytorch.org/


TFLMS

TFLMS is a Python graph editing library for Large Model Support (LMS) in TensorFlow that provides an approach to training large models, data, and batch sizes that cannot normally be fit in to GPU memory. It takes a computational graph defined by users, and automatically adds swap-in and swap-out nodes for transferring tensors from GPUs to the host and vice versa. During training and inferencing this makes the graph execution operate like operating system memory paging. The system memory is effectively treated as a paging cache for the GPU memory and tensors are swapped back and forth between the GPU memory and CPU memory.

For more information see the TFLMS README on GitHub: https://github.com/tungld/tensorflow/blob/3b39b5ecd69bebb1e9222c2292a57c5532931ade/tensorflow/contrib/lms/README.md

Development recommendations


PowerAI helps you get started faster with your deep learning development. Here are some tips for using PowerAI to add deep learning and AI to your application.

Ready your tools

Follow these simple steps to get your PowerAI-based application development started.

  1. Deploy PowerAI

    PowerAI deploys on a system far more rapidly than manual installation of frameworks. Start with:

    • Red Hat Enterprise Linux (RHEL) 7.5
    • NVIDIA CUDA SDK
    • NVIDIA GPU Driver for Linux

    The PowerAI binaries are available as RPM packages and run on IBM Power Systems S822LC and AC922 for HPC. See the PowerAI release notes for more information.

  2. Test your frameworks

    Once you’ve deployed PowerAI, you can test each of the Deep Learning training frameworks. Each framework included in PowerAI is unique and selecting a preferred framework for your application is important.

    The integrated installer for PowerAI means you have everything installed and performant, so you can rapidly try examples in each and select suited to your preferences.

  3. Devise your approach and start training

    Collecting great input data to train on is critical to your model’s success. Don’t neglect existing data inside your organization or consider training on external datasets. Your data can be visual, audio, text, or beyond.

    Packages like TensorFlow in PowerAI incorporate tools to help make your training network design even easier.


Ideas to get you started

Here are some recommendations for how to add deep learning to your applications.

The Enterprise

  • Layer Deep Learning atop your existing data-store
    Tease out value from your existing data by applying deep learning as a technique for advanced analysis.
  • Reshape or augment an existing business process
    Augment human insight or manual labor with machine intelligence. Use deep learning to train a visual or audio recognition system that helps guide decisions.

HPC

  • Apply deep learning before HPC simulation
    Improve the quality of your HPC simulation runs by using deep learning to identify which kinds of simulations to run or run first. Then run those high-likelihood simulations with greater precision.
  • Apply deep learning after HPC simulation
    Drowning in data after your HPC simulations run? Sift through existing unstructured data or vast outputs of a simulation with Deep Learning and gain new insights rapidly.

If you’re already working on other hardware…

  • Use PowerAI to get started faster, with all of the performance benefits of IBM Power
  • Use PowerAI to compare frameworks

Take a free test drive on PowerAI


Experience the performance, capability, and ease-of-use of PowerAI in the cloud

  • Run your own code or try one of the pre-packaged popular frameworks such as Caffe or TensorFlow. Find information about all of the components in PowerAI here: https://developer.ibm.com/linuxonpower/deep-learning-powerai/releases/.
  • Frameworks are available built, installed, and configured ready to use immediately.
  • If you don’t have an application but simply want to experience compelling industry use cases, the trial includes several code patterns to try out as Jupyter notebooks.
  • No set up required. Your free trial comes with a container pre-configured with the necessary frameworks and libraries.

Register now for your free 24-hour trial

Education and tech resources


Courses and learning paths

  • Data science and cognitive computing courses
    Build your Deep Learning skills, for free, with this learning path from cognitiveclass.ai.
    1. Deep Learning Fundamentals

      A great introduction to deep learning concepts, the different kinds of neural networks, and a non-exhaustive catalog of some of the critical frameworks.

    2. Deep Learning with TensorFlow

      Begin to practice Deep Learning by learning how to operate with TensorFlow, a key framework included in PowerAI from our collaborators at Google.

    3. Accelerating Deep Learning with GPU

      This course allows you to discover for yourself the value of the POWER architecture and GPU for Deep Learning workloads. Through hands on exercises, the advantage of POWER + GPU becomes obvious.

  • Deep Learning at Udacity
    Udacity offers the world’s most popular courses on Deep Learning. Longer form, but with deep educational rewards, this is the place to invest in your deep learning skills. Then, return to apply these skills with PowerAI.
    • Deep Learning, by Google
      Worldwide experts at Google and the Google Brain project deliver this well-structured long-term course. With a particular emphasis on TensorFlow, you’ll learn skills and complete a multitude of assignments that are similar to real-world problems.
    • Self-Driving Car Nanodegree Program
      NVIDIA has partnered with Udacity to offer world-class curriculum, expert instructors, and exclusive hiring opportunities in this self-driving car program.
  • NVIDIA Deep Learning Institute (DLI)
    DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning. Learn techniques for designing, training, and deploying neural networks for your domain. Explore common open source frameworks and NVIDIA’s latest GPU-accelerated deep learning platforms. Get Started with DLI labs

Technical resources

PowerAI and deep learning blogs



Even more blogs about PowerAI and deep learning on POWER

Containerize PowerAI with nvidia-docker
Published on August 31, 2017 / Updated on February 2, 2018
Learn how to build and run Dockerized deep learning analytics using PowerAI libraries on an IBM Power System S822 for High Performance Computing (“Minsky”) system with GPUs.

Deep Learning with High Resolution Images & Large Models
Published on December 12, 2017
Deep learning has had a profound impact on our ability to build highly accurate AI models. In the field of computer vision, we have gone from a 26% error rate of machine learning based models in 2011, to around 3% error rates using deep learning based computer vision. As a result, it is possible to see as well as humans on many vision tasks now.

Continue reading

IBM Research achieves record deep learning performance with new software technology
Published on August 8, 2017
IBM Research publishes in arXiv close to ideal scaling with new distributed deep learning software which achieved record communication overhead and 95% scaling efficiency on the Caffe deep learning framework over 256 NVIDIA GPUs in 64 IBM Power systems.

Continue reading

New PowerAI Developer Tools Make Deep Learning Easier to Use
Published on May 11, 2017
PowerAI started off as package of software distributions of many of the major deep learning software frameworks for model training like TensorFlow, Caffe, Torch, Theano, and the associated libraries like cuDNN. The PowerAI software has always been optimized for performance using the NVLink-based Power server, the IBM Power 822LC for HPC (“Minsky”).

Continue reading

New HPC and Deep Learning POWER8-Pascal GPU Cloud Available Now
Published on November 7, 2016
Nimbix, a high-performance computing (HPC) cloud provider, deployed a new cloud capability this week that features the extremely high-performance IBM servers that include the new POWER8 with NVIDIA NVLink processor and the new NVIDIA Tesla P100 Pascal GPU accelerator.

Continue reading

Connect and collaborate

Ask a question, contribute to the conversation, and meet the IBM PowerAI team: