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Watson Machine Learning Accelerator

Quickly and easily build an end-to-end deep learning environment for your organization with this enterprise-class software solution

WML Accelerator Release 1.2.1

Released on 06/28/2019

WML Accelerator is an enterprise-class software solution for quickly and easily building an end-to-end deep learning environment for your organization. WML Accelerator release 1.2.1 includes all of the open source deep learning frameworks, libraries, and tools included in Watson Machine Learning Community Edition (formerly PowerAI) 1.6.1 along with IBM Spectrum Conductor version 2.3.0, and IBM Spectrum Conductor Deep Learning Impact version 1.2.3 to give data scientists everything they need to build a distributed deep learning environment in hours rather than days or weeks — and to easily manage it as the environment grows.

Go to the IBM Knowledge Center for information about planning, installing, configuring, and managing WML Accelerator 1.2.1

What’s new

WML Accelerator 1.2.1 builds upon the previous release of WML Accelerator and includes the following updates and new features:

  • All of the updates and features included in WML CE 1.6.1
  • IBM Spectrum Conductor Deep Learning Impact is updated to version 1.2.3. Learn more.

Key features of WML Accelerator

  • Distributed deep learning architecture that simplifies the process of training deep learning models across a cluster for faster time to results.
  • Software libraries for machine learning (SnapML) acceleration and large model support (LMS) enabling more complex models with larger, more high-resolution data inputs.
  • Enhanced data ingest, preparation, and transformation tools, using Apache Spark for data management. IBM® Power® Systems servers are built for artificial intelligence (AI) applications, incorporating high-bandwidth and low-latency NVLink connections between GPU accelerators for peer-to-peer communications, and directly connecting GPU accelerators to system CPUs (and system memory).
  • Powerful model development tools, including real-time training visualization and runtime monitoring of accuracy and hyper-parameter search and optimization, for faster model development.
  • Ready-to-use deep learning frameworks (TensorFlow, Caffe-BVLC, and IBM Caffe) are included.
  • Multitenant architecture designed to run deep learning, high-performance analytics, and other long-running services and frameworks on shared resources.

How to get WML Accelerator

There are several ways to get WML Accelerator 1.2.1:

  • Install an evaluation version to give it a try. If you don’t already have one, you’ll need to register for an IBMID to access the evaluation. Instructions for installing the evaluation version can be found here: Installing (Manual) or Automated installer. When the evaluation version expires, you can upgrade to the entitled version by following these instructions: Updating from the evaluation version to the entitled version. or you can uninstall the product by following these instructions: Uninstalling.
  • Order it from your IBM representative or authorized Business Partner.

    When you are entitled to download the packages, download them from either:

Learn more about WML Acclerator

Previous WML Accelerator (and PowerAI Enterprise) releases

We recommend that you install the most current release, however, if you have an earlier version installed, you can find release information in the IBM Knowledge Center:

Requesting enhancements for IBM Watson Machine Learning Accelerator

The IBM Request for Enhancement (RFE) tool is now available for you to submit formal enhancement requests to the WML Accelerator development team. One of the benefits of using the RFE tool is that other clients can vote on submitted requirements, which helps IBM to prioritize requests.

Get started

Go here get started: ibm.biz/wmla-rfe

The RFE for Watson ML Accelerator pages are part of IBM Developer and require that you sign in with an IBM ID to submit or vote on a request. You should make sure that your IBM ID profile includes your current company and your email address to ensure that we can contact you if we have questions.

Search first

Once on the RFE page, click on the “Search” tab to view existing requests before you submit a new request. It is much more useful to vote for a previously submitted request than to submit a duplicate request.