Watson Machine Learning Community Edition Release 1.6.2
Release date: 10/31/2019
Watson Machine Learn Community Edition (WML CE) 1.6.2 builds upon the previous releases of WML CE and includes the following updates and new features:
- Improved performance for Distributed Deep Learning on large scale jobs.
- NVIDIA TensorRT, a C++ library provided by NVIDIA that focuses on running pre-trained networks quickly and efficiently for inferencing, is now included. See: Getting started with TensorRT.
- Apex, a PyTorch add-on package from NVIDIA with capabilities for automatic mixed precision (AMP) and distributed training, is no longer a technology preview and is now fully supported. See: Getting started with Apex.
- Torchtext, which is a companion package to PyTorch consisting of data processing utilities and popular data sets for natural language, is no longer a technology preview and is now fully supported. See: Getting started with torchtext.
- The XLA (Accelerated Linear Algebra) compiler has been enabled in Tensorflow. See: Getting started with TensorFlow.
- SnapML Estimators (LogisticRegression, LinearRegression, SupportVectorMachine) and Transformers (LogisticRegressionModel, LinearRegressionModel, SupportVectorMachineModel) are introduced and can be used in a Spark ML pipeline. See: Getting started with Snap Machine Learning (SnapML) on Apache Spark.
- The CPU-only variant of Pytorch is now supported. See: PyTorch GPU-enabled and CPU-only variants.
- Python 3.7 support was added.
Technology previews include:
- pai4sk: SnapBoost algorithm – a boosting algorithm that can be used to construct an ensemble of decision trees. See: Getting started with IBM Distributed Accelerated ML library.
- Dask support for GPU-backed dataframe (dask-cudf) and multi-GPU machine learning algorithms. See: Getting started with RAPIDS-CuML.
- CuPy: an open-source NumPy-compatible matrix library accelerated by CUDA. See: Getting started with RAPIDS.
- License information for the technology previews is found on the Technology Preview Code page in the IBM Knowledge Center.
For more details, including updated and deprecated packages, see the What’s New topic in the IBM Knowledge Center.
WLM CE is distributed as prebuilt containers, or on demand through the Conda provisioning process.
- All of the Conda packages are available in a Conda channel
- There is no install package to download, instead connect to the Conda channel and install your packages from there
- Package dependencies are automatically resolved
- Delivery of packages is open and continuous
- You can now run more than one framework at the same time in the same environment. For example, you can run TensorFlow and PyTorch at the same time.
- IBM Power System AC922 with NVIDIA Tesla V100 GPUs
- IBM Power System S822LC with NVIDIA Tesla P100 GPUs
- x86_64 systems with NVIDIA Tesla V100 or P100 GPUs
Differences between Power Systems and x86:
Power Systems x86 DDL Up to four nodes are supported Up to two nodes are supported SnapML Up to four nodes are supported Not supported
Supported operating systems and required 3rd party software:
- Red Hat Enterprise Linux for POWER LE 7.6 and 7.7
- Ubuntu 18.04.1
- NVIDIA GPU driver 418
- Anaconda driver 2019.07
For the full release notes and README, including software packages and prerequisites, start with the WML CE planning topic in the IBM Knowledge Center.
How to get WML CE 1.6.2
There a several ways for you to get WML CE 1.6.2.
- Order it. WML CE 1.6.2 is available as a no charge orderable part number from IBM. To place an order, please contact your IBM representative or authorized Business Partner.
- Get download instructions from here: http://ibm.biz/download-wmlce
- Connect to the WML CE Conda repository
- Get the Docker container from here: https://hub.docker.com/r/ibmcom/powerai/
- Get information about planning, configuring, and managing WML CE 1.6.2 in the IBM Knowledge Center:
- Read WML CE (formerly PowerAI) and deep learning blogs from the experts
- Review the WML CE technical resources library
We recommend that you install the most current release of WML CE, however, if you have an earlier version installed, you can find release information in the IBM Knowledge Center:
Requesting enhancements for WML CE
The IBM Request for Enhancement (RFE) tool is now available for you to submit formal enhancement requests to the WML CE 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.
Go here get started: ibm.biz/powerai-rfe
The RFE for WML CE 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.
Once on the RFE page, click on the â€śSearchâ€ť 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.
Alternative to submitting a RFE
If you prefer, you can submit your feedback directly to the WML CE team by completing this short survey form.