Watson Machine Learning Community Edition Release 1.7.0
Release date: 02/21/2020
Watson Machine Learn Community Edition (WML CE) 1.7.0 builds upon the previous releases of WML-CE and PowerAI and includes the following updates and new features:
- pai4sk and snapml-spark conda packages are now available on x86. See Getting started with IBM Distributed Accelerated ML library.
- snapml-spark now supports Spark 2.4 in addition to Spark 2.3. See Getting started with Snap Machine Learning (SnapML) on Apache Spark.
- Multi-threaded CPU training of DecisionTree algorithms in pai4sk.
- GPU-accelerated training of DecisionTree and RandomForest algorithms in pai4sk. See Getting started with IBM Distributed Accelerated ML library.
- RandomForest supports multi-GPU acceleration.
- Applications using pai4sk APIs can use up to two GPUs from a single node without a IBM Watson® Machine Learning Accelerator.
- Support for the IBM Power® Systems IC922 and the NVIDIA T4 Tensor Core GPU CUDA 10.2 Support
- TensorFlow 2.1 with eager execution and the redesigned APIs for TensorFlow 2
- PyTorch 1.3.1
- Horovod 0.19. See Getting started with Horovod for details.
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.
- LMS for TensorFlow 2. See Getting started with TensorFlow large model support
- 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 IC922 with NVIDIA Tesla T4 GPUs
- 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
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 440
- Anaconda driver 2019.10
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 1.7.0
There a several ways for you to get WML CE 1.7.0.
- Order it. WML CE 1.7.0 is available as a no charge orderable part number from IBM. To place an order, please contact your IBM representative or authorized Business Partner.
- 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 1.7.0 in the IBM Knowledge Center:
- Read the WML CE 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 “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.
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.