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Machine Learning with PowerAI

There are different documentation sources for users to learn more about the PowerAI Framework for Machine Learning. In the following the links are valid for PowerAI version 1.5.4.


The information provided here is available from IBM and can be used on partition ml only!

General Overview

Specific User Guides

  • Getting Started with PowerAI
  • Caffe
  • TensorFlow
  • TensorFlow Probability This release of PowerAI includes TensorFlow Probability. TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow.
  • TensorBoard
  • Snap ML This release of PowerAI includes Snap Machine Learning (Snap ML). Snap ML is a library for training generalized linear models. It is being developed at IBM with the vision to remove training time as a bottleneck for machine learning applications. Snap ML supports many classical machine learning models and scales gracefully to data sets with billions of examples or features. It also offers distributed training, GPU acceleration, and supports sparse data structures.
  • PyTorch This release of PowerAI includes the community development preview of PyTorch 1.0 (rc1). PowerAI's PyTorch includes support for IBM's Distributed Deep Learning (DDL) and Large Model Support (LMS).
  • Caffe2 and ONNX This release of PowerAI includes a Technology Preview of Caffe2 and ONNX. Caffe2 is a companion to PyTorch. PyTorch is great for experimentation and rapid development, while Caffe2 is aimed at production environments. ONNX (Open Neural Network Exchange) provides support for moving models between those frameworks.
  • Distributed Deep Learning Distributed Deep Learning (DDL). Works on up to 4 nodes on partition ml.

PowerAI Container

We have converted the official Docker container to Singularity. Here is a documentation about the Docker base container, including a table with the individual software versions of the packages installed within the container: PowerAI Docker Container.