Getting started with Snap Machine Learning (Snap ML)

This release of PowerAI includes Technology preview of 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 a large number of classical machine learning models and scales gracefully to data sets with billions of examples and/or features. It offers distributed training, GPU acceleration and supports sparse data structures.

The Snap ML library offers three different packages:

snap-ml-local

snap-ml-local is used for machine learning on a single machine.

For information on snap-ml-local, see /opt/DL/snap-ml-local/doc/README.md

snap-ml-mpi

snap-ml-mpi is used for distributed training of machine learning models across a cluster of machines.

For information about snap-ml-mpi, see /opt/DL/snap-ml-mpi/doc/README.md

snap-ml-spark

Similar to snap-ml-mpi, the snap-ml-spark package offers distributed training of models across a cluster of machines. The library is exposed to the user through a spark.ml-like interface and can seamlessly be integrated into existing pySpark application.

For information on snap-ml-spark, see /opt/DL/snap-ml-spark/doc/README.md