Snap Machine Learning (Snap ML) is a library for training generalized linear models. It is being developed at the IBM Research - Zürich laboratory 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.
"With Snap ML you can train your machine learning model faster than you can snap your fingers!"
The Snap ML library offers two different packages:
This package offers local, single-node as well as MPI-based distributed training. The library is exposed to the user via a sklearn-like python interface.
The local version of Snap ML is designed to run on a single machine. It targets small to medium scale data that can fit inside the memory of a single machine. snap-ml-local offers GPU acceleration and supports sparse data structures. The library is exposed to the user via a Python API compatible with sklearn and can seamlessly be integrated into existing Python application. With PowerAI 1.5.4 release onwards, snap-ml-local will ship a module named pai4sk. In PowerAI 1.6.0, this module is built upon scikit-learn 0.20.1 APIs and can be used as a replacement for scikit-learn. This module will automatically fall back to CPU algorithms when snap-ml-local doesn’t support an API. Refer to the API Documentation for the current set of GPU accelerated APIs exposed by pai4sk.
It also offers distributed training of models across a cluster of machines. This enables training on large scale datasets that exceed the memory capacity of a single machine. It offers GPU acceleration and supports sparse data structures. The library is exposed to the user via a sklearn-like python interface.
- Similar to distributed pai4sk-snapml, the snap-ml-spark package offers distributed training of models across a cluster of machines. The library is exposed to the user via a spark.ml like interface and can seamlessly be integrated into existing pySpark application.
- “Snap ML: A Hierarchical Framework for Machine Learning”, C. Dünner, T. Parnell, D. Sarigiannis, N. Ioannou, A. Anghel, G. Ravi, M. Kandasamy and H. Pozidis, in Advances of Neural Information Processing Systems (NeurIPS), 2018. [bibtex]