Introduction to Snap Machine Learning

Generalized linear models, such as logistic regression and support vector machines, remain some of the most widely-used techniques in the machine learning field. Their enduring popularity can be attributed to their desirable theoretical properties, effective training algorithms, and relative ease of interpretability. In this talk we will introduce Snap Machine Learning: a new library for fast training of such models, that is designed to enable new real-time and large-scale applications. The library was designed from the ground up with performance in mind. It exploits parallelism at three different levels: across multiple machines in a network, across heterogeneous compute nodes within a machine (e.g. CPU and GPU), as well as the massive parallelism offered by modern GPUs. In this talk we will review this new architecture and give examples of how the library can be used via the various APIs that are provided (e.g. Python, Apache Spark, MPI). Finally, we will present benchmarking results using the publicly available Terabyte Click Logs dataset (from Criteo Labs) and show that Snap Machine Learning can train a logistic regression classifier in 1.53 minutes, 46x faster than any of the results that have been previously reported using the same dataset.