Snap machine learning

A library that provides high-speed training of popular machine learning models on modern CPU/GPU computing systems.

Overview

Optimizing Machine Learning

Accelerate popular Machine Learning algorithms through system awareness, and hardware/software differentiation
Develop novel Machine Learning algorithms with best-in-class accuracy for business-focused applications

AI in Business – Challenges

Snap Machine Learning (Snap ML in short) is a library for training and scoring traditional machine learning models. Such traditional models power most of today's machine learning applications in business and are very popular among practitioners as well (see the 2019 Kaggle survey for details). Snap ML has been designed to address some of the biggest challenges that companies and practitioners face when applying machine learning to real use cases. These challenges are listed below.

Speed

Train and re-train on new data online
Large parameter, model searches
Make fast decisions

Efficiency

Use resources judiciously
Less resources means less $
On-prem and in the cloud

Accuracy

Make accurate decisions or predictions
Cost savings (e.g. card fraud), higher revenue (e.g. portfolio allocation)

Data Size

Learn from all available data
More data, better models, higher accuracy
Handle big data efficiently

Snap ML is

Fast

Multi-threaded CPU solvers as well as GPU and multi-GPU solvers that offer significant acceleration over established libraries.

Scalable

Distributed solvers (for generalized linear models currently) that scale gracefully to train TB-scale datasets in mere seconds.

Accurate

A novel gradient boosting machine that achieves state-of-the-art generalization accuracy over a majority of datasets.

Resource-efficient

Ability to complete large training jobs in less resources, with high resource utilization.

Consumable

Familiar Python scikit-learn APIs for single-server solvers and Apache Spark API for distributed solvers.

Supported Machine Learning Models

Generalized Linear Models

State‐of‐the‐art solvers on multi‐core, multi‐socket CPUs. Twice‐parallel, asynchronous stochastic coordinate descent (TPA‐SCD) for training linear models on GPUs.

Linear Regression
Logistic Regression
SVM

Tree-based Models

Memory‐efficient breadth‐first search algorithm for training of decision trees, random forests and gradient boosting machines.

Decision Trees
Random Forest

Gradient Boosting

Heterogeneous boosting machine that employs multiple classes of base learners, versus only decision trees.

Boosting Machine

Seamless acceleration of scikit-learn applications

Snap ML offers very powerful, multi‐threaded CPU solvers, as well as efficient GPU solvers. Here is a comparison of runtime between training several popular ML models in scikit‐learn and in Snap ML (both in CPU and GPU). Acceleration of up to 100x can often be obtained, depending on model and dataset.

At the same time, the above acceleration is almost seamless to the user. For data scientists using Python, only minimal changes are needed to their existing code to take advantage of Snap ML. Here is an example of using a Random Forest model in both scikit‐learn as well as Snap ML.

Gradient Boosting Machine

Gradient Boosting models comprise an ensemble of decision trees, similar to a random forest (RF). Although Deep neural networks achieve state-of-the-art accuracy on image, audio and NLP tasks, on structured datasets Gradient Boosting usually out-performs all other models in terms of accuracy. Some of the most popular Boosting libraries are XGBoost, LightGBM and CatBoost. Snap ML introduces SnapBoost, which targets high generalization accuracy through a stochastic combination of base learners, including decision trees and Kernel ridge regression models. Here are some benchmarks of SnapBoost against LightGBM and XGBoost, comparing accuracy across a collection of 48 datasets. SnapBoost learns a better model in about 2-out-3 of the datasets tested.