19 Oct 2021
Release
5 minute read

Snap ML pushes AutoAI to deliver 4x-faster automated machine learning on IBM Cloud

Research project Snap ML’s recent integration into AutoAI is already delivering dramatic acceleration of new use-cases on large datasets for Watson Studio users.

Research project Snap ML’s recent integration into AutoAI is already delivering dramatic acceleration of new use-cases on large datasets for Watson Studio users.

AutoAI is IBM’s flagship product for automated machine learning–a software library for fast training and inference of classical machine learning (ML) models on modern computing systems.

It generates ML pipelines that perform data cleaning, data pre-processing, feature engineering, model selection, hyper-parameter tuning, and more on Watson Studio users’ datasets.

When those datasets get large, searching for the best pipeline can take a long time… But not with Snap ML.

On a particular dataset related to a PoC with a big client, we showed that our integration effort cut the runtime from 29 hours to only 22 minutes, a whopping 79 times speed-up.

The AI challenges that AutoAI with Snap ML are addressing.
Figure 1:
The AI challenges addressed by AutoAI and Snap ML, and our five key design goals.

Classical ML techniques like those offered by AutoAI are very effective, particularly when applied to tabular datasets. According to the Kaggle State of Data Science survey, methods such as logistic regression, random forests and boosted trees remain the most frequently used ML algorithms in industry.

Our team designs algorithms for training models better suited to the underlying systems on which they run—be that in a cloud instance with a handful of vCPUs or a powerful on-prem server with a large number of CPUs and GPUs.

Using these smart algorithms, Snap ML can achieve significantly faster training and inference in both cloud and on-prem compute environments, without sacrificing any model accuracy. And by benchmarking this new version of AutoAI across a collection of large tabular datasets (from Kaggle), we’ve demonstrated that this integration effort resulted in a Our research in this area has been published at top conferences, such as NeurIPS, ICML and AAAI. And it has helped to define the state-of-the-art in classical ML algorithms. For more, watch our talk on Snap ML at NeurIPS 2021.4x faster runtime (on average).

Picking Snap ML is a… snap!

AutoAI searches for the best pipeline using a complex optimization algorithm. It involves repeated training of different machine learning models with various hyper-parameter configurations and feature engineering schemes. The machine learning models it uses as components are provided by a variety of different Python software frameworks including scikit-learn, XGBoost and LightGBM.

By enhancing AutoAI to consume models from Snap ML, alongside these other frameworks, our goal was to pass on Snap ML's speed-ups to AutoAI users and deliver end-to-end acceleration of the search for the best pipeline.

Sample list of Snap ML options within AutoAI.
Figure 2:
A list of ML algorithms used within AutoAI, now including Snap ML.

Before Snap ML integration, AutoAI always chose the ML models that gave the highest accuracy. This often resulted in a situation with two models, say a random forest from scikit-learn and a random forest from Snap ML, which get identical or very similar accuracy. But despite the Snap ML model being dramatically faster, there was no guarantee that AutoAI would pick the Snap ML model.

To ensure that AutoAI picked the fast Snap ML model over the equivalent scikit-learn model, it was necessary to develop a new criterion for selecting models inside AutoAI. So we’ve developed a method that picks models based on accuracy and runtime, to ensure that models that are both accurate and fast get selected and delivered to the customer.

Simple options for AutoAI algorithm selection.
Figure 3:
Simple options for AutoAI algorithm selection.

This acceleration can drive entirely new use cases of AutoAI, such as performing pipeline search on large datasets and a large collection of smaller datasets.

Installation is a Snap (ML), too

AutoAI gives our customers the option to export the learned ML pipeline to a Jupyter notebook, which can be inspected, re-trained or deployed on Linux/x86, Linux/IBM Power, Windows and MacOS. It was crucial to make the installation process of Snap ML on all these platforms as straightforward as possible.

For that, we created binary Python packages known as “wheels” for all these platforms and deployed them to the Python package index (PyPI). As a result, anyone can now install Snap ML just by using “pip install snapml.”

And our team also provides binary packages for IBM Power and IBM Z systems, which has helped us to massively improve the reach of Snap ML, independently of AutoAI. As of October 1, 2021, Snap ML has been downloaded 49,825 times from PyPI.

Get started with Snap ML with examples and tutorials on GitHub.

Date

19 Oct 2021

Authors

Tags

Share

Notes

  1. Note 1Our research in this area has been published at top conferences, such as NeurIPS, ICML and AAAI. And it has helped to define the state-of-the-art in classical ML algorithms. For more, watch our talk on Snap ML at NeurIPS 2021. ↩︎