September 26th, 2017

H2O.ai Releases H2O4GPU, the Fastest Collection of GPU Algorithms on the Market, to Expedite Machine Learning in Python

Category: GBM, GLM, GPU, k-Means
Gradient Linear Model (GLM)

H2O4GPU is an open-source collection of GPU solvers created by H2O.ai. It builds on the easy-to-use scikit-learn Python API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn with support for GPUs on selected (and ever-growing) algorithms. H2O4GPU inherits all the existing scikit-learn algorithms and falls back to CPU algorithms when the GPU algorithm does not support an important existing scikit-learn class option. It utilizes the efficient parallelism and high throughput of GPUs. Additionally, GPUs allow the user to complete training and inference much faster than possible on ordinary CPUs.
Today, select algorithms are GPU-enabled. These include Gradient Boosting Machines (GBM’s), Generalized Linear Models (GLM’s), and K-Means Clustering. Using H2O4GPU, users can unlock the power of GPU’s through the scikit-learn API that many already use today. In addition to the scikit-learn Python API, an R API is in development.
Here are specific benchmarks from a recent H2O4GPU test:

  • More than 5X faster on GPUs as compared to CPUs
  • Nearly 10X faster on GPUs
  • More than 40X faster on GPUs

“We’re excited to release these lightning-fast H2O4GPU algorithms and continue H2O.ai’s foray into GPU innovation,” said Sri Ambati, co-founder and CEO of H2O.ai. “H2O4GPU democratizes industry-leading speed, accuracy and interpretability for scikit-learn users from all over the globe. This includes enterprise AI users who were previously too busy building models to have time for what really matters: generating revenue.”
“The release of H2O4GPU is an important milestone,” said Jim McHugh, general manager and vice president at NVIDIA. “Delivered as part of an open-source platform it brings the incredible power of acceleration provided by NVIDIA GPUs to widely-used machine learning algorithms that today’s data scientists have come to rely upon.”
H2O4GPU’s release follows the launch of Driverless AI, H2O.ai’s fully automated solution that handles data science operations — data preparation, algorithms, model deployment and more — for any business needing world-class AI capability in a single product. Built by top-ranking Kaggle Grandmasters, Driverless AI is essentially an entire data science team baked into one application.
Following is some information on each GPU enabled algorithm as well as a roadmap.
Gradient Linear Model (GLM)

  • Framework utilizes Proximal Graph Solver (POGS)
  • Solvers include Lasso, Ridge Regression, Logistic Regression, and Elastic Net Regularization
  • Improvements to original implementation of POGS:
    • Full alpha search
    • Cross Validation
    • Early Stopping
    • Added scikit-learn-like API
    • Supports multiple GPU’s

Gradient Linear Model (GLM)
Gradient Boosting Machines (Please check out Rory’s blog on Nvidia Dev Blogs for a more detailed write-up on Gradient Boosted Trees on GPUs)

  • Based on XGBoost
  • Raw floating point data — binned into quantiles
  • Quantiles are stored as compressed instead of floats
  • Compressed quantiles are efficiently transferred to GPU
  • Sparsity is handled directly with high GPU efficiency
  • Multi-GPU enabled by sharing rows using NVIDIA NCCL AllReduce

Gradient Boosting Machines
k-Means Clustering

  • Based on NVIDIA prototype of k-Means algorithm in CUDA
  • Improvements to original implementation:
    • Significantly faster than scikit-learn implementation (50x) and other GPU implementations (5-10x)
    • Supports multiple GPUs

k-Means Clustering
H2O4GPU combines the power of GPU acceleration with H2O’s parallel implementation of popular algorithms, taking computational performance levels to new heights.
To learn more about H2O4GPU click here and for more information about the math behind each algorithm, click here.

Tags

Leave a Reply

H2O.ai Automatic Machine Learning on Red Hat OpenShift Container Platform Delivers Data Science Ease and Flexibility at Scale

Last week at Red Hat Summit in Boston, Sri Ambati, CEO and Founder, demonstrated how

May 14, 2019 - by Vinod Iyengar
6 Tips to Having it All

I posted this blog on Medium two years ago, thought I'd share a slight rework

May 12, 2019 - by Ingrid Burton
AI/ML Projects — Don’t get stymied in the last mile

Data Scientists build AI/ML models from data, and then deploy it to production – in

May 3, 2019 - by Karthik Guruswamy
Hortifrut uses AI to Determine the Freshness of Blueberries

Who doesn’t love sweet, delicious blueberries? Providing a steady supply of beautiful, tasty berries to the

May 2, 2019 - by Ingrid Burton
Fallback Featured Image
Can Your Machine Learning Model Be Hacked?!

I recently published a longer piece on security vulnerabilities and potential defenses for machine learning

May 2, 2019 - by Patrick Hall
Fallback Featured Image
H2O Driverless AI Updates

We are excited to announce the new release of H2O Driverless AI with lots of improved

April 25, 2019 - by Venkatesh Yadav, VP Customer Success

Join the AI Revolution

Subscribe, read the documentation, download or contact us.

Subscribe to the Newsletter

Start Your 21-Day Free Trial Today

Get It Now
Desktop img