Deliver Business Innovation with GPU-accelerated Machine Learning
H2O4GPU is an open source, GPU-accelerated machine learning package with APIs in Python and R that allows anyone to take advantage of GPUs to build advanced machine learning models. A variety of popular algorithms are available including Gradient Boosting Machines (GBM’s), Generalized Linear Models (GLM’s), and K-Means Clustering. Our benchmarks found that training machine learning models on GPUs was up to 40x faster than CPU based systems.
Key Features of H2O4GPU
Optimized for GPU Performance
Fully optimized to run on the latest-generation NVIDIA® Volta architecture GPUs, the NVIDIA Tesla® V100 and CUDA 9 software.
Broad Selection of GPU Enabled Algorithms
Available algorithms include Gradient Boosting Machines (GBM’s), Generalized Linear Models (GLM’s), and K-Means Clustering, SVD, PCA, K-means and XGBoost to deliver models and results faster to the business.
Builds on scikit-learn Python API
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.
Available R API
A new R API brings the benefits of GPU-accelerated machine learning to the R user community. The R package is a wrapper around the H2O4GPU Python package, and the interface follows standard R conventions for modeling.
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Featured Use Cases
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