Lightning Fast AI with GPUs
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 H2O4GPUA
- Optimized for GPU Performance
- Broad Selection of GPU Enabled Algorithm
- Builds on scikit-learn Python API
- Available R API
Fully optimized to run on the latest-generation NVIDIA® Volta architecture GPUs, the NVIDIA Tesla® V100 and CUDA 9 software.
Available algorithms include Gradient Boosting Machines (GBM’s), Generalized Linear Models (GLM’s), K-Means Clustering, SVD, PCA, K-means and XGBoost to deliver models and results faster to the business.
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.
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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