H2O4GPU is an open-source collection of GPU solvers created by H2O.ai. It builds on the easy-to-use scikit-learn 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.

Today, select algorithms are GPU-enabed. These include Gradient Boosting Machines (GBM’s), Generalized Linear Models (GLM’s), and K-Means Clustering.

H2O4GPU ROAD MAP

Currently Available: 

• GLM (POGS)
  
  
• Pyton API for scoring and training
  
  
• GBM
  
  
• Inference on GPU (GLM)
  
  
• Random Forest
  
  
• Inference on GPU (GBM)
  
  
• k-Means clustering
  
  
• Scikit learn API for compatibility
  
  
• PCA 
  
  
• R API for training and scoring 
  
  
• SVD 

Coming Q2 2018

• k-Nearest Neighbors 
  
  
• Matrix Factorization 
  
  
• Factorization Machines 
  
  
• Quantiles 
  
  
• Kalman Filters 
  
  
• Sort 
  
  
• Aggregator 
  
  
• API Support: 
  • GOAI API support 
    
    
  • Data.table 
    
     
• Performance & Scalability: 
  • Multi machine

Q4 2018

• Kernel Methods 
  
  
• Recommendation Engines – Non-Negative Matrix Factorization Recommendation Engines – Bayesian Neural Nets 
  
  
• MCMC Solver 
  
  
• Time Series 
  
  
• SVM 
  
  
• Text Analysis-TF-IDF 
  
  
• Text Analysis – Word2Vec 
  
  
• Text Analysis -0oc2Vec 
  
  
• Automatic K for K-means 
  
  
• H2O GLM – Lasso 
  
  
• Simulation Techniques 
  
  
• Sampling Techniques 
  
  
• Domain Specific Algorithms: 
  • Life Sciences 
    
    
  • Financial Services Underwriting 
    
    
  • Sampling Techniques

Gradient Linear Model (GLM) 

• Framework utilizes Proximal Graph Solver (POGS)
  
  
• Solvers include Lasso, Ridge Regression, Logistic Regression, and Elastic Net Regularization

SPECIFICATIONS

Software

• PC with Ubuntu 16.04+
  
  
• Install CUDA with bundled display drivers CUDA 8 or CUDA 9

Hardware

• Nvida GPU with Compute Capability >= 3.5

• Improvements to original implementation of POGS:
  • Full alpha search
    
    
  • Cross Validation
    
    
  • Early Stopping
    
    
  • Added scikit-learn-like API
    
    
  • Supports multiple GPU’s

Gradient Boosting Machines 

• 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 to high GPU efficiently
  
  
• Multi-GPU enabled by sharing rows using NVIDIA NCCL AllReduce

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 GPU’s