H2O4GPU

H2O4GPU is a collection of GPU solvers by H2Oai. It builds upon the easy-to-use Scikit-Learn API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for sklearn (i.e. import h2o4gpu as sklearn) with support for GPUs on selected (and ever-growing) algorithms. H2O4PU inherits all the existing Scikit-learn algorithms and falls-back to CPU aglorithms when the GPU algorithm does not support an importnat existing Scikit-learn class option.

Requirements

• PC with Ubuntu 16.04+

• Install CUDA with bundled display drivers CUDA 8 or CUDA 9

• Nvidia GPU with Compute Capability>=3.5 Capability Lookup.

Installation

Add to ~/.bashrc or environment (set appropriate paths for your OS):

export CUDA_HOME=/usr/local/cuda
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64/:$CUDA_HOME/lib/:$CUDA_HOME/extras/CUPTI/lib64

Download the Python wheel file for python 3.6 and CUDA 8:

• (TBA) Stable
• Bleeding edge

Start a fresh pyenv or virtualenv session.

Install the Python wheel file. NOTE: If you don't use a fresh environment, this will overwrite your py3nvml and xgboost installations to use our validated versions.

pip install h2o4gpu-0.0.3-py36-none-any.whl

Test your installation

import h2o4gpu
import numpy as np

X = np.array([[1.,1.], [1.,4.], [1.,0.]])
model = h2o4gpu.KMeans(n_clusters=2).fit(X)
model.fit(X).cluster_centers_

For more examples check our Jupyter notebook demos.

Plans and RoadMap

Vision is to develop fast GPU algorithms to complement the CPU algorithms in scikit-learn while keeping full scikit-learn API compatibility and scikit-learn CPU algorithm capability. The python module is to be used as a drop-in-replacement for scikit-learn that has the full functionality of sklearn's CPU algorithms. Functions and classes will be gradually overriden by GPU-enabled algorithms (unless n_gpu=0 is set and we have no CPU algorithm except scikit-learn's). The CPU algorithms and code initially will be sklearn, but gradually those may be replaced by faster open-source codes like those in Intel DAAL.

This vision is currently accomplished by using the open-source scikit-learn and xgboost and overriding scikit-learn calls with our own GPU versions. In cases when our GPU class is currently incapable of an important scikit-learn feature, we revert to the scikit-learn class.

Another primary goal is to support all operations the GPU via the GOAI initiative. This involves ensuring the GPU algorithms can take and return GPU pointers to data instead of going back to the host. In scikit-learn API language these are called fit_ptr, predict_ptr, transform_ptr, etc., where ptr stands for memory pointer.

Solver Classes

Among others, the solver can be used for the following classes of problems

• GLM: Lasso, Ridge Regression, Logistic Regression, Elastic Net Regulariation,
• KMeans
• Gradient Boosting Machine (GBM) via XGBoost

Planned:

• GLM: Linear SVM, Huber Fitting, Total Variation Denoising, Optimal Control, Linear Programs and Quadratic Programs.
• SVD, PCA

Contributing

Please refer to our CONTRIBUTING.md and DEVEL.md for instructions on how to build and test the project and how to contribute.

GitHub issues are used only for bugs, feature and enhancement discussion/tracking.

Questions

Please ask all h2o4gpu related questions either on StackOverflow or our Gitter,

Troubleshooting.

FAQ.

References

1. Parameter Selection and Pre-Conditioning for a Graph Form Solver -- C. Fougner and S. Boyd
2. Block Splitting for Distributed Optimization -- N. Parikh and S. Boyd
3. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers -- S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein
4. Proximal Algorithms -- N. Parikh and S. Boyd

Copyright

Copyright (c) 2017, H2O.ai, Inc., Mountain View, CA
Apache License Version 2.0 (see LICENSE file)


This software is based on original work under BSD-3 license by:

Copyright (c) 2015, Christopher Fougner, Stephen Boyd, Stanford University
All rights reserved.

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