December 23rd, 2017

H2O4GPU Hands-On Lab (Video) + Updates

RSS icon RSS Category: GPU, H2O4GPU
Kmeans on h204gpu

Deep learning algorithms have benefited significantly from the recent performance gains of GPUs. However, it has been uncertain whether GPUs can speed up powerful classical machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, clustering, and singular value decomposition.
Today I’d love to share another interesting presentation from #H2OWorld focused on H2O4GPU.
H2O4GPU is a GPU-optimized machine learning library with a Python scikit-learn API tailored for enterprise AI. The library includes all the CPU algorithms from scikit-learn and also has selected algorithms that benefit greatly from GPU acceleration.
In the video below, Jon McKinney, Director of Research at, discussed the GPU-optimized machine learning algorithms in H2O4GPU and showed their speed in a suite of benchmarks against scikit-learn run on CPUs.

A few recent benchmarks include:
Kmeans on h204gpu
kmeans clustering graph
Gradient boosting machine
Gradient boosting machine graph
We’re always receiving helpful feedback from the community and making updates.
Exciting updates to expect in Q1 2018 include:

  • Aggregator
  • Kalman Filters
  • K-nearest neighbors
  • Quantiles
  • Sort

If you’d like to learn more about H2O4GPU, I invite you to explore these helpful links:

Happy Holidays!

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