michalkurka/h2o-parallel-grid-search-benchmark
Parallel Grid Search benchmark - H2O Machine Learning
This project helps data scientists and machine learning engineers speed up their model development process when using H2O's machine learning platform. By building multiple machine learning models in parallel during a grid search, instead of one at a time, it significantly reduces the total training time. This is especially useful when experimenting with many different model configurations.
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Use this if you are an H2O user looking to accelerate the hyperparameter tuning phase of your machine learning projects, especially with complex or numerous models.
Not ideal if your H2O cluster has very limited memory per node, as parallel model building increases memory consumption.
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Jan 14, 2020
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