Can H2o AutoML benefit from a GPU instance on Sagemaker platform?

2
votes

I'm running some projects with H2o AutoML using Sagemaker notebook instances, and I would like to know if H2o AutoML can benefit from a GPU Sagemaker instance, if so, how should I configure the notebook?

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Can you share more about how you are using H2O AutoML and what version of H2O it is? – Erin LeDell
@ErinLeDell, for sure! I believe it's the 3.2 version of H2O AutoML. I work as a management consultant, as part of my job I use ML to predict the likelihood of events as well as predicting continuous variables using regression. Addiotanly, I've been using ML for forecasting. Specifically, I'm in a project which I need to predict the likelihood of an investment portfolio to fall in a window of time. In this project, I'll need to train a huge dataset, and then, I thought I could take advantage of GPU processing to reduce the training time. Usually, I use notebooks on Sagemaker. – Marcel Mendes Reis
You might want to double-check the version number, we are at 3.26 right now, so it's probably something close to that. – Erin LeDell
Yes, @ErinLeDell I realized it's the 3.26. – Marcel Mendes Reis

1 Answers

1
votes

H2O AutoML contains a handful of algorithms and one of them is XGBoost, which has been part of H2O AutoML since H2O version 3.22.0.1. XGBoost is the only GPU-capable algorithm inside of H2O AutoML, however, a lot of the models that are trained in AutoML are XGBoost models, so it still can be useful to utilize a GPU. Keep in mind that you must use H2O 3.22 or above to use this feature.

My suggestion is to test it on a GPU-enabled instance and compare the results to a non-GPU instance and see if it's worth the extra cost.