Improve h2o DRF runtime on a multi-node cluster

Question

I am currently running h2o\'s DRF algorithm an a 3-node EC2 cluster (the h2o server spans across all 3 nodes). My data set has 1m rows and 41 columns (40 predic

Answer 1

As Erin says, often adding more nodes just adds the capability for bigger data sets, not quicker learning. Random forest might be the worst; I get fairly good results with deep learning (e.g. 3x quicker with 4 nodes, 5-6x quicker with 8 nodes).

In your comment on Erin's answer you mention the real problem is you want to speed up hyper-parameter optimization? It is frustrating that h2o.grid() doesn't support building models in parallel, one on each node, when the data will fit in memory on each node. But you can do that yourself, with a bit of scripting: set up one h2o cluster on each node, do a grid search with a subset of hyper-parameters on each node, have them save the results and models to S3, then bring the results in and combine them at the end. (If doing a random grid search, you can run exactly the same grid on each cluster, but it might be a good idea to explicitly use a different seed on each.)