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 predictors and 1 response).

I use the R bindings to control the cluster and the RF call is as follows

model=h2o.randomForest(x=x,
                       y=y,
                       ignore_const_cols=TRUE,
                       training_frame=train_data,
                       seed=1234,
                       mtries=7,
                       ntrees=2000,
                       max_depth=15,
                       min_rows=50,
                       stopping_rounds=3,
                       stopping_metric="MSE",
                       stopping_tolerance=2e-5)

For the 3-node cluster (c4.8xlarge, enhanced networking turned on), this takes about 240sec; the CPU utilization is between 10-20%; RAM utilization is between 20-30%; network transfer is between 10-50MByte/sec (in and out). 300 trees are built until early stopping kicks in.

On a single-node cluster, I can get the same results in about 80sec. So, instead of an expected 3-fold speed up, I get a 3-fold slow down for the 3-node cluster.

I did some research and found a few resources that were reporting the same issue (not as extreme as mine though). See, for instance: https://groups.google.com/forum/#!topic/h2ostream/bnyhPyxftX8

Specifically, the author of http://datascience.la/benchmarking-random-forest-implementations/ notes that

While not the focus of this study, there are signs that running the distributed random forests implementations (e.g. H2O) on multiple nodes does not provide the speed benefit one would hope for (because of the high cost of shipping the histograms at each split over the network).

Also https://www.slideshare.net/0xdata/rf-brighttalk points at 2 different DRF implementations, where one has a larger network overhead.

I think that I am running into the same problems as described in the links above. How can I improve h2o's DRF performance on a multi-node cluster? Are there any settings that might improve runtime? Any help highly appreciated!

Answer 1

If your Random Forest is slower on a multi-node H2O cluster, it just means that your dataset is not big enough to take advantage of distributed computing. There is an overhead to communicate between cluster nodes, so if you can train your model successfully on a single node, then using a single node will always be faster.

Multi-node is designed for when your data is too big to train on a single node. Only then, will it be worth using multiple nodes. Otherwise, you are just adding communication overhead for no reason and will see the type of slowdown that you observed.

If your data fits into memory on a single machine (and you can successfully train a model w/o running out of memory), the way to speed up your training is to switch to a machine with more cores. You can also play around with certain parameter values which affect training speed to see if you can get a speed-up, but that usually comes at a cost in model performance.

Answer 2

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.)