Saving H2O models from R/Python API in Hadoop Environment

When you are using H2O in clustered environment i.e. Hadoop the machine could be different where h2o.savemodel() is trying to write the model and thats why you see the error “No such file or directory”. If you just give the path i.e. /tmp and visit the machine ID where H2O connection is initiated from R, you will see the model stored there.
Here is a good example to understand it better:
Step [1] Starting Hadoop driver in EC2 environment as below:
[ec2-user@ip-10-0-104-179 ~]$ hadoop jar h2o- -nodes 2 -mapperXmx 2g -output /usr/ec2-user/005
Open H2O Flow in your web browser:  <=== H2O is started.
Note: Above you could see that hadoop command is ran on ip address however the node where H2O server is shown as
Step [2] Connect R client with H2O
> h2o.init(ip = "", port = 54323, strict_version_check = FALSE)
Note: I have used the ip address as shown above to connect with existing H2O cluster. However the machine where I am running R client is different as its IP address is
Step [3]: Saving H2O model:
h2o.saveModel(my.glm, path = "/tmp", force = TRUE)
So when I am saving the mode it is saved at machine even when the R client was running at
ec2-user@ip-10-0-65-248 ~]$ ll /tmp/GLM*
-rw-r--r-- 1 yarn hadoop 90391 Jun 2 20:02 /tmp/GLM_model_R_1496447892009_1
So you need to make sure you have access to a folder where H2O service is running or you can save model at HDFS something similar to as below:
h2o.saveModel(my.glm, path = "hdfs://", force = TRUE)

Thats it, enjoy!!


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