Why as.h2o(dataframe) takes long time to send data to cloud and breaks rstudio?



I created a basic h2o cluster (4 vcpus, 15gb) on google cloud and logged into it from R with h2o.connect(external ip, port). After using fread() to read a large csv file (64mb, which my ubuntu did not have enough memory to run models on), I did:
as.h2o(/* output of fread */) - but this breaks the R studio. Can you pl point out what i am doing wrong here?

I tried to follow the analyticsvidhya tutorial to run large datasets from rstudio on google cloud using h2o cluster.

The data is a gene expression set with 50K+ features and a few 100’s of samples.


This has been resolved using h2o.importFile() as mentioned in this SO post: