assigning a destination frame to a prediction in h2o.ai - variable-assignment

Question

I like h2o.ai for machine learning using R.
https://cran.r-project.org/web/packages/h2o/h2o.pdf
I like random forests, but I'm making a few thousand predictions in a loop.
It is spamming up my memory with things like this:
I can't afford to keep them all in memory. I'm making my very nice computer work very hard. That means it doesn't have the capacity to hold all the balls in the air at once.
If I could assign a destination frame name to the prediction then each new one would overwrite the old ones.
How do I assign a destination frame name when I am performing "h2o.predict" on an object?
Things that I have tried that did not work:
h2o.predict(object = rf.hex, newdata = test.hex, predictions_frame = "predict.hex")
h2o.predict(object = rf.hex, newdata = test.hex, destination_frame = "predict.hex")
h2o.predict(object = rf.hex, newdata = test.hex, model_id = "predict.hex")

Answer 1

There is no way that I am aware of.
But as an alternative, inside your loop, you could call h2o.rm() on the return value from h2o.predict(). It is worth calling h2o.gc() as well. Something like:
for(data in alldata){
# ... prepare newdata
p = h2o.predict(model, newdata)
# ... do something with p here
h2o.rm(p)
h2o.rm(newdata) # If also not needed any more
h2o.gc()
}
Aside: you said "I'm making a few thousand predictions in a loop". Assuming they were all against the same model, remember you can batch them up, and give all thousand predictions in a single newdata dataframe. One call to h2o.predict() with 1000 entries is much more efficient than making 1000 h2o.predict() calls, for one newdata entry at a time.