I know this was raised before, and I saw a suggestion that using
gc.collect seemed to fix things, but I am getting I think a memory leak
in rpy2 which doesn't seem to occur in rpy. This could be how I am
coding things of course. Here is an example:
import rpy2.robjects as robjects
from numpy import *
import rpy2.robjects.numpy2ri
import gc
robjects.r('set.seed(99813)')
aa = array(robjects.r.runif(205))
bb = arange(0,1,step=1/205.0)robjects.r('library(locfit)')
lfraw = robjects.globalEnv.get("locfit.raw")
##and try this many times:
for i in range(90000):
if i%1000==0:
gc.collect()
print i
res = lfraw(bb,aa)
If I monitor this using top, the memory use steadily increases. This
does not happen with the equivalent code in rpy. We would like to use
some R functions many times, so the memory leak is of concern. Does
anyone have any insight to this problem -- perhaps I can modify my code
to avoid it, or should we go back to rpy?
I am using rpy2 2.0.3 on linux on 64bit suse running R-2.8.0
Thanks
Robert
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