I have a fairly large codebase written in numba, and I have noticed that when the cache is enabled for a function calling another numba compiled function in another file, changes in the called function are not picked up when the called function is changed. The situation occurs when I have two files:
testfile2:
import numba
@numba.njit(cache=True)
def function1(x):
return x * 10
testfile:
import numba
from tests import file1
@numba.njit(cache=True)
def function2(x, y):
return y + file1.function1(x)
If in a jupyter notebook, I run the following:
# INSIDE JUPYTER NOTEBOOK
import sys
sys.path.insert(1, "path/to/files/")
from tests import testfile
testfile.function2(3, 4)
>>> 34 # good value
However, if I change then change testfile2 to the following:
import numba
@numba.njit(cache=True)
def function1(x):
return x * 1
Then I restart the jupyter notebook kernel and rerun the notebook, I get the following
import sys
sys.path.insert(1, "path/to/files/")
from tests import testfile
testfile.function2(3, 4)
>>> 34 # bad value, should be 7
Importing both files into the notebook has no effect on the bad result. Also, setting cache=False only on function1 also has no effect. What does work is setting cache=False on all njit'ted functions, then restarting the kernel, then rerunning.
I believe that LLVM is probably inlining some of the called functions and then never checking them again.
I looked in the source and discovered there is a method that returns the cache object numba.caching.NullCache(), instantiated a cache object and ran the following:
cache = numba.caching.NullCache()
cache.flush()
Unfortunately that appears to have no effect.
Is there a numba environment setting, or another way I can manually clear all cached functions within a conda env? Or am I simply doing something wrong?
I am running numba 0.33 with Anaconda Python 3.6 on Mac OS X 10.12.3.