CODEWITHSUNDEEP
pythonmemorynumpyscipy
tylerthemiler
tylerthemiler

Reputation: 5746

Python/Numpy MemoryError

Basically, I am getting a memory error in python when trying to perform an algebraic operation on a numpy matrix. The variable u, is a large matrix of double (in the failing case its a 288x288x156 matrix of doubles. I only get this error in this huge case, but I am able to do this on other large matrices, just not this big). Here is the Python error:

 Traceback (most recent call last):

 File "S:\3D_Simulation_Data\Patient SPM Segmentation\20 pc
t perim erosion flattop\SwSim.py", line 121, in __init__
   self.mainSimLoop()

 File "S:\3D_Simulation_Data\Patient SPM Segmentation\20 pc
t perim erosion flattop\SwSim.py", line 309, in mainSimLoop
   u = solver.solve_cg(u,b,tensors,param,fdHold,resid) # Solve the left hand si
de of the equation Au=b with conjugate gradient method to approximate u

 File "S:\3D_Simulation_Data\Patient SPM Segmentation\20 pc
t perim erosion flattop\conjugate_getb.py", line 47, in solv
e_cg

u = u + alpha*p

MemoryError

u = u + alpha*p is the line of code that fails.

alpha is just a double, while u and r are the large matrices described above (both of the same size).

I don't know that much about memory errors especially in Python. Any insight/tips into solving this would be very appreciated!

Thanks

Upvotes: 26

Views: 76358

Answers (3)

DaveP
DaveP

Reputation: 7102

Another tip I have found to avoid memory errors is to manually control garbage collection. When objects are deleted or go our of scope, the memory used for these variables isn't freed up until a garbage collection is performed. I have found with some of my code using large numpy arrays that I get a MemoryError, but that I can avoid this if I insert calls to gc.collect() at appropriate places.

You should only look into this option if using "op=" style operators etc doesn't solve your problem as it's probably not the best coding practice to have gc.collect() calls everywhere.

Upvotes: 12

luispedro
luispedro

Reputation: 7014

Rewrite to

p *= alpha
u += p

and this will use much less memory. Whereas p = p*alpha allocates a whole new matrix for the result of p*alpha and then discards the old p; p*= alpha does the same thing in place.

In general, with big matrices, try to use op= assignment.

Upvotes: 52

Benjamin Bannier
Benjamin Bannier

Reputation: 58547

Your matrix has 288x288x156=12,939,264 entries, which for double could come out to 400MB in memory. numpy throwing a MemoryError at you just means that in the function you called the memory needed to perform the operation wasn't available from the OS.

If you can work with sparse matrices this might save you a lot of memory.

Upvotes: 7

Related Questions