Create large random boolean matrix with numpy

The problem is your RAM, the values are being stored in memory as it’s being created. I just created this matrix using this command:

np.random.choice(a=[False, True], size=(N, N), p=[p, 1-p])

I used an AWS i3 instance with 64GB of RAM and 8 cores. To create this matrix, htop shows that it takes up ~20GB of RAM. Here is a benchmark in case you care:

time np.random.choice(a=[False, True], size=(N, N), p=[p, 1-p])

CPU times: user 18.3 s, sys: 3.4 s, total: 21.7 s
Wall time: 21.7 s


 def mask_method(N, p):
    for i in range(N):
        mask[i] = np.random.choice(a=[False, True], size=N, p=[p, 1-p])
        if (i % 100 == 0):
            print(i)

time mask_method(N,p)

CPU times: user 20.9 s, sys: 1.55 s, total: 22.5 s
Wall time: 22.5 s

Note that the mask method only takes up ~9GB of RAM at it’s peak.

Edit: The first method flushes the RAM after the process is done where as the function method retains all of it.