Fastest way of generating numpy arrays or randomly distributed 0s and 1s

I need to generate masks for dropout for a specific neural network. I am looking at the fastest way possible to achieve this using numpy (CPU only).

I have tried:

def gen_mask_1(size, p=0.75):
    return np.random.binomial(1, p, size)


def gen_mask_2(size, p=0.75):
    mask = np.random.rand(size)
    mask[mask>p]=0
    mask[mask!=0]=1
    return mask

where p is the probability of having 1

The speed of these two approaches is comparable.

%timeit gen_mask_1(size=2048)
%timeit gen_mask_2(size=2048)

45.9 µs ± 575 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
47.4 µs ± 372 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Are there faster methods?