Performance difference between numpy.random and random.random in Python

I want to see what random number generator package is faster in my neural network.

I am currently changing a code from github, in which both numpy.random and random packages are used to generate random integers, random choices, random samples etc.

The reason that I am changing this code is that for research purposes I would like to set a global seed to be able to compare accuracy performance for different settings of hyperparameters. The problem is that at this moment I have to set 2 global seeds, both for the random package and for the numpy package. Ideally, I would like to set only one seed as drawings from two sequences of random number generators might become correlated more quickly.

However, I do not know what package will perform better (in terms of speed): numpy or random. So I would like to find seeds for both packages that correspond to exactly the same Mersenne Twister sequence. In that way, the drawings for both models are the same and therefore also the number of iterations in each gradient descent step are the same, leading to a difference in speed only caused by the package I use.

I could not find any documentation on pairs of seeds that end up in the same random number sequence for both packages and also trying out all kind of combinations seems a bit cumbersome.

I have tried the following:

np.random.seed(1)
numpy_1=np.random.randint(0,101)
numpy_2=np.random.randint(0,101)
numpy_3=np.random.randint(0,101)
numpy_4=np.random.randint(0,101)
for i in range(20000000):
    random.seed(i)
    random_1=random.randint(0,101)
    if random_1==numpy_1:
        random_2=random.randint(0,101)
        if random_2==numpy_2:
            random_3=random.randint(0,101)
            if random_3==numpy_3:
                random_4=random.randint(0,101)
                if random_4==numpy_4:
                    break
print(np.random.randint(0,101))
print(random.randint(0,101))

But this did not really work, as could be expected.