How To Increase Sklearn GMM predict() Performance Speed?

I am using Sklearn to estimate the Gaussian Mixture Model (GMM) on some data.

After the estimation, I have many query points. I would like to obtain their probabilities of belonging to each of the estimated Gaussian.

The code below works. However, the gmm_sk.predict_proba(query_points) part is very slow as I need to run it multiple times on 100000 sets of samples, where each sample contains 1000 points.

I guess that it happens because it is sequential. Is there a way to make it parallel? Or any other way to make it faster? Maybe on GPU using TensorFlow?

I saw TensorFlow has its own GMM algorithm but it was very hard to implement.

Here is the code that I have written:

import numpy as np
from sklearn.mixture import GaussianMixture
import time


n_gaussians = 1000
covariance_type = 'diag'
points = np.array(np.random.rand(10000, 3), dtype=np.float32)
query_points = np.array(np.random.rand(1000, 3), dtype=np.float32)
start = time.time()

#GMM with sklearn
gmm_sk = GaussianMixture(n_components = n_gaussians, covariance_type=covariance_type)
gmm_sk.fit(points)
mid_t = time.time()
elapsed = time.time() - start
print("learning took "+ str(elapsed))

temp = []
for i in range(2000):
    temp.append(gmm_sk.predict_proba(query_points))

end_t = time.time() - mid_t
print("predictions took " + str(end_t))    

I solved it ! using multiprocessing. just replaced

temp = []
for i in range(2000):
    temp.append(gmm_sk.predict_proba(query_points))

with

import multiprocessing as mp
    query_points = query_points.tolist()
    parallel = mp.Pool()
    fv = parallel.map(par_gmm, query_points)
    parallel.close()
    parallel.join()