Fashion MNIST: Is there an easy way to extract only 1% of the data to do a minimal gridsearch?

I am trying implement several models on the fashion-MNIST. I have imported the data according to the tf.keras tutorial:

import tensorflow as tf
from tensorflow import keras
import sklearn
import numpy as np

f_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = f_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']   
print(train_images)
print(train_labels)
(60000, 28, 28)
(60000,)

print(test_images)
print(test_labels)
(10000, 28, 28)
(10000,)
 
# Need to concatenate as GridsearchCV takes entire set in input
all_images = np.concatenate((train_images, test_images))
all_labels = np.concatenate((train_labels, test_labels))

print(all_images.shape)
print(all_labels.shape)
(70000, 28, 28)
(70000,)

The 10 labels are equally distributed in both training and testing set:

Since this is only to practice I would like to implement a minimal grid search but instead of using the entire set of 70 000 samples I'd like to extract only say 1% to do a grid search on that.

That way I can learn how it works without spending to much time on the computation.

The tutorials I see use however only the from skelearn.model_selection import GridSearchCV module which takes the entire set as input:

# Splitting the entire set into train and test
X_train, X_test, y_train, y_test = train_test_split(all_images,all_labels, 
test_size=0.3, random_state = 101)

parameters_grid={'C':[0.001, 0.01, 0.1, 1, 10], 'gamma': [1, 0.1, 
0.01, 0.001, 0.0001],
            'kernel': ['rbf']}
grid=GridSearchCV(SVC(),parameters_grid, refit = True, verbose = 3)
grid.fit( )

So far the only work around I could think of is to use only the set of test_images as it is smaller. But I guess it would still run for a while, given that it contains 10 000 images...

I also thought about changing the function to use a just a smaller portion for training like so:

# Splitting the entire set into train and test
X_train, X_test, y_train, y_test  = train_test_split(test_images, test_labels, test_size=0.99, random_state = 101)

That way I'd use only the test_images that hold only 10 000 samples. I think this would lead to the models being trained on only 1% of the 10 000 and the rest will just be used for testing.

Is there an better python-way to extract only 1% of all_images or test_images with the corresponding all_labels or test_labels?

Obviously I would build the final model feeding all 60 000 training samples and subsequently test it on the 10 000.

I googled and talked to colleagues but no hits or answers.