I want to run some machine learning algorithms such as PCA and KNN with a relatively large dataset of images (>2000 rgb images) in order to classify these images.

My source code is the following:

import cv2 import numpy as np import os from glob import glob from sklearn.decomposition import PCA from sklearn import neighbors from sklearn import preprocessing   data = []  # Read images from file for filename in glob('Tables/*.jpg'):      img = cv2.imread(filename)     height, width = img.shape[:2]     img = np.array(img)      # Check that all my images are of the same resolution     if height == 529 and width == 940:          # Reshape each image so that it is stored in one line         img = np.concatenate(img, axis=0)         img = np.concatenate(img, axis=0)         data.append(img)  # Normalise data data = np.array(data) Norm = preprocessing.Normalizer() Norm.fit(data) data = Norm.transform(data)  # PCA model pca = PCA(0.95) pca.fit(data) data = pca.transform(data)  # K-Nearest neighbours knn = neighbors.NearestNeighbors(n_neighbors=4, algorithm='ball_tree', metric='minkowski').fit(data) distances, indices = knn.kneighbors(data)  print(indices) 

However, my laptop is not sufficient for this task so I need to use the computational resources of an online platform (e.g. like the ones provided by GCP).

However, Google Cloud Platform is like a labyrinth at first-sight. How can I simply use some of the resources of GCP (faster CPUs, GPU etc) to run my source code above?