How to successsfully run an ML algorithm with a medium sized data set on a mediocre laptop?

Question

I have a Lenovo IdeaPad laptop with 8 GB RAM and Intel Core I5 processor. I have 60k data points each 100 dimentional. I want to do KNN and for it I am running LMNN algorithm to find a Mahalanobis Metric.
Problem is after 2 hours of running a blank screen appears on my ubuntu. I am not getting what is the problem! Is my memory getting full or something else?
So is there some way to optimize this my code?

My dataset: data
My LMNN implementation:

import numpy as np
import sys
from modshogun import LMNN, RealFeatures, MulticlassLabels
from sklearn.datasets import load_svmlight_file

def main(): 

    # Get training file name from the command line
    traindatafile = sys.argv[1]

    # The training file is in libSVM format
    tr_data = load_svmlight_file(traindatafile);

    Xtr = tr_data[0].toarray(); # Converts sparse matrices to dense
    Ytr = tr_data[1]; # The trainig labels

    # Cast data to Shogun format to work with LMNN
    features = RealFeatures(Xtr.T)
    labels = MulticlassLabels(Ytr.astype(np.float64))



    # Number of target neighbours per example - tune this using validation
    k = 18

    # Initialize the LMNN package
    lmnn = LMNN(features, labels, k)
    init_transform = np.eye(Xtr.shape[1])

    # Choose an appropriate timeout
    lmnn.set_maxiter(200000)
    lmnn.train(init_transform)

    # Let LMNN do its magic and return a linear transformation
    # corresponding to the Mahalanobis metric it has learnt
    L = lmnn.get_linear_transform()
    M = np.matrix(np.dot(L.T, L))

    # Save the model for use in testing phase
    # Warning: do not change this file name
    np.save("model.npy", M) 

if __name__ == '__main__':
    main()

Answer 1

Exact k-NN has scalability problems.

Scikit-learn has documentation page (scaling strategies) on what to do in such situation (many algorithms have partial_fit method, butunfortunately kNN doesn't have it).

If you'd accept to trade some accuracy for speed you can run something like approximate nearest neighbors.