[scikit-learn] MLPClassifier on WIndows 10 is 4 times slower than that on macOS?

[Gael Varoquaux]

I suspect that it is probably due to the linear-algebra libraries: your
scientific Python install on macOS is probably using optimized
linear-algebra (ie optimized numpy and scipy), but not your install on
Windows.

I would recommend you to look at how you installed you Python
distribution on macOS and on Windows, as you likely have installed an
optimized one on one of the platforms and not on the other.

Cheers,

Gaël

On Sat, Dec 15, 2018 at 09:02:06AM -0500, Kouichi Matsuda wrote:
> Hi Hi everyone,

> I am writing a scikit-learn program to use MLPClassifier to learn
> Fashion-MNIST.
> The following is the program. It's very simple.
> When I ran it on Windows 10 (Core-i7-8565U, 1.8GHz, 16GB) note book, it took
> about 4 minutes.
> However, when I ran it on MacBook(macOS), it took about 1 minutes.
> Does anyone help me to understand the reason why Windows 10 is so slow?
> Am I missing something?

> Thanks,  

> import os import gzip import numpy as np #from https://github.com/
> zalandoresearch/fashion-mnist/blob/master/utils/mnist_reader.py def load_mnist
> (path, kind='train'): labels_path = os.path.join(path,'%s-labels-idx1-ubyte.gz'
> % kind) images_path = os.path.join(path,'%s-images-idx3-ubyte.gz' % kind) with
> gzip.open(labels_path, 'rb') as lbpath: labels = np.frombuffer(lbpath.read(),
> dtype=np.uint8, offset=8) with gzip.open(images_path, 'rb') as imgpath: images
> = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16) images =
> images.reshape(len(labels), 784) return images, labels x_train, y_train =
> load_mnist('data', kind='train') x_test, y_test = load_mnist('data', kind=
> 't10k') from sklearn.neural_network import MLPClassifier import time import
> datetime print(datetime.datetime.today()) start = time.time() mlp =
> MLPClassifier() mlp.fit(x_train, y_train) print((time.time() - start)/ 60)


> ---
> MATSUDA, Kouichi, Ph.D.

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    Gael Varoquaux
    Senior Researcher, INRIA Parietal
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