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Deep learning training speed with 1080 Ti and M1200

June 19th, 2018

I compared the speed of Nvidia’s 1080 Ti on a desktop (Intel i5-3470 CPU, 3.2G Hz, 32G memory) and NVIDIA Quadro M1200 w/4GB GDDR5, 640 CUDA cores on a laptop (CPU: Intel Core i7-7920HQ (Quad Core 3.10GHz, 4.10GHz Turbo, 8MB 45W, Memory: 64G).

The code I used is Keras’ own example (mnist_cnn.py) to classiy MNIST dataset:

MNIST dataset

MNIST dataset

'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, shuffled and split between train and test sets

if K.image_data_format() == 'channels_first':
    input_shape = (1, img_rows, img_cols)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()


          verbose=1, validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)

The result is that 1080 Ti is 3 times faster than M1200:

M1200 (1 epoch) 1080 Ti (1 epoch)
18s 6s
Author: Xu Cui Categories: deep learning, programming Tags:
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  1. November 21st, 2018 at 20:38 | #1

    I don’t see M1200 in support list of CUDA. How did you do that?

  2. November 21st, 2018 at 21:22 | #2

    I saw it is listed here at https://developer.nvidia.com/cuda-gpus