Google Colab is very slow compared to my PC

Question

I've recently started to use Google Colab, and wanted to train my first Convolutional NN. I imported the images from my Google Drive thanks to the answer I got here.

Then I pasted my code to create the CNN into Colab and started the process. Here is the complete code:

Part 1: Setting up Colab to import picture from my Drive

(part 1 is copied from here as it worked as exptected for me

Step 1:

!apt-get install -y -qq software-properties-common python-software-properties module-init-tools !add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null !apt-get update -qq 2>&1 > /dev/null !apt-get -y install -qq google-drive-ocamlfuse fuse 

Step 2:

from google.colab import auth auth.authenticate_user() 

Step 3:

from oauth2client.client import GoogleCredentials creds = GoogleCredentials.get_application_default() import getpass !google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL vcode = getpass.getpass() !echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} 

Step 4:

!mkdir -p drive !google-drive-ocamlfuse drive 

Step 5:

print('Files in Drive:') !ls drive/ 

Part 2: Copy pasting my CNN

I created this CNN with tutorials from a Udemy Course. It uses keras with tensorflow as backend. For the sake of simplicity I uploaded a really simple version, which is plenty enough to show my problems

from keras.models import Sequential from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import Flatten  from keras.layers import Dense  from keras.layers import Dropout from keras.optimizers import Adam  from keras.preprocessing.image import ImageDataGenerator  

parameters

imageSize=32  batchSize=64  epochAmount=50 

CNN

classifier=Sequential()   classifier.add(Conv2D(32, (3, 3), input_shape = (imageSize, imageSize, 3), activation = 'relu')) #convolutional layer  classifier.add(MaxPooling2D(pool_size = (2, 2))) #pooling layer  classifier.add(Flatten()) 

ANN

classifier.add(Dense(units=64, activation='relu')) #hidden layer  classifier.add(Dense(units=1, activation='sigmoid')) #output layer  classifier.compile(optimizer = "adam", loss = 'binary_crossentropy', metrics = ['accuracy']) #training method 

image preprocessing

train_datagen = ImageDataGenerator(rescale = 1./255,                                shear_range = 0.2,                                zoom_range = 0.2,                                horizontal_flip = True)  test_datagen = ImageDataGenerator(rescale = 1./255)   training_set = train_datagen.flow_from_directory('drive/School/sem-2-2018/BSP2/UdemyCourse/CNN/dataset/training_set',                                              target_size = (imageSize, imageSize),                                              batch_size = batchSize,                                              class_mode = 'binary')  test_set = test_datagen.flow_from_directory('drive/School/sem-2-2018/BSP2/UdemyCourse/CNN/dataset/test_set',                                         target_size = (imageSize, imageSize),                                         batch_size = batchSize,                                         class_mode = 'binary')  classifier.fit_generator(training_set,                      steps_per_epoch = (8000//batchSize),                      epochs = epochAmount,                      validation_data = test_set,                      validation_steps = (2000//batchSize)) 

Now comes my Problem

First of, the training set I used is a database with 10000 dog and cat pictures of various resolutions. (8000 training_set, 2000 test_set)

I ran this CNN on Google Colab (with GPU support enabled) and on my PC (tensorflow-gpu on GTX 1060)

This is an intermediate result from my PC:

Epoch 2/50 63/125 [==============>...............] - ETA: 2s - loss: 0.6382 - acc: 0.6520 

And this from Colab:

Epoch 1/50 13/125 [==>...........................] - ETA: 1:00:51 - loss: 0.7265 - acc: 0.4916 

Why is Google Colab so slow in my case?

Personally I suspect a bottleneck consisting of pulling and then reading the images from my Drive, but I don't know how to solve this other than choosing a different method to import the database.

Answer 1

As @Feng has already noted, reading files from drive is very slow. This tutorial suggests using some sort of a memory mapped file like hdf5 or lmdb in order to overcome this issue. This way the I\O Operations are much faster (for a complete explanation on the speed gain of hdf5 format see this).