Google Colaboratory: misleading information about its GPU (only 5% RAM available to some users)

Question

update: this question is related to Google Colab\'s \"Notebook settings: Hardware accelerator: GPU\". This question was written before the \"TPU\" option was added.

Answer 1

I believe if we have multiple notebooks open. Just closing it doesn't actually stop the process. I haven't figured out how to stop it. But I used top to find PID of the python3 that was running longest and using most of the memory and I killed it. Everything back to normal now.

Answer 2

Google Colab resource allocation is dynamic, based on users past usage. Suppose if a user has been using more resources recently and a new user who is less frequently uses Colab, he will be given relatively more preference in resource allocation.

Hence to get the max out of Colab , close all your Colab tabs and all other active sessions, reset the runtime of the one you want to use. You'll definitely get better GPU allocation.

Answer 3

just give a heavy task to google colab, it will ask us to change to 25 gb of ram.

example run this code twice:

import numpy as np
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
from keras.layers.advanced_activations import LeakyReLU
from keras.datasets import cifar10
(train_features, train_labels), (test_features, test_labels) = cifar10.load_data()
model = Sequential()

model.add(Conv2D(filters=16, kernel_size=(2, 2), padding="same", activation="relu", input_shape=(train_features.shape[1:])))
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))

model.add(Conv2D(filters=32, kernel_size=(3, 3), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))

model.add(Conv2D(filters=64, kernel_size=(4, 4), padding="same", activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))

model.add(Flatten())

model.add(Dense(25600, activation="relu"))
model.add(Dense(25600, activation="relu"))
model.add(Dense(25600, activation="relu"))
model.add(Dense(25600, activation="relu"))
model.add(Dense(10, activation="softmax"))

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.fit(train_features, train_labels, validation_split=0.2, epochs=10, batch_size=128, verbose=1)

then click on get more ram :)