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

Question

So to prevent another dozen of answers suggesting invalid in the context of this thread suggestion to !kill -9 -1, let's close this thread:

The answer is simple:

As of this writing Google simply gives only 5% of GPU to some of us, whereas 100% to the others. Period.

dec-2019 update: The problem still exists - this question's upvotes continue still.

mar-2019 update: A year later a Google employee @AmiF commented on the state of things, stating that the problem doesn't exist, and anybody who seems to have this problem needs to simply reset their runtime to recover memory. Yet, the upvotes continue, which to me this tells that the problem still exists, despite @AmiF's suggestion to the contrary.

dec-2018 update: I have a theory that Google may have a blacklist of certain accounts, or perhaps browser fingerprints, when its robots detect a non-standard behavior. It could be a total coincidence, but for quite some time I had an issue with Google Re-captcha on any website that happened to require it, where I'd have to go through dozens of puzzles before I'd be allowed through, often taking me 10+ min to accomplish. This lasted for many months. All of a sudden as of this month I get no puzzles at all and any google re-captcha gets resolved with just a single mouse click, as it used to be almost a year ago.

And why I'm telling this story? Well, because at the same time I was given 100% of the GPU RAM on Colab. That's why my suspicion is that if you are on a theoretical Google black list then you aren't being trusted to be given a lot of resources for free. I wonder if any of you find the same correlation between the limited GPU access and the Re-captcha nightmare. As I said, it could be totally a coincidence as well.

Last night I ran your snippet and got exactly what you got:

Gen RAM Free: 11.6 GB  | Proc size: 666.0 MB
GPU RAM Free: 566MB | Used: 10873MB | Util  95% | Total 11439MB

but today:

Gen RAM Free: 12.2 GB  I Proc size: 131.5 MB
GPU RAM Free: 11439MB | Used: 0MB | Util   0% | Total 11439MB

I think the most probable reason is the GPUs are shared among VMs, so each time you restart the runtime you have chance to switch the GPU, and there is also probability you switch to one that is being used by other users.

UPDATED: It turns out that I can use GPU normally even when the GPU RAM Free is 504 MB, which I thought as the cause of ResourceExhaustedError I got last night.

If you execute a cell that just has
!kill -9 -1
in it, that'll cause all of your runtime's state (including memory, filesystem, and GPU) to be wiped clean and restarted. Wait 30-60s and press the CONNECT button at the top-right to reconnect.

Restart Jupyter IPython Kernel:

!pkill -9 -f ipykernel_launcher

Find the Python3 pid and kill the pid. Please see the below image

Note: kill only python3(pid=130) not jupyter python(122).

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 :)