with Google Colab

Due to personal reasons I am not able create an account through Paperspace or other cloud platforms. I heard that Google is providing free GPU through the Colab platform. Will that be sufficient to complete both parts of the deep learning course?
It provides the tesla K80 gpu and 13gb of RAM.


I’ve successfully completed part 1 of the Deep Learning course using Google Colab. It took a little fiddling around, but I think I have found the 1-run script to make it work. Here are the steps I take:

  1. Upload the desired notebook to my Google Drive, then open it in Colab

  2. Change runtime type to Python 3, and then change Hardware Accelerator to GPU

  3. When the runtime is connected, add this block of code to the top:

  4. Run it, then the rest of the notebook should run fine!

NB: You need to run this script any time you connect to a new runtime or open the notebook


Hello @vishal.pani,

I’m currently in the second half of Part 1 of the Deep Learning Course. It’s perfectly possible to complete the whole course just using Colab.

In fact, a great resource that gives you almost a similar experience with using AWS, Paperspace or Google Cloud is Clouderizer using the Colab as a backend platform for the GPU and Google Drive as your permanent disk, so it’s a free computation engine because of Colab, a free permanent disk for saving datasets and models because you can connect your Google Drive account to it.

The team at clouderizer already created a Community Project for which does all the configuration for you and load the updated libraries each time you start the project, loads any Kaggle Datasets at start or install python libraries or linux libraries.

You can sign up and literally in minutes begin running the notebooks with a terminal available in the browser.

In fact it’s even better than the paid Cloud Products mentioned because we can access the project, terminal and notebooks from anywhere just using the browser without any install and no configuration, no sweat and not a one dollar spent.

I’m not afiliated at all to Clouderizer by the way, I highly recommend because it’s awesome for study. I even have some credits in Google Cloud but I prefer to use clouderizer because I can access from any crappy PC during the day when I have some free time.

Check detailed instructions in these links:


Happy Learning


Thank you for the helpful responses @grosenthal and @ronaldokun!
Now I can dive into the course without the fear of crashing midway!

I am facing an issue with setting up on cloderizer . Next button not visible on SETUP page. Any workarounds?

I was able to set it up without any problems. Can you send a screenshot?

It was a simple case of scrolled up page. Prompt response from Prakash of clouderrizer solved the issue,

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Prakash is very helpful and always available to solve any issue.

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How did anyone solved the gpu memory issue that google colab has?


@sgaseretto Did you try to run the code? I went through the first notebook without any problem.

The memory issues come down to luck (or location). Basically some % of users are allocated the 5% and hence can’t use colab. The rest of users get the full 12gb of GPU RAM (like me :slight_smile:

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How did you save your work when you edited the fast ai notebooks, or if you created a new notebook from scratch? I find if I save my notebooks, stop the project, then come back later and start it all up again my work has all disappeared.

Hello Kyap,

Did you connect your Google Drive account to your Project at Clouderizer?

Everything in the folders:

  • ( code )
  • out

Are saved in the Drive inside a folder named clouderizer

The clouderizer platform sync these folders every few minutes. Wait for at least 5 minutes before saving the notebook to close your clouderizer project, or just check your Google Drive to see if the file was updated.


I’ve been working through all the lessons in Colab, and haven’t set up Clouderizer.

Here’s my notebook explaining how to use Colab for Fastai (including importing data from various places, which is where I’ve had the most trouble so far!). I’ll keep adding cells for each lesson as I work through it.

The basics:

Always add a cell to the top of the notebook like this:

!pip3 install fastai

How to import data from fastai URLs (lessons 1, 3, 4):

For lesson 1:

# Get the Dogs & Cats data, unzip it, and put it in the 'data' directory:
!wget && unzip -d data/

# Check to make sure the folders all unzipped properly:
!ls data/dogscats

For lesson 3:

# Get the Rossmann data and make a directory to put it in:
!wget && mkdir -p ~/data/rossmann

# Unzip the .tgz file, and put it in the right directory:
# x for extract
# -v for verbose    # NOTE: I usually turn this off; it prints a lot...
# -z for gnuzip
# -f for file (should come at last just before file name)
# -C to extract the zipped contents to a different directory
!tar -xzf rossmann.tgz -C ~/data/rossmann/

# Make sure the data is where we think it is:
!ls ~/data/rossmann

For lesson 4:

# Get the IMDB data:

# Unzip the tgz file, and put it in the right directory:
# x for extract
# -v for verbose    # NOTE: I usually turn this off; it prints a lot...
# -z for gnuzip
# -f for file (should come at last just before file name)
# -C to extract the zipped contents to a different directory
!tar -xvzf aclImdb.tgz -C data/

# Make sure the data is where we think it is:
!ls data/aclImdb

How to import data from Kaggle using the Kaggle CLI (lesson 2):
I found this forum post very useful.

# Install the Kaggle API
!pip3 install kaggle

# Import kaggle.json from Google Drive
# This snippet will output a link which needs authentication from any Google account
from googleapiclient.discovery import build
import io, os
from googleapiclient.http import MediaIoBaseDownload
from google.colab import auth


drive_service = build('drive', 'v3')
results = drive_service.files().list(
        q="name = 'kaggle.json'", fields="files(id)").execute()
kaggle_api_key = results.get('files', [])

filename = "/content/.kaggle/kaggle.json"
os.makedirs(os.path.dirname(filename), exist_ok=True)

request = drive_service.files().get_media(fileId=kaggle_api_key[0]['id'])
fh = io.FileIO(filename, 'wb')
downloader = MediaIoBaseDownload(fh, request)
done = False
while done is False:
    status, done = downloader.next_chunk()
    print("Download %d%%." % int(status.progress() * 100))
os.chmod(filename, 600)

# List the files for the Planet data 
!kaggle competitions files -c planet-understanding-the-amazon-from-space

# Download the data from Kaggle
# -c: competition name
# -f: which file you want to download
# -p: path to where the file should be saved
!kaggle competitions download -c planet-understanding-the-amazon-from-space -f train-jpg.tar.7z -p ~/data/planet/
!kaggle competitions download -c planet-understanding-the-amazon-from-space -f test-jpg.tar.7z -p ~/data/planet/
!kaggle competitions download -c planet-understanding-the-amazon-from-space -f -p ~/data/planet/

# In order to unzip the 7z files, need to install p7zip
# This was helpful:
!apt-get install p7zip-full

# Unzip the 7zip files 
# -d: which file to un7zip
!p7zip -d ~/data/planet/test-jpg.tar.7z #-oc:/data/planet
!p7zip -d ~/data/planet/train-jpg.tar.7z #-oc:/data/planet

# Unzip the .tar files 
!tar -xvf ~/data/planet/test-jpg.tar
!tar -xvf ~/data/planet/train-jpg.tar

# Move the unzipped folders into data/planet/
!mv test-jpg ~/data/planet/ && mv train-jpg ~/data/planet/

# Unzip the regular file
!unzip ~/data/planet/ -d ~/data/planet/

# Make sure everything looks as it should:
!ls ~/data/planet/

Finally, if you’re worried about how much of the GPU is available, there’s a cell you can run that checks the % utilization of your current GPU. See the Stack Overflow link that Sebastian posted earlier on in this thread.

Hope this helps some of you get started more quickly!


great and cool!

Hello Everyone and thank you for your helpful comments.
I am new to and I have been trying to find the best way to get started with the GPU acceleration. Since I have a gaming laptop with Nvidia GeForce GTX 1060, I always have the option of running the notebooks locally. But since it is more convenient to run on cloud, I thought if giving Google Colab a try. So far, I have been unsuccessful in getting the session to run with GPA and always get the error:

Failed to assign a backend
No backend with GPU available. Would you like to use a runtime with no accelerator?

My guess is that I’m late to the party and everyone is using the GPUs. If you think this is the case, do you suggest any other free way of cloud computing or should I install Ubuntu?

You can try Kaggle Kernels, they include free GPUs now


Hi William,

Thank you for the tip. Actually I just found that out right when you posted. Cheers!

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thank you for sharing this.

You can also import from Kaggle like this:

!pip install kaggle-cli

and then
!kg download -c dog-breed-identification -u yourusername -p password