Quick Guide to Run your Python Scripts on Google Colaboratory | by Eden Au | Towards Data Science

If you are looking for an interactive way to run your Python script, say you want to start a machine learning project with a couple of friends, look no further — Google Colab is the best solution for you. You can work online and save your code on your local Google Drive, and it allows you to
Run your scripts with free GPUs (and TPUs!)
Utilize pre-installed Python libraries and Jupyter Notebook features
Work anywhere you want, it is on the clouds
Share codes and collaborate with colleagues
In short,
Google Colab = Jupyter Notebook + Free GPUs
with arguably cleaner interface than most (if not all) alternatives. I have come up with some code snippets for you to master Google Colab. I hope this becomes a go-to article when you need some ready-made codes to solve common problems on Colab.
Basics
Files
Libraries
Machine Learning
Remarks
Originally published on my blog edenau.github.io.
Go to ‘Runtime’ > ‘Change runtime type’ > ‘Hardware accelerator’, and select ‘GPU’ or ‘TPU’. You can check if the GPU is enabled by
import tensorflow as tfdevice_name = tf.test.gpu_device_name()if device_name != '/device:GPU:0':  raise SystemError('GPU device not found')
An error will be raised if GPU is not enabled. Note that you can only run a session continuously for maximum 12 hours, and the environment do not persist across sessions.
SHIFT + ENTER
Simply add an ! before, for example:
!ls '/content/gdrive/My Drive/Colab Notebooks/'
Let’s check the information of OS, processors and RAM they are using:
!cat /proc/version!cat /proc/cpuinfo!cat /proc/meminfo
Linux, no surprise.
Use the following script:
from google.colab import drivedrive.mount('/content/gdrive')
You will then be asked to sign in your Google account and copy an authorization code. Click the link, copy the code, paste the code.
Go to this URL in a browser: https://accounts.google.com/signin/oauth/...Enter your authorization code: ·········· Mounted at /content/gdrive
You can simply upload files manually to your Google Drive, and access them using codes above. Alternatively, you can use the following code:
from google.colab import filesuploaded = files.upload()
Copy the executable file to /usr/local/bin, and give yourself permission to execute it.
!cp /content/gdrive/My\ Drive/Colab\ Notebooks/<FILE> /usr/local/bin!chmod 755 /usr/local/bin/<FILE>
Use pip in bash command:
!pip install <PACKAGE_NAME>
or conda:
!wget -c https://repo.continuum.io/archive/Anaconda3-5.1.0-Linux-x86_64.sh!chmod +x Anaconda3-5.1.0-Linux-x86_64.sh!bash ./Anaconda3-5.1.0-Linux-x86_64.sh -b -f -p /usr/local!conda install -q -y --prefix /usr/local -c conda-forge <PACKAGE_NAME>import syssys.path.append('/usr/local/lib/python3.6/site-packages/')
Use ngrok:
# Run Tensorboard in the backgroundLOGDIR = '/tmp/log'get_ipython().system_raw(    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'    .format(LOGDIR))# Use ngrok to tunnel traffic to localhost! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip! unzip ngrok-stable-linux-amd64.zipget_ipython().system_raw('./ngrok http 6006 &')# Retrieve public url! curl -s http://localhost:4040/api/tunnels | python3 -c \    "import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])"
and you will get a hyperlink like this:
https://d5b842b9.ngrok.io
You can access your Tensorboard via the link!
Many of my projects were developed using Google Colab. Check out the following articles for more.
towardsdatascience.com
towardsdatascience.com
towardsdatascience.com
Thank you for reading!
Originally published on my blog edenau.github.io.