Run Project Jupyter Notebooks On Amazon EC2

This is tutorial on running Project Jupyter Notebook on an Amazon EC2 instance. It is based on a tutorial by Piyush Agarwal which did not work for me immediately, but I tweaked a few things and got it working.

Note: This is not a beginner’s tutorial. I don’t explain some of the steps fully and don’t explain some concepts. There are other tutorials out there for that.

Create an AWS account

An EC2 instance requires an AWS account. You can make an account here.

Log into AWS and go to the EC2 main page. Then you will see a ‘Launch Instance’ button.

Launch a new instance

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Select Ubuntu

Select Ubuntu

Select t2.micro

Select Micro T2

Check out your new instance

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Create a new security group

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Create and download a new key pair

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View connect instructions

connect_1

connect_2

Set permissions on key pair

chmod 400 tutorial.pem

Open terminal

terminal

Connect using ssh

ssh

ssh -i "tutorial.pem" [email protected]

Are you sure you want to continue connecting (yes/no)?

success!

Download Anaconda to instance

Visit Anaconda’s download page and right click to get the url of the latest version of the Linux 64-bit version. In my case this url was:

https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh

Anaconda!

Now, back in the terminal, tell the EC2 instance to download that file. Note: You aren’t downloading the file to your computer, you are downloading it to the EC2 instance and installing it from there.

wget https://repo.continuum.io/archive/Anaconda3-4.2.0-Linux-x86_64.sh

Anaconda!

Install Anaconda

bash Anaconda3-4.2.0-Linux-x86_64.sh

Press enter a few times

Type ‘yes’ to agree

‘Press ENTER to confirm the location’

Anaconda!

Do you wish the installer to prepend the Anaconda3 install location to PATH in your /home/ubuntu/.bashrc ? [yes|no] [no] >>> yes

If it doesn’t allow you to answer yes, you can manually set it through the prompt they show:

You may wish to edit your .bashrc or
prepend the Anaconda3 install location:

$ export PATH=/home/ubuntu/anaconda3/bin:$PATH

Check that Anaconda is the preferred environment

which python /home/ubuntu/anaconda3/bin/python

Create a password for jupyter notebook

ipython

from IPython.lib import passwd

passwd()

Enter password: [Create password and press enter] Verify password: [Press enter]

'sha1:98ff0e580111:12798c72623a6eecd54b51c006b1050f0ac1a62d'

exit

Create config profile

jupyter notebook --generate-config

Create certificates for https

mkdir certs

cd certs

sudo openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mycert.pem -out mycert.pem

Answer questions

Configure jupyter

cd ~/.jupyter/

vi jupyter_notebook_config.py

Vim

Insert this at the beginning of the document:

c = get_config()

# Kernel config
c.IPKernelApp.pylab = 'inline'  # if you want plotting support always in your notebook

# Notebook config
c.NotebookApp.certfile = u'/home/ubuntu/certs/mycert.pem' #location of your certificate file
c.NotebookApp.ip = '0.0.0.0'
c.NotebookApp.open_browser = False  #so that the ipython notebook does not opens up a browser by default
c.NotebookApp.password = u'sha1:98ff0e580111:12798c72623a6eecd54b51c006b1050f0ac1a62d'  #the encrypted password we generated above
# Set the port to 8888, the port we set up in the AWS EC2 set-up
c.NotebookApp.port = 8888

Remember to replace sha1:98ff0e580111:12798c72623a6eecd54b51c006b1050f0ac1a62d with your password!

Vim

Press esc

Press shift-z

Create folder for notebooks

cd ~

mkdir Notebooks

cd Notebooks

Create new screen

This command allows you to create a separate screen for just your Jupyter process logs while you continue to do other work on the ec2 instance.

screen

Start Jupyter notebook

jupyter notebook

jupyter

Detach from screen

Command: Ctrl-a and then d

Other useful commands:

  1. Create new window: Ctrl-a c.
  2. Switch windows: Ctrl-a n
  3. Reattach to Screen: screen -r
  4. See running screen processes screen -ls

Visit Jupyter notebook in browser

Your EC2 instance will have a long url, like this:

ec2-52-39-239-66.us-west-2.compute.amazonaws.com

Visit that URL in your browser: (make sure to include the https at the beginning, or you’ll have access errors.)

https://ec2-52-39-239-66.us-west-2.compute.amazonaws.com:8888/

chrome

chrome

chrome