The purpose of this page is to overview AWS-based (EC2) approaches to creating and using Jupyter notebooks, We describe a standard server-based approach with some shareability features and a personal approach which makes use of an ssh tunnel. The latter is also described on this technical page. Aside from AWS you can also create a static Jupyter notebook on GitHub, you can clone a Jupyter repo from GitHub and you can also explore the option described here.
We outline two EC2-based approaches here, shared and non-shared. The shared form is a nice collaboration tool but has two drawbacks: First you share **write** access with anyone you give the password to and second (in early 2017 anyway) there is some browser security complaining that you must push past. This seems a bit sketchy.
In the introduction above we mention five approaches to using Jupyter notebooks. Here we concentrate on the first two of these, both on EC2 instances.
Approach one: Personal Jupyter notebook, ssh tunnel
Suppose you need a Jupyter notebook for your own research; you do not need to share it with anyone. You create an EC2 instance, get the .pem key pair file, install Jupyter on the instance and that’s it, you are ready to go… except that you don’t have an obvious way of getting the Jupyter notebook to appear in a browser (because it the Jupyter server is running remotely). The solution: ssh tunneling as described here.
You may encounter errors where the port on your local machine had previously been opened and you cannot reuse the port. In this case, you can check opened ports on local machine by using this command:
sudo lsof -i :port number
You must close the port before you can reuse it.
Approach two: A shared Jupyter notebook
Suppose that you would like to share access to a Jupyter notebook on an EC2 instance with a few colleagues via a URL and a simple password. Follow the directions given
Approach two has two differences from Approach one: First your colleagues can edit the notebook files. (It may be wise to periodically back them up.) Second as of 2017 the connection seems to be non-secure and a bit sketchy.
Our notes on Approach Two
The links we give above or a quick internet search are quite possibly better resources than our notes given here.
- Spin up an AWS instance with Ubuntu AMI and install Anaconda
- Install Jupyter Notebook
% sudo apt-get install jupyter-notebook
Once you’ve installed Jupyter Notebook, follow the steps below:
% jupyter notebook --generate-config
Then launch ipython and generate a hashed password to add to the configuration file you generated:
% ipython In : from notebook.auth import passwd In : passwd() Enter password: Verify password: Out: 'sha1:--long_string_of_characters--'
The “sha1:–string–” is a hashed password. It is used below in a configuration file.
Generate a self-signed certificate using openssl so that your hashed password is encrypted
$ openssl req -x509 -nodes -days 365 -newkey rsa:1024 -keyout mykey.key -out mycert.pem
Set Jupyter Notebook to use the certificate when it starts:
$ jupyter notebook --certfile=mycert.pem --keyfile mykey.key
You can also set the Jupyter Notebook to use the certificate when it starts by editing the configuration file:
$ vi ~/.jupyter/jupyter_notebook_config.py
You will want to add the following lines to the config file (or uncomment those lines – remove the hashes):
# Set options for certfile, ip, password, and toggle off # browser auto-opening c.NotebookApp.certfile = u'/absolute/path/to/your/certificate/mycert.pem' c.NotebookApp.keyfile = u'/absolute/path/to/your/certificate/mykey.key' # Set ip to '*' to bind on all interfaces (ips) for the public server c.NotebookApp.ip = '*' c.NotebookApp.password = u'sha1:--long_string_of_characters--' c.NotebookApp.open_browser = False # It is a good idea to set a known, fixed port for server access c.NotebookApp.port = 9999
Note: You must open port 9999 on the EC2 instance. You can do this from the AWS console using the Security Groups and Inbound rules.
Note: If http://ipaddress:9999 doesn’t work, use https://ipaddress:9999.
Creating Alarms for Jupyter Notebook Service
kilroy this part is not in place yet; see source nbk
Jupyter notebook auto-restart
This is how you manually start / stop a Jupyter server:
% sudo service jupyter start % sudo service jupyter stop
You may also want to delve into the difference between these two commands:
% jupyter notebook % nohup jupyter notebook
The first will halt when you log off the machine; the second uses ‘nohup’ to make the process persist in the background so you can log out. (‘nohup’ is short for no hang-up; a holdover from phone modem days.)
This manual starting and stopping can get tedious (tm Isabella Boom); so this section describes how to set your Jupyter notebook server to start automatically on reboot. As noted your EC2 instance may restart as often as every couple days or so. The notes below assume the Jupyter notebook server is installed through Anaconda. It assumes that you have an EC2 instance running Ubuntu.
Download this script and save it on your EC2 instance as /etc/init.d/jupyter. Edit this file to make this modification:
Make sure there is a /var/log/jupyter/ folder. If necessary create one using sudo mkdir.
Issue the following sequence from the command line: Respectively these make the init.d file executable, generate a Jupyter config file, and update the rc process to include your Jupyter service. Once this is done you can reboot the instance and make sure the Jupyter service starts properly.
% sudo chmod +x /etc/init.d/jupyter % sudo jupyter --generate-config -f /etc/jupyter/jupyter_config.py % sudo update-rc.d jupyter defaults