Acessing Amazon Web Services (AWS)

For our activities, we will split into 20 groups. Each group will have their own cloud computing instance with which they can build and run JEDI. We currently use Amazon Web Services (AWS) to provide our cloud computing instances. For those familiar with AWS, these compute instances through AWS’s Elastic Compute Cloud (EC2) service.

Though you can access the cloud instances directly through ssh if you wish, we have set up a web interface to each of the nodes by means of Jupyter lab. We recommend that you use the web interface because this provides a number of attractive features, including the ability to navigate the directory tree, open new terminals, transfer files, and most importantly, to display graphics.

Important: The AWS EC2 instances (and their associated JupyterLab URLs) will only be active in the afternoons during the week of the Academy. We will create the instances on Monday so the links below will not work before then. Then, each night of the Academy (Monday-Thursday), we will shut the instances down overnight as a cost-saving measure. However, all data will be saved so you can pick up the next day precisely where you left off. Then, after our last activity on Thursday afternoon, we will permanently terminate all instances.


If you ever wish to leave your node up longer (for example, one evening to work from your hotel), just make arrangments with a JEDI Master (Mark).

So, to participate in the activities, you will need three things:

  1. Your group number
  2. A URL to access your AWS instance through the JupyterLab web interface (see table)
  3. A password

A JEDI master will assign you a group number and give you the password. Write these down. They will be the same every day and you will need them for every activity. Once you know your group number, you can access your URL by selecting the appropriate link in the following table. You should bookmark this link because, again, it will not change - it will be yours for the entire Academy.

Group GitHub user name ip address JupyterLab URL
1 jedi-ac01
2 jedi-ac02
3 jedi-ac03
4 jedi-ac04
5 jedi-ac05
6 jedi-ac06
7 jedi-ac07
8 jedi-ac08
9 jedi-ac09
10 jedi-ac10
11 jedi-ac11
12 jedi-ac12
13 jedi-ac13
14 jedi-ac14
15 jedi-ac15
16 jedi-ac16
17 jedi-ac17
18 jedi-ac18
19 jedi-ac19
20 jedi-ac20


Write down your group number and your password. Bookmark your Jupyter URL. Making a mistake here will mean that you will be working in the work space of another group.

Now, to log into your cloud computing instance, merely select the appropriate URL from the Table above and enter the password at the prompt.

You should now see the default JupyterLab interface, which includes a directory tree displayed as an interactive (i.e. clickable) menu on the left and a large display area. At the top of the display area you should see several tabs. One is labeled Console 1. This is a Jupyter python console, capable of interpreting python commands. Another tab is labeled ubuntu with a local ip address. This is an ssh terminal, running bash. From here you have access to the linux command line. This is where we will be doing most of our work.

For more tips on how to work with the Jupyter interface, see Activity A on Monday.

If you are unable to link to the web interface, you can access the node directly through ssh, as mentioned above. To follow this method, you first need to acquire the academy-virginia.pem file from a JEDI master which contains an ssh key for authentication. Then you should use the -Y option to ssh to set up X forwarding as follows (remember that your username is ubuntu):

ssh -i academy-virginia.pem -Y ubuntu@<ip-address>

Then you can use the display application to display image files over the internet. Or, you can scp the files to your local machine (using the same pem file for authentication) and view them locally.

Contact Mark Miesch ( if you have any problems or questions.