Go to Connection > SSH > Auth, browse and select the ppk file downloaded from our keypair creation earlier on.Go to Connection > Data and enter the username obtained from the “Connect to instance” page.Enter the instance IP address in the Host Name field.Using the information from the instance summary page, we can configure PuTTY to connect to our EC2 instance: Connect - This gives us the instructions for connecting to the EC2 instance. Public IPv4 address - we need this to connect to the EC2 instance and also, to configure our Jupyter server.ģ. Instance status - it should be running for us to be able to connect to itĢ. On the instance summary page, we look at a few things:ġ.
It’s a good idea to create billing alerts as suggested above as we don’t want to be charged unknowingly when the free tier usage is exceeded.
We can quickly access our EC2 instance from the Launch Status page by clicking on the link highlighted below. Make sure you still have the private key that was downloaded to the machine. This is where we choose the key pair that we have created earlier on. In the final step to launch the EC2 instance, we will be prompted to select a key pair for connecting to our instance. Associating Key pair for EC2 instance connection While we are allowing all IP addresses to access the EC2 instance with source set to Anywhere, you should restrict the access from known IP addresses only. Otherwise, add the rules for port 22 for SSH and port 8888 for the Jupyter server. Configure Security Groupĭepending on whether SSL is configured, we can open the firewall for HTTP or HTTPS. We can attach additional EBS volumes to the EC2 instance anytime. The data in the instance store will be lost.Īlthough not covered in this article, it is advisable to use more durable data storage such as Amazon EBS for example. if the EC2 instance is stopped or hibernates. Note that data in an instance store persists only during the lifetime of its associated instance, i.e. We can only specify the instance store volumes for the EC2 instance during launching, we should therefore increase the volume size. Go for the t2.micro instance type which gives us 750 free hours per month for the first year. We shall select the free tier eligible Ubuntu server. We are going to focus on:įor those steps that are not mentioned, we will keep to the default configuration. There are 7 steps to launching the server. Click on the “Launch instances” button to start creating the virtual machine. Launching Amazon EC2 instancesįrom the AWS left menu bar, navigate to Instances. However, we will have to convert this file to a ppk file when we use PuTTY. This will give us a private key file (*.pem file) that can be used with OpenSSH. We can also create the key pair before launching our EC2 instance. We will use it to configure PuTTY for SSH into our Amazon EC2 instance.
Select the ppk option to download a PuTTY private key file. We can create the key pair from the Amazon EC2 dashboard. Log into AWS and navigate to the Amazon EC2 main page. Generating Amazon EC2 key pairĪ key pair is a set of security credentials that allow us to connect to an Amazon EC2 instance. The video below takes us through the process of setting up a Ubuntu server with Amazon EC2 and JupyterLab with an atoti tutorial. Launching Ubuntu instance with Amazon EC2Īmazon EC2 is a web service that allows us to boot an Amazon Machine Image (AMI) to configure a virtual machine. In this use case, we used PuTTY to remote access the Ubuntu server that we are going to create. We created a free account with the AWS free tier for this article.
With a common development platform deployed on AWS using Amazon EC2, your end-users can start building the dashboards, or even explore the source code of the notebook behind the app.įor more information about atoti and what you can do with it, follow this link. One limitation of this solution is that your users should be mindful of not concurrently running the same notebook or restarting the kernels of other users. We will show you a simpler solution that uses a shared instance of JupyterLab. The recommended solution would be to implement a JupyterHub. The goal of this article is to give access to atoti development platform from the cloud for anyone to create their own notebooks and run a BI web application. In this article, we are going to see how we can set up a JupyterLab along with atoti on a virtual machine using AWS Elastic Compute Cloud, also known as EC2. In case you haven’t heard of atoti, it is a Python library that allows multidimensional data analysis and comes with dashboarding capability.
In a previous article, we have covered how to deploy a BI dashboard in AWS using Docker, specifically using the atoti Docker image.