I have an AWS Windows Server 2016 VM. This VM has a bunch of libraries/software installed (dependencies).
I'd like to, using python3, launch and deploy multiple clones of this instance. I want to do this so that I can use them almost like batch compute nodes in Azure.
I am not very familiar with AWS, but I did find this tutorial.
Unfortunately, it shows how to launch an instance from the store, not an existing configured one.
How would I do what I want to achieve? Should I create an AMI from my configured VM and then just launch that?
Any up-to-date links and/or advice would be appreciated.
Yes, you can create an AMI from the running instance, then launch N instances from that AMI. You can do both using the AWS console or you could call boto3 create_image() and run_instances(). Alternatively, look at Packer for creating AMIs.
You don't strictly need to create an AMI. You could simply the bootstrap each instance as it launches via a user data script or some form of CM like Ansible.
Related
I built a simple NodeJS application and ran on an EC2 instance.
Everything works fine. I decided to create an AMI (Amazon Linux based) and a launch template to be used by a ASG.
The problem is, I cannot get the application start automatically.
I tried to add the following command through the user_data field but it doesn't work:
node main.js
Any ideas on how to automatically start this application once launched by the ASG?
Typically you would add this to the startup script of the AMI, so once the instance has started it will run the script on boot.
You may want to look at PM2 as well, it's a great tool for things like this and also makes it easy to setup each node instance in cluster mode ( assuming you have an EC2 instance with more than one core )
Some other ways of doing this, albeit not an auto scale but with DigitalOcean they offer a CASS model called 'apps' that basically pushes you app into a container from a git-repo and deploys it, you can then just spin new instances out as needed. Downside is that the bandwidth is a bit small but CND etc can help with that.
I am trying to launch my own AMI using user-data so that it can run a script and then terminate.
So I launched an Ec2 Windows Base and configure it to have all the tools I need (NodeJS etc) and saved my script to C:\Projects\index.js.
I then saved it as an Image.
So I then used the console to launch an EC2 from my new AMI with the user-data of
node C:\Projects\index.js --uuid=1
</powershell>
If I run that command having RDP into the EC2 it works, so it seems that the userdata did not run when the Image was started.
Having read some of the other questions and answers it could be because the AMI created was made from an Instance that started already. So the userdata did not persist.
Can anyone advise me on how I can launch my AMI with a custom userdata each time? (as the UUID will change)
Thanks
Another solution that worked for me is to run Sysprep with EC2Launch.
The issue is that AWS doesn't reestablish the route to the profile service (169.254.169.254) in your custom AMI. See response by SanjitPatel in this post. So when I tried to use my custom AMI to create spot requests, my new instances were failing to find user data.
Shutting down with Sysprep, essentially forces AWS re-do all setup work on the instance, as if it were run for the first time. So when you create your instance, shut it down with Sysprep and then create your custom AMI, AWS will setup the profile service route correctly for the new instances and execute your user data. This also avoids manually changing Windows Tasks and executing user data on subsequent boots, as persist tag does.
Here is a quick step-by-step:
1.Create an instance using one of the AWS Windows AMIs (Windows Server 2016 Nano Server doesn't support Sysprep) and passing your desired user data (this may be optional, but good to make sure AWS wires setup scripts correctly to handle user data).
2.Customize your instance as needed.
3.Shut down your instance with Sysprep. Just open EC2LaunchSettings application and click "Shutdown with Sysprep".
4.Create your custom AMI from the instance you just shut down.
5.Use your custom AMI to create other instances, passing user data on instance creation. User data will be executed on instance launch. In my case, I used Spot Request screen, which had a User Data text box.
Hope this helps!
We have a Java based application running in Linux EC2 instances. We have different components running in different EC2 instances.
Every month we go for an AMI upgrade by creating new instances, deploying the code and killing the old instances. We are facing challenge in making sure that all components are deployed successfully in the instances.
Are there tools to get a snapshot of the application file system from old instance before AMI upgrade and compare it with the snapshot taken from new instance after code deployment?
If so that will make our work easier. We have a tool of 8 instances running the application.
I am working on a school project along with my team where we have to analyze large data sets using Python. The data is in the form of images (jpeg files). Since the analysis involves images we will be using TensorFlow, OpenCV etc. As the data set is large we are exploring running Python on EC2 and storing the data set on S3. Is there any wiki or guide that can help us with:
1) Set up Python (3.5) on EC2 and connect to S3 bucket where the files are stored.
2) Create a multi-user environment where all the team members (five) can access the server remotely and run tests against the data set/files.
My skill level on AWS is basic at best. Greatly appreciate any help with this.
At a high level, you would want to use the AWS CLI, but there are several things that you would need to setup first.
Create an account and enter the IAM console to create your users. I assume you would want to assign them all to the same group and define one permission policy for all of them. You should need access to only EC2 and S3. You will need a complete working knowledge of the IAM service (it is relatively small).
Create a role so that your EC2 instance can get access to S3. Follow this tutorial.
Use the AWS CLI to access your EC2 instance. The installation/development workflow should mimic a linux workflow very closely.
I have an EC2 instance which runs an app hosted on a private git repo.
I need to be able to launch many of these from my master server. At the moment, I have 5 fixed "worker" instances which I start/stop from the master with no problem. Each worker starts, pulls the repo, and launches the app on startup. This is obviously not a good solution and I want to make it more flexible (launch as many instances as I want, etc). The configuration and packages are final so I feel good about bundling it all into an AMI.
Is there a way for me to bundle my git keys into the AMI, in order to launch many similar instances and have them all pull and launch my app on startup without heving to connect to each of them and enter the password? Is there a better way? I've read about cloud-init, user-data, puppet and many other things, but I'm quite novice in the matter and couldn't find a proper example using ssh keys.
Instead of bundling the keys into the AMI, I suggest you keep them separate from the AMI because:
If you change your git keys, you don't have to build a new AMI
Unauthorized users who have privileges to launch an instance from your AMI cannot launch your app
I suggest using the user-data feature. You can optionally encrypt your keys and base64encode it if you want to. When you launch your instance manually or using CLI/API, you can pass your keys which can be accessed by the instance once it is launched. There are variety of ways to access the data (python, curl to name a few). I suggest you use AWS metadata server because your instance does not need your AWS credentials to fetch the user-data. Once your instance is launched, have your app make the following call, get the keys and then pull the repo:
curl http://169.254.169.254/latest/user-data
returns your metadata (no credentials needed). You can optionally base64decode and decrypt your keys and use it to pull the repo. If you do not want the extra security, you can bypass encrypt/base64 part.