How to use AWS in our projects
We try to use the principle of least privilege. That means that when you get credentials to access our AWS account, we will try to limit your privileges as much as possible, so that you can still do the things you need to do with your AWS account. This ensures that you don't accidentally do things you don't need to do that can affect others (e.g., deleting data you are not working with, or starting machines when you don't need to do that).
Compromised credentials are credentials that may have been exposed to people who should not have them. One (scarily common) way in which credentials can get compromised is if they are somehow stored in code and get committed to a repository and pushed to GitHub. Please note that if you committed credentials into the history of a git repo, they will still be there in the history of the repo, even if you delete them and commit the deletion. This means that if you committed the credentials, and you are not sure how to remove them from the repo history, do not push that repo to GitHub, and ask for someone to help.
We hope to never have compromised credentials, but if you have reason to believe that your credentials were compromised (e.g., you accidentally pushed them to GitHub), please let us know as soon as possible, so that we can mitigate the potential harm of this.
If your credentials allow you to log into the AWS console, you must use multi-factor authentication. MFA means that in addition to entering your password when logging in, another form of authentication will be required to get access to the console. For example, a cell-phone app that provides you with a token.
Rules of thumb for resource usage
Always tag resources with a "project" tag
Tagging resources helps us track how much resources are being used in what project. Tagging is easy if you are using the console. Whenever starting a resource (e.g., EC2 instance, or S3 bucket, you will have the option to tag it through the console).
We currently have the following tags:
Consider how you are using storage
Block storage (for example an EC2 instance hard-drive) is about an order of magnitude more expensive than an Amazon Machine Image, which is about an order of magnitude more expensive than highly-available S3, which is about an order of magnitude more expensive than Glacier storage.
Use the smallest possible instance for the job
Similar to storage, please consider what machine you are using. When launching an EC2 instance from the console, pay attention to the RAM and CPU that are provided by the instance that you are using and choose the smallest amount of these that will let you comfortably do the work you want to do. Similarily
When in doubt, ask someone
If you think that what you are about to do will cost less than $50, go ahead and do it (As an example,
preprocessing with qsiprep/cloudknot on spot instances costs about $1/participant). If you think that it would
cost $50 - $1,000, please post to the
#AWS slack channel, and wait for someone else to provide feedback. If
you are interested in doing something really ambitious that you think will cost more than $1,000, please ask
Ariel (he'll probably say "go ahead").
You can calculate usage/cost with the AWS cost calculator.
Benchmark large projects using data subsets
Relatedly, if you are planning ambitious calculations on AWS, benchmark your computational pipeline on smaller subsets of your data. For example, if you want to analyze a large dataset of 2,000 subjects, you should first demonstrate that your pipeline works on a sample of 10-20 subjects from the same dataset. This will allow you detect bugs and fine-tune your analysis pipeline before the larger production run.
Please adopt this practice in your own work and ask to see evidence of these "benchmarking runs" when providing feedback for others in the
#AWS slack channel.