From 7982fa0fe29518b3adfc46dd0d402c51207a262f Mon Sep 17 00:00:00 2001
From: zbyosufzai <145053952+zbyosufzai@users.noreply.github.com>
Date: Tue, 6 Feb 2024 13:45:49 -0500
Subject: [PATCH] Update README.md
linked to extramural and intramural setup docs
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README.md | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/README.md b/README.md
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@@ -29,7 +29,7 @@ Use this repository to learn about how to use AWS by exploring the linked resour
+ [Additional Training](#tr)
## **Getting Started**
-You can learn a lot of what is possible on AWS in the AWS Getting Started [Tutorials Page](https://aws.amazon.com/getting-started/hands-on/?getting-started-all.sort-by=item.additionalFields.sortOrder&getting-started-all.sort-order=asc&awsf.getting-started-category=*all&awsf.getting-started-level=*all&awsf.getting-started-content-type=*all&awsm.page-getting-started-all=2) and we recommend you go there and explore some of the tutorials on offer. Nonetheless, it can be hard to know where to start if you are new to the cloud. To help you, we thought through some of the most common tasks you will encounter doing cloud-enabled research and gathered tutorials and guides specific to those topics. We hope the following materials are helpful as you explore cloud-based research. For an alternative perspective, you can also check out Lynn Langit's [AWS for Bioinformatics repo](https://github.com/lynnlangit/aws-for-bioinformatics).
+You can learn a lot of what is possible on AWS in the AWS Getting Started [Tutorials Page](https://aws.amazon.com/getting-started/hands-on/?getting-started-all.sort-by=item.additionalFields.sortOrder&getting-started-all.sort-order=asc&awsf.getting-started-category=*all&awsf.getting-started-level=*all&awsf.getting-started-content-type=*all&awsm.page-getting-started-all=2) and we recommend you go there and explore some of the tutorials on offer. Nonetheless, it can be hard to know where to start if you are new to the cloud. To help you, we thought through some of the most common tasks you will encounter doing cloud-enabled research and gathered tutorials and guides specific to those topics. We hope the following materials are helpful as you explore cloud-based research. For an alternative perspective, you can also check out Lynn Langit's [AWS for Bioinformatics repo](https://github.com/lynnlangit/aws-for-bioinformatics). If you would like to register for an **extramural Cloud Lab account**, check out the instructions to do so [here](docs/extramural_account_registration.md). For **intramural users** follow these instructions [here](docs/Intramural_STAKs.md) for obtaining your short term access keys.
## **Overview**
There are three primary ways you can run analyses using AWS: using **Virtual Machines**, **Jupyter Notebook instances**, and **Serverless services**. We give a brief overview of each of these here and go into more detail in the sections below. [Virtual machines](https://aws.amazon.com/getting-started/launch-a-virtual-machine-B-0/) are like desktop computers, but you access them through the cloud console and you get to pick the operating system and the specs such as CPU and memory. In AWS, these virtual machines are called Elastic Compute Cloud or EC2 for short. Jupyter Notebook instances are virtual machines with preconfigured with Jupyter Lab. On AWS these are run through [SageMaker](https://aws.amazon.com/pm/sagemaker/?trk=8987dd52-6f33-407a-b89b-a7ba025c913c&sc_channel=ps&sc_campaign=acquisition&sc_medium=ACQ-P|PS-GO|Brand|Desktop|SU|Machine%20Learning|Sagemaker|US|EN|Text&s_kwcid=AL!4422!3!532502995192!e!!g!!aws%20sagemaker&ef_id=CjwKCAjw7IeUBhBbEiwADhiEMfXNyIY5DZB4FG17gZcXYycBpN1lNPRNfXdxWP9NhTY_t_IrAmEiIhoCIqwQAvD_BwE:G:s&s_kwcid=AL!4422!3!532502995192!e!!g!!aws%20sagemaker), which is also AWS's ML/AI platform. You decide what kind of virtual machine you want to 'spin up' and then you can run Juptyer notebooks on that virtual machine. Finally, Serverless services are services that allow you to run things, an analysis, an app, a website, and not have to deal with your own servers (VMs). There are still servers running somewhere, you just don't have to manage them. All you have to do is call a command that runs your analysis in the background, and then see the outputs usually in a storage bucket.