Update README.md

Author: bethac07

README.md

@@ -1,70 +1,12 @@
-# Distributed-Something
-Run encapsulated docker containers that do... something in the Amazon Web Services (AWS) infrastructure.
-We are interested in scientific image analysis so we have used it for [CellProfiler](https://github.com/CellProfiler/Distributed-CellProfiler), [Fiji](https://github.com/CellProfiler/Distributed-Fiji), and [BioFormats2Raw](https://github.com/CellProfiler/Distributed-OmeZarrMaker).
-You can use it for whatever you want!
-
-## Documentation
-Full documentation is available on our [Documentation Website](https://distributedscience.github.io/Distributed-Something).
-
-## Overview
-
-This code is an example of how to use AWS distributed infrastructure for running anything Dockerized.
-The configuration of the AWS resources is done using boto3 and the AWS CLI.
-The worker is written in Python and is encapsulated in a Docker container.
-There are four AWS components that are minimally needed to run distributed jobs:
-
-
-1. An SQS queue
-2. An ECS cluster
-3. An S3 bucket
-4. A spot fleet of EC2 instances
-
-
-All of them can be managed individually through the AWS Management Console.
-However, this code helps to get started quickly and run a job autonomously if all the configuration is correct.
-The code runs a script that links all these components and prepares the infrastructure to run a distributed job.
-When the job is completed, the code is also able to stop resources and clean up components.
-It also adds logging and alarms via CloudWatch, helping the user troubleshoot runs and destroy stuck machines.
-
-## Running the code
-
-### Step 1
-Edit the config.py file with all the relevant information for your job.
-Then, start creating the basic AWS resources by running the following script:
+# Distributed-HelloWorld
 
- $ python3 run.py setup
-
-This script initializes the resources in AWS.
-Notice that the docker registry is built separately and you can modify the worker code to build your own.
-Any time you modify the worker code, you need to update the docker registry using the Makefile script inside the worker directory.
-
-### Step 2
-After the first script runs successfully, the job can now be submitted to with the following command:
-
- $ python3 run.py submitJob files/exampleJob.json
-
-Running the script uploads the tasks that are configured in the json file.  
-You have to customize the exampleJob.json file with information that make sense for your project.
-You'll want to figure out which information is generic and which is the information that makes each job unique.
-
-### Step 3
-After submitting the job to the queue, we can add computing power to process all tasks in AWS.
-This code starts a fleet of spot EC2 instances which will run the worker code.
-The worker code is encapsulated in Docker containers, and the code uses ECS services to inject them in EC2.
-All this is automated with the following command:
-
- $ python3 run.py startCluster files/exampleFleet.json
-
-After the cluster is ready, the code informs you that everything is setup, and saves the spot fleet identifier in a file for further reference.
-
-### Step 4
-When the cluster is up and running, you can monitor progress using the following command:
-
- $ python3 run.py monitor files/APP_NAMESpotFleetRequestId.json
+[Distributed-Something](https://github.com/DistributedScience/Distributed-Something) is an app to run encapsulated docker containers that do... something in the Amazon Web Services (AWS) infrastructure.
+We are interested in scientific image analysis so we have used it for [CellProfiler](https://github.com/DistributedScience/Distributed-CellProfiler), [Fiji](https://github.com/DistributedScience/Distributed-Fiji), and [BioFormats2Raw](https://github.com/DistributedScience/Distributed-OmeZarrMaker).
+You can use it for whatever you want!
 
-The file APP_NAMESpotFleetRequestId.json is created after the cluster is setup in step 3.
-It is important to keep this monitor running if you want to automatically shutdown computing resources when there are no more tasks in the queue (recommended).
+Here, as an example, we have used it to make an app that lets you say hello to the world, as well as list some of your favorite things. The full code changes are available [here](https://github.com/DistributedScience/Distributed-HelloWorld/pull/1/files)
 
-See our [full documentation](https://distributedscience.github.io/Distributed-Something) for more information about each step of the process.
+Happy Distributing!
 
-![Distributed-Something](documentation/DS-documentation/images/Distributed-Something_chronological_overview.png)
+## Documentation
+Full documentation is available on our [Documentation Website](https://distributedscience.github.io/Distributed-Something).