Using ECS to deploy a docker app to AWS

June 24, 2021

Elastic Container Service (ECS) is a cloud computing service provided by Amazon Web Services (AWS) to manage containers and allow developers to run applications in the Cloud without having to configure an environment for the code to run.

ECS was developed by Amazon in response to the rise of containerization. ECS enables developers to easily use Docker containers for a wide range of activities from hosting to running complex microservices that require many containers.

Docker is a containerization technology that allows packaging of an application and its libraries into one so that the application can easily be run without the need to do further configurations.

Amazon Web Services (AWS) is a cloud computing platform for deploying and hosting web applications. Besides cloud services, AWS also provides distributed computing services. One of the services is Elastic Container Services (ECS).


In this article, we will understand how to deploy a Docker-based application to Amazon Web Services using Elastic Container Service (ECS).

Flask is a python framework for building web applications. We need Flask to create a small web project to deploy to AWS. I prefer flask for its simplicity and besides, it does not require any special tools to work with.

By the end of the tutorial, you will be able to understand how to create, dockerize and deploy a flask app.


You will need the following for the tutorial:

  1. A text editor to create the flask application. I use VSCode.
  2. A basic understanding of Python and Flask Framework.
  3. Docker installed on your computer.
  4. AWS CLI installed.
  5. An AWS account. You call follow this guide to create one.

Creating the flask app

To create the flask app, we need to install flask. In the directory of the project, run the command below:

pip install flask

Next, create two files, requirements.txt to hold the project libraries, and its dependencies, and

Add the following code to the file:

from flask import Flask, render_template
import flask
app = Flask(__name__)
# Index route
def index():
    return render_template('index.html')
    # We are telling flask to look for a file named index in the templates folder
    #then render it to the user interface. Note that by default flask looks for templates in a folder named templates.
if __name__ == '__main__':

Create a folder named templates which contains the HTML files rendered on the webpage. In this folder, create a file named index.html, then add the snippets below:

<!DOCTYPE html>
<html lang="en">
    <meta charset="UTF-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Docker | AWS Index</title>
    <link rel="stylesheet" href="">
    <div class="container">
    <div class="jumbotron text-center">
    <p>Lorem ipsum, dolor sit amet consectetur adipisicing elit. Illum officia asperiores impedit possimus quas 
        officiis expedita velit, at architecto iusto natus modi quaerat, nulla laboriosam atque odio amet debitis 
        et voluptas ea! Pariatur autem tempora placeat saepe doloribus minus ab maxime excepturi neque illo. Ratione,
        sapiente magnam? Perspiciatis, molestias nihil.</p>

Now, let us run the flask app to see if it works. In the terminal, execute the command below:

set FLASK_APP=''
flask run -p 5000

You should have the information below in the terminal:

* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
* Debug mode: off
* Running on (Press CTRL+C to quit)

Now, head over to the browser on to see if the application is running.

App running locally

Dockerizing the flask app

Now that our application is up and running, we need to dockerize it. Dockerization is packaging the application and its environmental libraries and dependencies into one so that the app can run anywhere without having to perform new environmental configurations.

First, we need to create a dockerfile. The docker file is used by the Docker engine to create a new docker image of the application container. It sets up an environment needed to run the application.

Create a file called Dockerfile and add the snippets below:

# For more information, please refer to
FROM python:3.8
# working directory
WORKDIR /user/src/app
# copy all files to the container
COPY .  .
# Install pip requirements
RUN python -m pip install --no-cache-dir -r requirements.txt
# port number to expose
# run the command
CMD ["python", "./"]

Running the container

In this phase, we will combine all the files and run the docker container.

Startup the Docker Desktop then, execute the command below:

docker build -t app 

Docker will execute each line of the Dockerfile as shown below:

Docker Build

Next, execute the command below to run the docker container:

docker run -p 8888:5000 -t app

Creating a user in ECS

Head over to IAM to create a user and grant the user AmazonEC2ContainerRegistryFullAccess permissions.

You can follow this link for a guide on user creation.

Execute the command below to set up the user via terminal:

aws configure

Insert the Access key ID of the user you created above and the access key. Strike the enter key to leave the remaining settings as default.

You should have the details set as the following:

AWS Access Key ID [****************TIEH]: YOUR ACCESS KEY ID
AWS Secret Access Key [****************QkxN]: YOUR ACCESS KEY
Default region name [us-east-1]:
Default output format [a]:

Create an Elastic Container Service repository

Next, we need to create a new ECS repository as shown below:

  1. In the ECS console, head over to Elastic Container Registry (ECR).
  2. Click on get started.
  3. Enter the repository name. I called mine test.
  4. Leave the remaining settings as default the click on Create repository.

You should have a screen like the below:

Creating a task

Uploading the Docker image to the repository

Next, we need to upload the docker image we created to ECR.

First, log in to the user by retrieving an authentication and using the token to authenticate the user.

Execute the command below:

aws ecr get-login-password --region YOUR REGION | docker login --username AWS --password-stdin YOUR ID.dkr.ecr.YOUR

Building the Docker image:

docker build -t test 

Tag the built image so that you can upload it to the created repository:

docker tag test:latest YOUR ID.dkr.ecr.YOUR

Finally, we need to push the image to the AWS repository:

docker push YOUR ID.dkr.ecr.YOUR

Docker push command

These commands can be found in the repository by clicking on the view push commands button on the repository page.

Creating ECS clusters

  1. In the AWS console, head over to ECS.
  2. Click the create cluster button.
  3. Select EC2 Linux + Networking then proceed to the next step.
  4. On the next page, insert the cluster name. I called mine test.
  5. Set Provisioning Model as On-Demand Instance.
  6. For EC2 Instance type, select t3a.micro.
  7. Under networking, set the VPC to the default VPC.
  8. Set the Subnets to the first subnet in the dropdown.
  9. Set the Auto-assign public IP to Enabled.
  10. For the Security group, use the default value.
  11. Click on create then wait for the process to finish.

If the procedure is successful, then you should see a window as below:

Creating the ECS Cluster

Creating task definitions

  1. Click view cluster then in the left sidebar, click on Task Definitions.
  2. Click on Create new Task DefiniTion and select EC2 then proceed to the next page.
  3. Enter the task name. I used testAppTask as my task name.
  4. Fill in the details as you desire then click add container.
  5. In the next container, enter the container name. I used testAppContainer. Enter the container image URL.
  6. Scroll down to port mappings. In the Host port, enter 8888, and in the Container port enter 5000.
  7. Click add then scroll down to create.

Creating a task

Deploy the created task

  1. In the left sidebar, click clusters.
  2. Select the created cluster.
  3. In the cluster page click the tasks tab then run a new task.
  4. On the next page, select EC2 as the launch type.
  5. Under task definition, select the task you created above. It automatically fills in.
  6. In the cluster name enter the name of the cluster we created.
  7. Last, scroll down to run the task.

If you head back to the cluster page and click the created cluster, you should see the task status as below:

Rask Running

Testing the URL

Now, we are almost done. We need to test our application deployment status.

  1. In the EC2 instances, go to network and security.
  2. Under the security groups, select the default.
  3. Scroll down to edit inbound rules.
  4. Click on the Add rule button.
  5. In the protocol select TCP, enter 8888 for the Port range, then use for the source info to be accessed from anywhere.

Your configurations should be as below before clicking the save button:

Edit inbound rules

For the selected instance, copy the public DNS URL. The instance public DNS is displayed on the page. Paste it into a new tab of your browser on port 8888.

If you did everything correctly, your app should be up and running!


In this tutorial, we went over a successful deployment of a Docker application to AWS using Elastic Container Service. We started by building a mini flask application.

You can find the source code for the application here.

Next, we dockerized the application in readiness for deployment and uploaded the docker image to an ECR repository.

Lastly, we deployed the application to AWS.

Further Reading

Please check out the links below for a further understanding of Docker, AWS, and Elastic Container Services:

Peer Review Contributions by: Srishilesh P S

About the author

Victor Elvis

Victor is a Computer Engineering student, a tech enthusiast and an upcoming writer. He loves researching on trending tech topics. In his free time, he likes to practice his programming skills.

This article was contributed by a student member of Section's Engineering Education Program. Please report any errors or innaccuracies to