Empowering Serverless Apps: AWS Lambda with Container Image Deployment

Overview

AWS Lambda with Container Image Deployment enhances serverless app development, allowing developers to package their entire application into a container image, including code, runtime, and dependencies. Overcoming the limitations of AWS Lambda layers, this approach offers more control, enabling the inclusion of required libraries directly in the image. It streamlines workflows, simplifies testing, and integrates seamlessly with container-based tools. This empowers developers to build feature-rich serverless apps with scalability and cost-effectiveness.

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Introduction to Amazon ECR

Amazon ECR stands for Amazon Elastic Container Registry. It is a fully managed container registry service provided by Amazon Web Services (AWS). Amazon ECR allows you to store, manage, and deploy container images, making it easier to build, test, and run containerized applications in the AWS ecosystem.

Benefits of Amazon ECR

Secure and Private Registry: Amazon ECR provides secure storage for your container images, ensuring they are encrypted at rest and in transit. It integrates with AWS Identity and Access Management (IAM) to manage access control, allowing you to define fine-grained permissions for users and AWS services.
Integration with AWS Services: Amazon ECR seamlessly integrates with other AWS services, such as Amazon Elastic Container Service (ECS), AWS Fargate, and AWS Lambda. You can use Amazon ECR to store your container images and easily deploy them to these services for running your applications.
Scalability and Performance: Amazon ECR is built on AWS’s highly scalable infrastructure, ensuring that you can push and pull container images quickly and reliably, even with high levels of concurrency. It automatically scales to handle increased demand without impacting performance.
Lifecycle Policies: Amazon ECR allows you to define lifecycle policies for your container images. You can configure rules to automatically expire or delete older images, helping you manage storage costs and ensure that only the necessary images are retained.
Integration with Docker CLI: Amazon ECR seamlessly integrates with the Docker CLI, allowing you to push and pull container images to and from your Amazon ECR repositories using familiar Docker commands. This simplifies the workflow for developers already using Docker in their local environments.
Registry Permissions and Sharing: Amazon ECR provides options for sharing container images with other AWS accounts or making them publicly accessible. You can control who can access your container images and easily collaborate with teams or share images with external users.

Pre-requisite setup

Create a requirements.txt: This file will contain all the dependencies which are needed to execute the Python file in a docker container like the one below:

requests==2.25.1
pip==23.0.1
opencv-python-headless==4.7.0.72
numpy==1.24.2
matplotlib==3.7.0

2. Create a python file containing the code as below sample code:

try:
    import json
    import sys
    import requests
    print("All imports ok ...")
except Exception as e:
    print("Error Imports : {} ".format(e))
def lambda_handler(event, context):
    print("Hello AWS!")
    print("event = {}".format(event))
    return {
        'statusCode': 200,
    }

try:

import json

import sys

import requests

print("All imports ok ...")

except Exception as e:

print("Error Imports : {} ".format(e))

def lambda_handler(event, context):

print("Hello AWS!")

print("event = {}".format(event))

return {

'statusCode': 200,

}

3. Create Dockerfile: This text file contains instructions for building a Docker image. It serves as a blueprint or recipe for creating a Docker container with a specific configuration and set of dependencies. When you build a Docker image using a Dockerfile, it automates creating a reproducible and consistent environment for your application.

FROM public.ecr.aws/lambda/python:3.10
COPY shoe1.png ./
COPY shoe2.png ./
COPY requirements.txt ./
RUN pip3 install -r requirements.txt 
COPY myfunction.py ./
CMD ["myfunction.lambda_handler"]

FROM public.ecr.aws/lambda/python:3.10

COPY shoe1.png ./

COPY shoe2.png ./

COPY requirements.txt ./

RUN pip3 install -r requirements.txt

COPY myfunction.py ./

CMD ["myfunction.lambda_handler"]

Different ways to create a Docker Container

From local

Once you create these files under a specific folder, use the docker desktop to run the container. When you go to the docker desktop, you can see the command like the following on the homepage and execute the below command on CMD of Visual Studio Code.

docker

docker2

Run this command to build an image on the container, random similarity is the image name

docker build -t image-similarity .

1	docker build -t image-similarity .

docker3

2. Using Amazon EC2 Ubuntu

Launch an Amazon EC2 with the Ubuntu Server 20.04 AMI

docker4

SSH into the Server and Install Docker

sudo su
apt-get update
apt install docker.io
snap install docker
docker –version
apt-get install awscli –y
docker pull public.ecr.aws/lambda/python:3.7

sudo su

apt-get update

apt install docker.io

snap install docker

docker –version

apt-get install awscli –y

docker pull public.ecr.aws/lambda/python:3.7

AWS IAM role with the below permission

AmazonEC2ContainerRegistryFullAccess	
AmazonElasticContainerRegistryPublicFullAccess

1 2	AmazonEC2ContainerRegistryFullAccess AmazonElasticContainerRegistryPublicFullAccess

Pull the AWS base image python3.7 from Amazon Gallery: https://gallery.ecr.aws/lambda/python

docker pull public.ecr.aws/lambda/python:3.10

1	docker pull public.ecr.aws/lambda/python:3.10

Using the below command, check docker image is successfully pulled or not:

docker image ls

1	docker image ls

Run the image as a container

docker run -it -d IMAGEID /bin/bash
docker exec -it CONTAINERID bash

1 2	docker run -it -d IMAGEID /bin/bash docker exec -it CONTAINERID bash

Install all the dependencies inside the docker container

pip install awslambdaric
Pip install numpy
Pip install pandas
Pip install scikit-learn
Pip install opencv-python
Pip install matplotlib

pip install awslambdaric

Pip install numpy

Pip install pandas

Pip install scikit-learn

Pip install opencv-python

Pip install matplotlib

Steps to upload Docker image from local/Amazon EC2 to Amazon ECR

Create a private repository Amazon ECR as shown below:

2. After the repository is created. Select the repository and click on view push commands

ec2

ec3

3. Execute all the above commands

ec4

ec5

ec7

4. After running all the commands, we could see that image is pushed in Amazon ECR

ecr4

Steps to create AWS Lambda using Amazon ECR

Go to the AWS Lambda function. Click on create function, choose Container Image, provide the name for AWS Lambda, browse the container image, and click Create.

ecr

ecr2

2. Once AWS Lambda is created, click Test.

ecr3

Conclusion

Deploying an AWS Lambda function using a container image provides several advantages for running serverless applications. By leveraging AWS Lambda’s support for custom container environments, you can package your application code, dependencies, and configurations into a container image and deploy it as an AWS Lambda function.

Drop a query if you have any questions regarding AWS Lambda, Amazon ECR and we will get back to you quickly.

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FAQs

1. Why should we create AWS lambda using Amazon ECR?

ANS: – By leveraging an Amazon ECR Docker image for your Lambda function, you can work around dependencies that don’t work well with AWS Lambda layers and gain more flexibility in managing dependencies within a containerized environment. However, it’s essential to monitor and optimize your function’s performance and resource utilization, considering the size and content of the Docker image.

2. Can the AWS Lambda function be created using multiple Docker images from different repositories?

ANS: – No, an AWS Lambda function can only use a single Docker image from a single Amazon ECR repository. AWS Lambda functions are designed to be self-contained and operate within the context of a single container image. If your application requires multiple Docker images, you would typically split the functionality into separate AWS Lambda functions, each using its container image.

3. Can I update the Docker image in Amazon ECR without redeploying the AWS Lambda function?

ANS: – Yes, you can update the Docker image in Amazon ECR without redeploying the Lambda function. To update the image, you can build a new version of the image with the desired changes, tag it with a new version or tag, and push it to the ECR repository. The AWS Lambda function can be configured to use the latest version or a specific version or tag of the image. The next invocation of the Lambda function will automatically use the updated image.

WRITTEN BY Hridya Hari

Hridya Hari works as a Research Associate - Data and AIoT at CloudThat. She is a data science aspirant who is also passionate about cloud technologies. Her expertise also includes Exploratory Data Analysis.