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Wesley Cheek
Wesley Cheek

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Deploy a Docker built Lambda function with AWS CDK

This project is a minimum working example of deploying an AWS Lambda function using Docker and AWS CDK.

Deploying Lambda functions using Docker has a number of benefits

  • Package all necessary libraries into a single Docker image
  • Bypass AWS Lambda's size constraint of 512 mb. Docker images stored on AWS ECR have a maximum size of 10 gb. I will show you how to deploy Tensorflow models in another post!
  • It’s easy!

GitHub Repository

CDK init & deploy

I won’t cover setting up CDK and bootstrapping the environment. You can find that information here.

Once you have set up CDK, we need to set up the project:

  1. mkdir cdk_docker_lambda && cd cdk_docker_lambda

  2. cdk init --language python

  3. source .venv/bin/activate

  4. pip install -r requirements.txt && pip install -r requirements-dev.txt

    Now deploy empty stack to AWS:

  5. cdk deploy

Stack design

Our stack will deploy only a lambda function. The lambda function will be built using Docker, so be sure to have Docker installed and the Docker daemon running.

# cdk_docker_lambda/

from aws_cdk import Stack
from aws_cdk import aws_lambda as _lambda
from constructs import Construct

class CdkDockerLambdaStack(Stack):
    def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:
        super().__init__(scope, construct_id, **kwargs)


    def build_lambda_func(self):
        self.prediction_lambda = _lambda.DockerImageFunction(
            # Function name on AWS
            # Use aws_cdk.aws_lambda.DockerImageCode.from_image_asset to build
            # a docker image on deployment
                # Directory relative to where you execute cdk deploy
                # contains a Dockerfile with build instructions

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Lambda function

Create a new directory in cdk_docker_lambda called ExampleDockerLambda. Here we are going to put a Dockerfile, requirements.txt which holds our function’s dependencies, and the lambda function itself,


FROM amazon/aws-lambda-python:latest

LABEL maintainer="Wesley Cheek"
# Installs python, removes cache file to make things smaller
RUN yum update -y && \
    yum install -y python3 python3-dev python3-pip gcc && \
    rm -Rf /var/cache/yum

# Copies requirements.txt file into the container
COPY requirements.txt ./
# Installs dependencies found in your requirements.txt file
RUN pip install -r requirements.txt

# Be sure to copy over the function itself!
# Goes last to take advantage of Docker caching.

# Points to the handler function of your lambda function
CMD ["example_docker_lambda.handler"]
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# Very simple

import requests

def handler(event, context):
    return "Hello Lambda!"

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Now cdk deploy. AWS CDK will build your new Lambda function using Docker and then push it for you to the ECR repository that was originally created for you by running cdk bootstrap during the CDK setup. How convenient.

After the image is built and pushed, CDK will deploy the necessary infrastructure. You can navigate to the AWS CloudFormation console to view the deployment. It should only take a couple minutes.

Once finished, you will find your beautiful Docker deployed Lambda function on the Lambda console

Lambda console showing new function

Test your Lambda function

We can use any kind of event since the function always just returns a string.

Successful test of new lambda function

Have fun easily deploying any sized Lambda you’d like using AWS CDK and Docker! Make sure to cdk destroy when you're done to avoid any charges.

Top comments (3)

davewilton profile image
David Wilton

Thanks for this I found it really helpful. One thing to note, in the Docker file the .py is copied on before the requirements and pip install. The result will be that every time the python file is changed and you debug docker will rebuild the image. Docker has some very clever caching, so copy the python file last!

wesleycheek profile image
Wesley Cheek • Edited

Ah that's a good tip! Thanks - I've added your suggestion to the example Dockerfile.

niterich profile image

Very straightforward and simple, thanks for this!