Deploying AWS Lambda with Docker: Bypass the 250MB Layer Limit

Introduction Deploying Lambda functions with large dependencies can be frustrating—especially when you bump into the strict 250MB layer limit. But there’s a workaround: containerized deployment. In this guide, we will walk through deploying a Lambda function using Docker images, bypassing size restrictions while keeping setup simple. No advanced AWS knowledge required—just a step-by-step approach to get you up and running. Requirements Docker AWS CLI VS Code Creating src and main.py Create a folder by running the command mkdir [folder-name] cd into the new folder: cd [folder-name] Create source folder(src)in the root folder that will house your python file by running the following command: mkdir src and cd into it to create your python file. I named it main.py in my case but it can be any name. We will use a basic Lambda handler for this example, it logs the event/context details and responds with 'Hello world'. import logging logging.basicConfig(level=logging.INFO, force=True) logger = logging.getLogger(name) def handler(event, context): logger.info(f"Received event\n{event}") logger.info(f"Received context\n{context}") return "Hello world" Creating Dockerfile Next is to create Dockerfile at the root folder by running the command mkdir Dockerfile We are using AWS's Python base image. For other languages, see AWS's documentation on Lambda base images. # Step 1: Pull the base image FROM public.ecr.aws/lambda/python:3.10 # Step 2: Copy requirements.txt COPY ./requirements.txt ${LAMBDA_TASK_ROOT} # Step 3: Install the specified packages RUN pip install -r requirements.txt # Step 4: Copy function code COPY ./src ${LAMBDA_TASK_ROOT} # Step 5: Set the CMD to your handler CMD [ "main.handler" ] Ensure that your file path is accurate. Creating Elastic Container Registry Head over to your AWS console to create ECR to house your container image. Search for ECR and select Elastic Container Registry Click create a repository. Choose a name for your repository, leave other settings as default and click create. Open the newly created repository and select view push commands It will open up the command to build and push your image to your ECR. Build and Push docker image Copy the first command and run it at the root level in VS Code Copy and run the second command to build your docker image Next is to copy the third command to tag the image Finally copy the last command to push the image to ECR You can confirm that the image has been pushed by refreshing your ECR page. Creating Lambda function We need to create a lambda by searching for lambda function and select lambda. Click create a function Choose container image option, give a name to your function, copy and paster the URI of your image and click create. Test your function Test your function to see if it is working. Click on the Test tab and select Test. Now you can customize the handler in ./src/main.py and add required dependencies to ./requirements.txt for your specific use case. Thanks for staying till the end

May 6, 2025 - 17:47

Deploying AWS Lambda with Docker: Bypass the 250MB Layer Limit

Introduction

Deploying Lambda functions with large dependencies can be frustrating—especially when you bump into the strict 250MB layer limit. But there’s a workaround: containerized deployment.

In this guide, we will walk through deploying a Lambda function using Docker images, bypassing size restrictions while keeping setup simple. No advanced AWS knowledge required—just a step-by-step approach to get you up and running.

Requirements

Docker
AWS CLI
VS Code

Creating src and main.py

Create a folder by running the command

mkdir [folder-name]
cd into the new folder:

cd [folder-name]

Create source folder(src)in the root folder that will house your python file by running the following command:

mkdir src

and cd into it to create your python file.

I named it main.py in my case but it can be any name. We will use a basic Lambda handler for this example, it logs the event/context details and responds with 'Hello world'.

import logging

logging.basicConfig(level=logging.INFO, force=True)
logger = logging.getLogger(__name__)

def handler(event, context):
    logger.info(f"Received event\n{event}")
    logger.info(f"Received context\n{context}")

    return "Hello world"

Creating Dockerfile

Next is to create Dockerfile at the root folder by running the command mkdir Dockerfile

We are using AWS's Python base image. For other languages, see AWS's documentation on Lambda base images.

# Step 1: Pull the base image
FROM public.ecr.aws/lambda/python:3.10

# Step 2: Copy requirements.txt
COPY ./requirements.txt ${LAMBDA_TASK_ROOT}

# Step 3: Install the specified packages
RUN pip install -r requirements.txt

# Step 4: Copy function code
COPY ./src ${LAMBDA_TASK_ROOT}

# Step 5: Set the CMD to your handler
CMD [ "main.handler" ]

Ensure that your file path is accurate.

Creating Elastic Container Registry

Head over to your AWS console to create ECR to house your container image.
Search for ECR and select Elastic Container Registry
Click create a repository.
Choose a name for your repository, leave other settings as default and click create.
Open the newly created repository and select view push commands
It will open up the command to build and push your image to your ECR.

Build and Push docker image

Copy the first command and run it at the root level in VS Code
Copy and run the second command to build your docker image
Next is to copy the third command to tag the image
Finally copy the last command to push the image to ECR
You can confirm that the image has been pushed by refreshing your ECR page.

Creating Lambda function

We need to create a lambda by searching for lambda function and select lambda.
Click create a function
Choose container image option, give a name to your function, copy and paster the URI of your image and click create.

Test your function

Test your function to see if it is working. Click on the Test tab and select Test. Now you can customize the handler in ./src/main.py and add required dependencies to ./requirements.txt for your specific use case.

Thanks for staying till the end