How to install external modules in a Python Lambda Function created by AWS CDK?


I’m using the Python AWS CDK in Cloud9 and I’m deploying a simple Lambda function that is supposed to send an API request to Atlassian’s API when an Object is uploaded to an S3 Bucket (also created by the CDK). Here is my code for CDK Stack:

from aws_cdk import core
from aws_cdk import aws_s3
from aws_cdk import aws_lambda
from aws_cdk.aws_lambda_event_sources import S3EventSource

class JiraPythonStack(core.Stack):
    def __init__(self, scope: core.Construct, id: str, **kwargs) -> None:
        super().__init__(scope, id, **kwargs)

        # The code that defines your stack goes here
        jira_bucket = aws_s3.Bucket(self,

        event_lambda = aws_lambda.Function(


The lambda function code uses the requests module which I’ve imported. However, when I check the CloudWatch Logs, and test the lambda function – I get:

Unable to import module ‘JiraFileLambda’: No module named ‘requests’

My Question is: How do I install the requests module via the Python CDK?

I’ve already looked around online and found this. But it seems to directly modify the lambda function, which would result in a Stack Drift (which I’ve been told is BAD for IaaS). I’ve also looked at the AWS CDK Docs too but didn’t find any mention of external modules/libraries (I’m doing a thorough check for it now) Does anybody know how I can work around this?

Edit: It would appear I’m not the only one looking for this.

Here’s another GitHub issue that’s been raised.



It now appears as though there is a new type of (experimental) Lambda Function in the CDK known as the PythonFunction. The Python docs for it are here. And this includes support for adding a requirements.txt file which uses a docker container to add them to your function. See more details on that here. Specifically:

If requirements.txt or Pipfile exists at the entry path, the construct will handle installing all required modules in a Lambda compatible Docker container according to the runtime.

Original Answer:

So this is the awesome bit of code my manager wrote that we now use:

    def create_dependencies_layer(self, project_name, function_name: str) -> aws_lambda.LayerVersion:
        requirements_file = "lambda_dependencies/" + function_name + ".txt"
        output_dir = ".lambda_dependencies/" + function_name
        # Install requirements for layer in the output_dir
        if not os.environ.get("SKIP_PIP"):
            # Note: Pip will create the output dir if it does not exist
                f"pip install -r {requirements_file} -t {output_dir}/python".split()
        return aws_lambda.LayerVersion(
            project_name + "-" + function_name + "-dependencies",

It’s actually part of the Stack class as a method (not inside the init). The way we have it set up here is that we have a folder called lambda_dependencies which contains a text file for every lambda function we are deploying which just has a list of dependencies, like a requirements.txt.

And to utilise this code, we include in the lambda function definition like this:

        get_data_lambda = aws_lambda.Function(
            layers=[self.create_dependencies_layer(PROJECT_NAME, GET_DATA_LAMBDA_NAME)]

Answered By – Jamie

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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