Error importing rpy2.robjects in AWS Lambda: No module named '_cffi_backend'

Question

I am working on an AWS Lambda function that integrates a Python Flask API with an R machine learning model using rpy2. However, I encounter the following error when trying to import rpy2.robjects: Attempting to import rpy2.robjects... ImportError encountered: No module named '_cffi_backend'

Current Environment -

• AWS Lambda Runtime for Lambda Function: Python 3.8
• AWS Lambda Layers: The Lambda function is configured with two custom layers-
  1. one containing the R runtime and required libraries (/opt/R) (This is working fine, I tried printing R version in my python lambda function.)
  2. and another with Python dependencies, including rpy2.

Despite ensuring that rpy2 and its dependencies (such as cffi) are installed in the Python layer under /opt/python/lib/python3.8/site-packages, the function fails with the error: No module named '_cffi_backend'.

The same setup works locally on my MacBook, but the issue persists in AWS Lambda.

I am looking for guidance on resolving this error and ensuring that rpy2 and its dependencies work correctly in the AWS Lambda environment. Specifically, I need help understanding why _cffi_backend is not recognized and what steps I can take to fix this issue.

Answer 1

When running an AWS Lambda function that imports rpy2.robjects, the following error occurs:

ModuleNotFoundError: No module named '_cffi_backend'

This error is caused by the missing _cffi_backend module, which is part of the cffi library. The issue typically arises because AWS Lambda uses Amazon Linux as its runtime environment, and cffi (a dependency of rpy2) includes compiled components that must match the Lambda runtime environment. If the package is installed on a different OS (e.g., macOS or Windows), it won’t work on Lambda.

You need to package your Lambda function with rpy2 and its dependencies in an Amazon Linux environment. Here are two approaches:

Option 1: Use a Lambda Layer A Lambda Layer allows you to bundle dependencies separately and reuse them across multiple functions.

Create a Directory for Dependencies: mkdir lambda-layer && cd lambda-layer mkdir python

Set Up Amazon Linux Environment: Use Docker to mimic the AWS Lambda runtime: docker run -it --rm -v $(pwd):/lambda amazonlinux:2 /bin/bash

Install Required Packages in Docker: Inside the Docker container:

yum update -y yum install -y python3-pip python3-devel gcc libffi-devel pip3 install rpy2 cffi -t /lambda/python exit

Package the Layer: Compress the python directory into a zip file: cd .. zip -r lambda-layer.zip python

Upload the Layer to AWS: Go to the AWS Management Console, create a new Lambda Layer, and upload the lambda-layer.zip. Attach the layer to your Lambda function.

Option 2: Package the Function with Dependencies If you prefer not to use a Lambda Layer, you can include all dependencies directly in your function deployment package: Set Up Amazon Linux Environment:

docker run -it --rm -v $(pwd):/lambda amazonlinux:2 /bin/bash Install Dependencies Inside Docker: yum update -y yum install -y python3-pip python3-devel gcc libffi-devel pip3 install rpy2 cffi -t /lambda exit

Package Your Function: Add your Python function file (e.g., lambda_function.py) to the /lambda directory, then compress the contents:

cd lambda zip -r function.zip .

Upload to AWS Lambda: Upload the function.zip file directly to your Lambda function.