Introduction to Running MediaPipe on AWS Lambda

MediaPipe, a robust cross-platform framework for building pipelines to process perceptual data, has become essential for computer vision and machine learning tasks. Deploying MediaPipe on AWS Lambda offers a serverless environment to scale and manage workloads, making it easier and more cost-effective to run MediaPipe applications. This guide walks you through deploying MediaPipe on AWS Lambda using a Docker image, covering everything from setting up your development environment to testing your Lambda function on AWS.

Setting Up Your Development Environment

Before diving into creating the Lambda function, ensure your development environment is ready. Here’s what you’ll need:

  1. AWS CLI: Install and configure the AWS CLI for easier interaction with AWS services.
  2. Docker: Since we’ll use a Docker container to package MediaPipe, Docker must be installed and running.
  3. AWS Elastic Container Registry (ECR): We’ll use ECR to store our Docker image. Ensure you have access to an AWS ECR repository.
  4. Node.js and npm: Node.js can simplify scripting and testing locally. If your handler requires it, ensure it’s installed.
  5. Python and MediaPipe libraries: Ensure Python and MediaPipe are set up locally to develop and test code effectively before deploying it to Lambda.

Creating a Basic Handler Function

The handler function is Lambda’s entry point. In this case, it will use MediaPipe to process input data (e.g., an image or video file) and return the results. Here’s an example function structure:

import mediapipe as mp

import json

def lambda_handler(event, context):

    # Initialize MediaPipe processing (e.g., face detection)

    mp_face_detection = mp.solutions.face_detection

    face_detection = mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.5)

    # Process image (assuming input from event payload)

    image_data = event.get(‘image_data’)

    results = face_detection.process(image_data)

    

    # Return the results as a JSON object

    return {

        ‘statusCode’: 200,

        ‘body’: json.dumps({‘faces’: results})

    }

This function initializes MediaPipe’s face detection, processes the input, and returns detected faces in the output. Test locally to ensure it runs as expected.

Preparation for Docker Image Creation

AWS Lambda requires code to be deployed as Docker images when using custom libraries like MediaPipe. We’ll create a Dockerfile to define the environment:

  1. Base Image: For compatibility, use(Amazon Linux as an official AWS Lambda Python image for compatibility (amazonlinux is another option using custom setups).
  2. Install Dependencies: MediaPipe requires specific libraries like opencv-python, which must be installed via pip.
  3. Copy the Code: Include your handler function and any additional code for Lambda to execute.

Sample Dockerfile

# Start with AWS Lambda’s Python base image

FROM public.ecr.aws/lambda/python:3.8

# Install MediaPipe and OpenCV

RUN pip install mediapipe opencv-python-headless

# Copy function code

COPY app.py ${LAMBDA_TASK_ROOT}

# Set the handler (file_name.function_name)

CMD [“app.lambda_handler”]

Save this Dockerfile and ensure it’s ready for the build step.

Building the Docker Image

To build the image, navigate to the directory containing the Dockerfile and run the following command:

docker build -t mediapipe-lambda .

After building, confirm the image exists with:

docker images

Pushing the Docker Image to AWS ECR

Once the Docker image is built, push it to ECR for Lambda to use.

  1. Create an ECR Repository: Use the AWS Management Console or CLI to create a new ECR repository.

aws ecr create-repository –repository-name mediapipe-lambda-repo

  1. Authenticate Docker with ECR: Run the following command to log in:

aws ecr get-login-password –region your-region | docker login –username AWS –password-stdin <your-account-id>.dkr.ecr.<your-region>.amazonaws.com

  1. Tag the Docker Image: Tag the image for ECR.

docker tag mediapipe-lambda:latest <your-account-id>.dkr.ecr.<your-region>.amazonaws.com/mediapipe-lambda-repo:latest

  1. Push the Image to ECR:

docker push <your-account-id>.dkr.ecr.<your-region>.amazonaws.com/mediapipe-lambda-repo:latest

Setting Up the AWS Lambda Function

  1. Create the Lambda Function: Go to the Lambda console and create a new function. Select Container Image as the function code source.
  2. Select the ECR Image: Choose the image you uploaded to ECR.
  3. Configure Memory and Timeout: MediaPipe may require more memory and a higher timeout. Adjust as necessary.
  4. Set Permissions: Ensure your Lambda function has the required permissions to access AWS services (e.g., S3 for image storage).

Testing the Lambda Function Locally and on AWS

Testing is crucial to ensure the function runs smoothly in Lambda’s environment.

  1. Test Locally with Docker: Run your Docker container locally to test its function:
    docker run -p 9000:8080 mediapipe-lambda:latest

Send a test event to the container:
curl -XPOST “http://localhost:9000/2015-03-31/functions/function/invocations” -d ‘{“image_data”: “<encoded-image-data>”}’

  1. Test on AWS Lambda: Create a test event with sample image data in the Lambda console. Review the output in the console logs for verification.

Conclusion

Deploying MediaPipe on AWS Lambda allows you to leverage the benefits of a serverless framework to scale MediaPipe applications efficiently. This approach combines Docker images’ flexibility with AWS Lambda’s scalability, making it an ideal solution for high-demand, cost-effective media processing.

References

Deploy Lambda functions with container images

Create a Lambda function using a container image