Using numpy in AWS Lambda

Updated to include the solution here, rather than a link:

After much effort, I found that I had to create my deployment package from within a python3.6 virtualenv, rather than directly from the host machine. I did the following within a Ubuntu 16.04 docker image. This assumes that you have python3.6, virtualenv and awscli already installed/configured, and that your lambda function code is in the ~/lambda_code directory:

1) cd ~ (We'll build the virtualenv in the home directory)

2) virtualenv venv --python=python3.6 (Create the virtual environment)

3) source venv/bin/activate (Activate the virtual environment)

4) pip install numpy

5) cp -r ~/venv/lib/python3.6/site-packages/* ~/lambda_code (Copy all installed packages into root level of lambda_code directory. This will include a few unnecessary files, but you can remove those yourself if needed)

6) cd ~/lambda_code

7) zip -r9 ~/package.zip . (Zip up the lambda package)

8) aws lambda update-function-code --function-name my_lambda_function --zip-file fileb://~/package.zip (Upload to AWS)

Your lambda function should now be able to import numpy with no problems.

If you want a more out-of-the-box solution, you could consider using serverless to deploy your lambda function. Before I found the above solution, I followed the guide here and was able to run numpy successfully in a python3.6 lambda function.

As of 2018 it's best to just use the inbuilt layers functionality.

AWS have actually released a pre-made one with numpy in it: https://aws.amazon.com/blogs/aws/new-for-aws-lambda-use-any-programming-language-and-share-common-components/

An easy way to make your lambda function support the numpy library for python 3.7:

1. Go to your lambda function page
2. Find the Layers section at the bottom of the page.
3. Click on Add a layer.
4. Choose AWS layers as layer source.
5. Select AWSLambda-Python37-Scipy1x as AWS layers.
6. Select 37 for version.
7. And finally click on Add.

Now your lambda function is ready to support numpy.