This repo contains a build.sh script that's intended to be run in an Amazon Linux docker container, and build scikit-learn, numpy, and scipy for use in AWS Lambda. For more info about how the script works, and how to use it, see my blog post on deploying sklearn to Lambda.

There was an older version of this repo, now archived in the ec2-build-process branch, used an EC2 instance to perform the build process and an Ansible playbook to execute the build. That version still works, but the new dockerized version doesn't require you to launch a remote instance.

To build the zipfile, pull the Amazon Linux image and run the build script in it.

$ docker pull amazonlinux:2016.09
$ docker run -v $(pwd):/outputs -it amazonlinux:2016.09 \
      /bin/bash /outputs/build.sh

That will make a file called venv.zip in the local directory that's around 40MB.

Once you run this, you'll have a zipfile containing sklearn and its dependencies, to use them add your handler file to the zip, and add the lib directory so it can be used for shared libs. The minimum viable sklearn handler would thus look like:

import os
import ctypes

for d, _, files in os.walk('lib'):
    for f in files:
        if f.endswith('.a'):
            continue
        ctypes.cdll.LoadLibrary(os.path.join(d, f))

import sklearn

def handler(event, context):
    # do sklearn stuff here
    return {'yay': 'done'}

With just compression and stripped binaries, the full sklearn stack weighs in at 39 MB, and could probably be reduced further by:

1. Pre-compiling all .pyc files and deleting their source
2. Removing test files
3. Removing documentation

For my purposes, 39 MB is sufficiently small, if you have any improvements to share pull requests or issues are welcome.

This project is MIT Licensed, for license info on the numpy, scipy, and sklearn packages see their respective sites. Full text of the MIT license is in LICENSE.txt.