title: Deploying python with docker, images too big
body: <p>We've built a large python repo that uses lots of libraries (numpy, scipy, tensor flow, ...) And have managed these dependencies through a conda environment. Basically we have lots of developers contributing and anytime someone needs a new library for something they are working on they 'conda install' it. </p>
  
  <p>Fast forward to today and now we need to deploy some applications that use our repo. We are deploying using docker, but are finding that these images are really large and causing some issues, e.g. 10+ GB. However each individual application only uses a subset of all the dependencies in the environment.yml. </p>
  
  <p>Is there some easy strategy for dealing with this problem? In a sense I need to know the dependencies for each application, but I'm not sure how to do this in an automated way. </p>
  
  <p>Any help here would be great. I'm new to this whole AWS, Docker, and python deployment thing... We're really a bunch of engineers and scientists who need to scale up our software. We have something that works, it just seems like there has to be a better way .</p>