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I'm using Amazon's Elastic Beanstalk to deploy an example Flask app. I can get a simple "Hello World" app deployed perfectly, but now I'm trying to deploy the app with scipy as a requirement.

I've included the necessary packages in my .ebextensions/:

packages:
    yum:
        gcc-c++: []
        gcc-gfortran: []
        python27-devel: []
        atlas-sse3-devel: []
        lapack-devel: []
        libpng-devel: []
        zlib-devel: []
        postgresql93-devel: []

If I leave scipy and numpy in the requirements.txt file, the deploy fails because numpy has to be installed before scipy.

I can fix this by commenting out scipy from my requirements.txt, and adding a container_commands section to my .ebextensions:

container_commands:
    01_install_scipy:
        command: "pip install scipy"

I don't like this approach because I want all of my requirements to live in my requirements.txt file for development purposes. Selectively commenting out pip requirements from the requirements.txt file feels wrong and can get complicated if I have a bunch of other libraries that depend on scipy.

Additionally, building scipy from source takes a very long time, especially on relatively small EC2 instances. I have tried installing using yum, but this leads to using old versions of scipy and not having scipy in the virtual environment.

So, I have two problems:

  1. requirements.txt: Is there any way to install scipy to my virtual environment that doesn't require me to comment out selective requirements from my requirements.txt file?
  2. Speed: Is there any way to pre-compile scipy and still make it available in the virtual environment?
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You should package your application (zip) before to deploy it. This package should include all deps your application need so you don't have to pre-install module when deploying.

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