4.1.2 • Public • Published

    Serverless Python Requirements

    serverless CircleCI appveyor npm code style: prettier

    A Serverless v1.x plugin to automatically bundle dependencies from requirements.txt and make them available in your PYTHONPATH.

    Requires Serverless >= v1.12


    sls plugin install -n serverless-python-requirements

    🍎🍺🐍 Mac Brew installed Python notes

    Cross compiling!

    Compiling non-pure-Python modules or fetching their manylinux wheels is supported on non-linux OSs via the use of Docker and the docker-lambda image. To enable docker usage, add the following to your serverless.yml:

        dockerizePip: true

    The dockerizePip option supports a special case in addition to booleans of 'non-linux' which makes it dockerize only on non-linux environments.

    To utilize your own Docker container instead of the default, add the following to your serverless.yml:

        dockerImage: <image name>:tag

    This must be the full image name and tag to use, including the runtime specific tag if applicable.

    Alternatively, you can define your Docker image in your own Dockerfile and add the following to your serverless.yml:

        dockerFile: ./path/to/Dockerfile

    With Dockerfile the path to the Dockerfile that must be in the current folder (or a subfolder). Please note the dockerImage and the dockerFile are mutually exclusive.

    To install requirements from private git repositories, add the following to your serverless.yml:

        dockerizePip: true
        dockerSsh: true

    The dockerSsh option will mount your $HOME/.ssh/id_rsa and $HOME/.ssh/known_hosts as a volume in the docker container. If your SSH key is password protected, you can use ssh-agent because $SSH_AUTH_SOCK is also mounted & the env var set. It is important that the host of your private repositories has already been added in your $HOME/.ssh/known_hosts file, as the install process will fail otherwise due to host authenticity failure.

    🏁 Windows notes

    Pipenv support ✨🍰✨

    If you include a Pipfile and have pipenv installed instead of a requirements.txt this will use pipenv lock -r to generate them. It is fully compatible with all options such as zip and dockerizePip. If you don't want this plugin to generate it for you, set the following option:

        usePipenv: false

    Dealing with Lambda's size limitations

    To help deal with potentially large dependencies (for example: numpy, scipy and scikit-learn) there is support for compressing the libraries. This does require a minor change to your code to decompress them. To enable this add the following to your serverless.yml:

        zip: true

    and add this to your handler module before any code that imports your deps:

      import unzip_requirements
    except ImportError:

    Slim Package

    Works on non 'win32' environments: Docker, WSL are included
    To remove the tests, information and caches from the installed packages, enable the slim option. This will: strip the .so files, remove __pycache__ directories and dist-info directories.

        slim: true

    Custom Removal Patterns

    To specify additional directories to remove from the installed packages, define the patterns using regex as a slimPatterns option in serverless config:

        slim: true
          - "*.egg-info*"

    This will remove all folders within the installed requirements that match the names in slimPatterns

    Omitting Packages

    You can omit a package from deployment with the noDeploy option. Note that dependencies of omitted packages must explicitly be omitted too. By default, this will not install the AWS SDKs that are already installed on Lambda. This example makes it instead omit pytest:

          - pytest

    Extra Config Options

    extra pip arguments

    You can specify extra arguments to be passed to pip like this:

          dockerizePip: true
              - --cache-dir
              - .requirements-cache

    When using --cache-dir don't forget to also exclude it from the package.

        - .requirements-cache/**

    Customize requirements file name

    Some pip workflows involve using requirements files not named requirements.txt. To support these, this plugin has the following option:

        fileName: requirements-prod.txt

    Per-function requirements

    If you have different python functions, with different sets of requirements, you can avoid including all the unecessary dependencies of your functions by using the following structure:

    ├── serverless.yml
    ├── function1
    │      ├── requirements.txt
    │      └──
    └── function2
           ├── requirements.txt

    With the content of your serverless.yml containing:

      individually: true
        handler: index.handler
        module: function1
        handler: index.handler
        module: function2

    The result is 2 zip archives, with only the requirements for function1 in the first one, and only the requirements for function2 in the second one.

    Quick notes on the config file:

    • The module field must be used to tell the plugin where to find the requirements.txt file for each function.
    • The handler field must not be prefixed by the folder name (already known through module) as the root of the zip artifact is already the path to your function.

    Customize Python executable

    Sometimes your Python executable isn't available on your $PATH as python2.7 or python3.6 (for example, windows or using pyenv). To support this, this plugin has the following option:

        pythonBin: /opt/python3.6/bin/python

    Vendor library directory

    For certain libraries, default packaging produces too large an installation, even when zipping. In those cases it may be necessary to tailor make a version of the module. In that case you can store them in a directory and use the vendor option, and the plugin will copy them along with all the other dependencies to install:

        vendor: ./vendored-libraries
        handler: hello.handler
        vendor: ./hello-vendor # The option is also available at the function level 

    Manual invocations

    The .requirements and using zip support) files are left behind to speed things up on subsequent deploys. To clean them up, run sls requirements clean. You can also create them (and unzip_requirements if using zip support) manually with sls requirements install.

    Invalidate requirements caches on package

    If you are using your own Python library, you have to cleanup .requirements on any update. You can use the following option to cleanup .requirements everytime you package.

        invalidateCaches: true

    🍎🍺🐍 Mac Brew installed Python notes

    Brew wilfully breaks the --target option with no seeming intention to fix it which causes issues since this uses that option. There are a few easy workarounds for this:


    • Create a virtualenv and activate it while using serverless.


    Also, brew seems to cause issues with pipenv, so make sure you install pipenv using pip.

    🏁 Windows dockerizePip notes

    For usage of dockerizePip on Windows do Step 1 only if running serverless on windows, or do both Step 1 & 2 if running serverless inside WSL.

    1. Enabling shared volume in Windows Docker Taskbar settings
    2. Installing the Docker client on Windows Subsystem for Linux (Ubuntu)

    Native Code Dependencies During Build

    Some Python packages require extra OS dependencies to build successfully. To deal with this, replace the default image (lambci/lambda:python3.6) with a Dockerfile like:

    # AWS Lambda execution environment is based on Amazon Linux 1
    FROM amazonlinux:1
    # Install Python 3.6
    RUN yum -y install python36 python36-pip
    # Install your dependencies
    RUN curl -s | python3
    RUN yum -y install python3-devel mysql-devel gcc
    # Set the same WORKDIR as default image
    RUN mkdir /var/task
    WORKDIR /var/task

    Then update your serverless.yml:

        dockerFile: Dockerfile

    Native Code Dependencies During Runtime

    Some Python packages require extra OS libraries (*.so files) at runtime. You need to manually include these files in the root directory of your Serverless package. The simplest way to do this is to commit the files to your repository:

    For instance, the mysqlclient package requires If you use the Dockerfile from the previous section, you can extract this file from the builder Dockerfile:

    1. Extract the library:
    docker run --rm -v "$(pwd):/var/task" sls-py-reqs-custom cp -v /usr/lib64/mysql57/ .

    (If you get the error Unable to find image 'sls-py-reqs-custom:latest' locally, run sls package to build the image.) 2. Commit to your repo:

    git add
    git commit -m "Add"
    1. Verify the library gets included in your package:
    sls package
    zipinfo .serverless/

    (If you can't see the library, you might need to adjust your package include/exclude configuration in serverless.yml.)


    • @dschep - Lead developer & maintainer
    • @azurelogic - logging & documentation fixes
    • @abetomo - style & linting
    • @angstwad - deploy --function support
    • @mather - the cache invalidation option
    • @rmax - the extra pip args option
    • @bsamuel-ui - Python 3 support
    • @suxor42 - fixing permission issues with Docker on Linux
    • @mbeltran213 - fixing docker linux -u option bug
    • @Tethik - adding usePipenv option
    • @miketheman - fixing bug with includes when using zip option
    • @wattdave - fixing bug when using deploymentBucket
    • @heri16 - fixing Docker support in Windows
    • @ryansb - package individually support
    • @cgrimal - Private SSH Repo access in Docker, dockerFile option to build a custom docker image, real per-function requirements, and the vendor option
    • @kichik - Imposed windows & noDeploy support, switched to adding files straight to zip instead of creating symlinks, and improved pip chache support when using docker.
    • @dee-me-tree-or-love - the slim package option
    • @alexjurkiewicz - docs about docker workflows


    npm i serverless-python-common-requirements

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