Serverless Python Requirements

!Github Actions

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

---

_Originally developed by Capital One, now maintained in scope of Serverless, Inc_

_Capital One considers itself the bank a technology company would build. It's delivering best-in-class innovation so that its millions of customers can manage their finances with ease. Capital One is all-in on the cloud and is a leader in the adoption of open source, RESTful APIs, microservices and containers. We build our own products and release them with a speed and agility that allows us to get new customer experiences to market quickly. Our engineers use artificial intelligence and machine learning to transform real-time data, software and algorithms into the future of finance, reimagined._

---

Install

``shell
sls plugin install -n serverless-python-requirements
`

This will automatically add the plugin to your project's package.json and the plugins section of its
serverless.yml. That's all that's needed for basic use! The plugin will now bundle your python
dependencies specified in your requirements.txt or Pipfile when you run sls deploy.

For a more in depth introduction on how to use this plugin, check out
this post on the Serverless Blog

If you're on a mac, check out these notes about using python installed by brew.

Cross compiling

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

`yaml
custom:
pythonRequirements:
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:

`yaml
custom:
pythonRequirements:
dockerImage: :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:

`yaml
custom:
pythonRequirements:
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:

`yaml
custom:
pythonRequirements:
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.

In case you want to use a different key, you can specify the path (absolute) to it through dockerPrivateKey option:

`yaml
custom:
pythonRequirements:
dockerizePip: true
dockerSsh: true
dockerPrivateKey: /home/.ssh/id_ed25519
`

If your SSH key is password protected, you can use ssh-agent
because $SSH_AUTH_SOCK is also mounted & the env var is 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.

You can also pass environment variables to docker by specifying them in dockerEnv
option:

`yaml
custom:
pythonRequirements:
dockerEnv:
- https_proxy
`

:checkered_flag: Windows notes

:sparkles::cake::sparkles: Pipenv support

Requires pipenv in version 2022-04-08 or higher.

If you include a Pipfile and have pipenv installed, this will use pipenv to generate requirements instead of a requirements.txt. 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:

`yaml
custom:
pythonRequirements:
usePipenv: false
`

:sparkles::pencil::sparkles: Poetry support

If you include a pyproject.toml and have poetry installed instead of a requirements.txt this will use
poetry export --without-hashes -f requirements.txt -o requirements.txt --with-credentials 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:

`yaml
custom:
pythonRequirements:
usePoetry: false
`

Be aware that if no poetry.lock file is present, a new one will be generated on the fly. To help having predictable builds,
you can set the requirePoetryLockFile flag to true to throw an error when poetry.lock is missing.

`yaml
custom:
pythonRequirements:
requirePoetryLockFile: false
`

If your Poetry configuration includes custom dependency groups, they will not be installed automatically. To include them in the deployment package, use the poetryWithGroups, poetryWithoutGroups and poetryOnlyGroups options which wrap poetry export's --with, --without and --only parameters.

`yaml
custom:
pythonRequirements:
poetryWithGroups:
- internal_dependencies
- lambda_dependencies
`

$3

Poetry by default generates the exported requirements.txt file with -e and that breaks pip with -t parameter
(used to install all requirements in a specific folder). In order to fix that we remove all -e from the generated file but,
for that to work you need to add the git dependencies in a specific way.

Instead of:

`toml
[tool.poetry.dependencies]
bottle = {git = "git@github.com/bottlepy/bottle.git", tag = "0.12.16"}
`

Use:

`toml
[tool.poetry.dependencies]
bottle = {git = "https://git@github.com/bottlepy/bottle.git", tag = "0.12.16"}
`

Or, if you have an SSH key configured:

`toml
[tool.poetry.dependencies]
bottle = {git = "ssh://git@github.com/bottlepy/bottle.git", tag = "0.12.16"}
`

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:

`yaml
custom:
pythonRequirements:
zip: true
`

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

`python
try:
import unzip_requirements
except ImportError:
pass
`

$3

_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__
and dist-info directories as well as .pyc and .pyo files.

`yaml
custom:
pythonRequirements:
slim: true
`

#### Custom Removal Patterns

To specify additional directories to remove from the installed packages,
define a list of patterns in the serverless config using the slimPatterns
option and glob syntax. These patterns will be added to the default ones (/.py[c|o], /__pycache__, */.dist-info*).
Note, the glob syntax matches against whole paths, so to match a file in any
directory, start your pattern with **/.

`yaml
custom:
pythonRequirements:
slim: true
slimPatterns:
- '*/.egg-info*'
`

To overwrite the default patterns set the option slimPatternsAppendDefaults to false (true by default).

`yaml
custom:
pythonRequirements:
slim: true
slimPatternsAppendDefaults: false
slimPatterns:
- '*/.egg-info*'
`

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

#### Option not to strip binaries

In some cases, stripping binaries leads to problems like "ELF load command address/offset not properly aligned", even when done in the Docker environment. You can still slim down the package without *.so files with:

`yaml
custom:
pythonRequirements:
slim: true
strip: false
`

$3

Another method for dealing with large dependencies is to put them into a
Lambda Layer.
Simply add the layer option to the configuration.

`yaml
custom:
pythonRequirements:
layer: true
`

The requirements will be zipped up and a layer will be created automatically.
Now just add the reference to the functions that will use the layer.

`yaml
functions:
hello:
handler: handler.hello
layers:
- Ref: PythonRequirementsLambdaLayer
`

If the layer requires additional or custom configuration, add them onto the layer option.

`yaml
custom:
pythonRequirements:
layer:
name: ${self:provider.stage}-layerName
description: Python requirements lambda layer
compatibleRuntimes:
- python3.7
licenseInfo: GPLv3
allowedAccounts:
- '*'
`

Omitting Packages

You can omit a package from deployment with the noDeploy option. Note that
dependencies of omitted packages must explicitly be omitted too.

This example makes it instead omit pytest:

`yaml
custom:
pythonRequirements:
noDeploy:
- pytest
`

Extra Config Options

$3

You can enable two kinds of caching with this plugin which are currently both ENABLED by default.
First, a download cache that will cache downloads that pip needs to compile the packages.
And second, a what we call "static caching" which caches output of pip after compiling everything for your requirements file.
Since generally requirements.txt files rarely change, you will often see large amounts of speed improvements when enabling the static cache feature.
These caches will be shared between all your projects if no custom cacheLocation is specified (see below).

_Please note: This has replaced the previously recommended usage of "--cache-dir" in the pipCmdExtraArgs_

`yaml
custom:
pythonRequirements:
useDownloadCache: true
useStaticCache: true
`

$3

There are two additional options related to caching.
You can specify where in your system that this plugin caches with the cacheLocation option.
By default it will figure out automatically where based on your username and your OS to store the cache via the appdirectory module.
Additionally, you can specify how many max static caches to store with staticCacheMaxVersions, as a simple attempt to limit disk space usage for caching.
This is DISABLED (set to 0) by default.
Example:

`yaml
custom:
pythonRequirements:
useStaticCache: true
useDownloadCache: true
cacheLocation: '/home/user/.my_cache_goes_here'
staticCacheMaxVersions: 10
`

$3

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

`yaml
custom:
pythonRequirements:
pipCmdExtraArgs:
- --compile
`

$3

You can specify extra arguments to be passed to docker build during the build step, and docker run during the dockerized pip install step:

`yaml
custom:
pythonRequirements:
dockerizePip: true
dockerBuildCmdExtraArgs: ['--build-arg', 'MY_GREAT_ARG=123']
dockerRunCmdExtraArgs: ['-v', '${env:PWD}:/my-app']
`

$3

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

`yaml
custom:
pythonRequirements:
fileName: requirements-prod.txt
`

$3

**Note: this feature does not work with Pipenv/Poetry, it requires requirements.txt
files for your Python modules.**

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:

`shell
├── serverless.yml
├── function1
│ ├── requirements.txt
│ └── index.py
└── function2
├── requirements.txt
└── index.py
`

With the content of your serverless.yml containing:

`yml
package:
individually: true

functions:
func1:
handler: index.handler
module: function1
func2:
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.

$3

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:

`yaml
custom:
pythonRequirements:
pythonBin: /opt/python3.6/bin/python
`

$3

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:

`yaml
custom:
pythonRequirements:
vendor: ./vendored-libraries
functions:
hello:
handler: hello.handler
vendor: ./hello-vendor # The option is also available at the function level
`

Manual invocation

The .requirements and requirements.zip (if using zip support) files are left
behind to speed things up on subsequent deploys. To clean them up, run:

`plaintext
sls requirements clean
`

You can also create them (and unzip_requirements if
using zip support) manually with:

`plaintext
sls requirements install
`

The pip download/static cache is outside the serverless folder, and should be manually cleaned when i.e. changing python versions:

`plaintext
sls requirements cleanCache
`

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.

`yaml
custom:
pythonRequirements:
invalidateCaches: true
`

:apple::beer::snake: 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:

- Install Python from python.org and specify it with the
pythonBin option.

OR

- Create a virtualenv and activate it while using serverless.

OR

- Install Docker and use the dockerizePip option.

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

:checkered_flag: 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
1. 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 with a Dockerfile like:

`dockerfile
FROM public.ecr.aws/sam/build-python3.9

Install your dependencies


RUN yum -y install mysql-devel
`

Then update your serverless.yml:

`yaml
custom:
pythonRequirements:
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 use the dockerExtraFiles option.

For instance, the mysqlclient package requires libmysqlclient.so.1020. If you use the Dockerfile from the previous section, add an item to the dockerExtraFiles option in your serverless.yml:

`yaml
custom:
pythonRequirements:
dockerExtraFiles:
- /usr/lib64/mysql57/libmysqlclient.so.1020
`

Then verify the library gets included in your package:

`bash
sls package
zipinfo .serverless/xxx.zip
`

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

Optimising packaging time

If you wish to exclude most of the files in your project, and only include the source files of your lambdas and their dependencies you may well use an approach like this:

`yaml
package:
individually: false
include:
- './src/lambda_one/**'
- './src/lambda_two/**'
exclude:
- '**'
`

This will be very slow. Serverless adds a default "**" include. If you are using the cacheLocation parameter to this plugin, this will result in all of the cached files' names being loaded and then subsequently discarded because of the exclude pattern. To avoid this happening you can add a negated include pattern, as is observed in .

Use this approach instead:

`yaml
package:
individually: false
include:
- '!./**'
- './src/lambda_one/**'
- './src/lambda_two/**'
exclude:
- '**'
`

Custom Provider Support

$3

This plugin is compatible with the Scaleway Serverless Framework Plugin to package dependencies for Python functions deployed on Scaleway. To use it, add the following to your serverless.yml:

`yaml
provider:
name: scaleway
runtime: python311

plugins:
- serverless-python-requirements
- serverless-scaleway-functions
`

To handle native dependencies, it's recommended to use the Docker builder with the image provided by Scaleway:

`yaml
custom:
pythonRequirements:
# Can use any Python version supported by Scaleway
dockerImage: rg.fr-par.scw.cloud/scwfunctionsruntimes-public/python-dep:3.11
`

Contributors

- @dschep - Original developer
- @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, current maintainer
- @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, update eslint,
- @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 cache support when using docker.
- @dee-me-tree-or-love - the slim package option
- @alexjurkiewicz - docs about docker workflows
- @andrewfarley - Implemented download caching and static caching
- @bweigel - adding the slimPatternsAppendDefaults option & fixing per-function packaging when some functions don't have requirements & Porting tests from bats to js!
- Poetry support
- @squaresurf
- @drice
- @ofercaspi
- @tpansino
- @david-mk-lawrence - added Lambda Layer support
- @bryantbriggs - Fixing CI/CD
- @jacksgt - Fixing pip issues
- @lephuongbg - Fixing single function deployment
- @rileypriddle - Introducing schema validation for module property
- @martinezpl - Fixing test issues, adding dockerPrivateKey` option