A Serverless v1.x plugin to automatically bundle dependencies from
requirements.txt and make them available in your
Requires Serverless >= v1.12
sls plugin install -n serverless-python-requirements
Compiling non-pure-Python modules or fetching their manylinux wheels is
supported on non-linux OSs via the use of Docker and the
To enable docker usage, add the following to your
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
custom:pythonRequirements: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
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
custom:pythonRequirements:dockerizePip: truedockerSsh: true
dockerSsh option will mount your
$HOME/.ssh/known_hosts as a
volume in the docker container. If your SSH key is password protected, you can use
$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
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
dockerizePip. If you don't want this plugin to generate it for you, set the following option:
To help deal with potentially large dependencies (for example:
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
and add this to your handler module before any code that imports your deps:
try:import unzip_requirementsexcept ImportError:pass
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:
You can specify extra arguments to be passed to pip like this:
custom:pythonRequirements:dockerizePip: truepipCmdExtraArgs:- --cache-dir- .requirements-cache
pip workflows involve using requirements files not named
To support these, this plugin has the following option:
Sometimes your Python executable isn't available on your
python3.6 (for example, windows or using pyenv).
To support this, this plugin has the following option:
requirements.zip(if 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
using zip support) manually with
sls requirements install.
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.
custom: pythonRequirements: invalidateCaches: true
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:
Also, brew seems to cause issues with pipenv, so make sure you install pipenv using pip.
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.
dockerFileoption to build a custom docker image.