A Serverless v1.x plugin to automatically bundle dependencies from
requirements.txt and make them available in your
Requires Serverless >= v1.12
npm install --save serverless-python-requirements
Add the plugin to your
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
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.
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:
If you want to be able to use
sls invoke local and don't have a check for
lambda or a
except ImportError around that import, you can use the
following option to make this plugin not delete the
You can omit a package from deployment with the
noDeploy option. Note that
dependencies of omitted packages must explicitly be omitted too.
For example, this will not install the AWS SDKs that are already installed on
custom:pythonRequirements:noDeploy:- boto3- botocore- docutils- jmespath- python-dateutil- s3transfer- six
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
This requires an update to your serverless.yml:
provider: name: aws runtime: python3.6
And be sure to clean up
requirements.zip if they exist as
python2.7 and python3.6 code can't coexist.