Specify dependencies in Python

There are two ways to specify dependencies for Cloud Functions written in Python: using the pip package manager's requirements.txt file or packaging local dependencies alongside your function.

Dependency specification using the Pipfile/Pipfile.lock standard is not supported. Your project should not include these files.

Specifying dependencies with pip

Dependencies in Python are managed with pip and expressed in a metadata file called requirements.txt. This file must be in the same directory as the main.py file that contains your function code.

When you deploy or redeploy your function, Cloud Functions uses pip to download and install the latest version of your dependencies as declared in the requirements.txt file. The requirements.txt file contains one line per package. Each line contains the package name, and optionally, the requested version. For more details, see the requirements.txt reference.

To prevent your build from being affected by dependency version changes, consider pinning your dependency packages to a specific version.

The following is an example requirements.txt file:

functions-framework
requests==2.20.0
numpy

The Functions Framework is a required dependency for all functions. Although Cloud Functions installs it on your behalf when the function is created, we recommend that you include it as an explicit dependency for clarity.

If your function relies on private dependencies, we recommend that you mirror functions-framework to your private registry. Include the mirrored functions-framework as a dependency to your function to avoid installing the package from the public internet.

Packaging local dependencies

You can also package and deploy dependencies alongside your function. This approach is useful if your dependency is not available via the pip package manager or if your Cloud Functions environment's internet access is restricted.

For example, you might use a directory structure such as the following:

myfunction/
├── main.py
└── localpackage/
    ├── __init__.py
    └── script.py

You can then import the code as usual from localpackage using the following import statement.

# Code in main.py
from localpackage import script

Note that this approach will not run any setup.py files. Packages with those files can still be bundled, but may not run correctly on Cloud Functions.

Using private dependencies

Private dependencies from Artifact Registry

An Artifact Registry Python repository can host private dependencies for your Python function. When deploying to Cloud Functions, the build process will automatically generate Artifact Registry credentials for the Cloud Build service account. You only need to include the Artifact Registry URL in your requirements.txt without generating additional credentials. For example:

--extra-index-url REPOSITORY_URL
sampleapp
Flask==0.10.1
google-cloud-storage

Private dependencies from other repositories

Dependencies are installed in a Cloud Build environment that does not provide access to SSH keys. Packages hosted in repositories that require SSH-based authentication must be vendored and uploaded alongside your project's code, as described in the previous section.

You can use the pip install command with the -t DIRECTORY flag to copy private dependencies into a local directory before deploying your app, as follows:

  1. Copy your dependency into a local directory:

    pip install -t DIRECTORY DEPENDENCY

  2. Add an empty __init__.py file to the DIRECTORY directory to turn it into a module.

  3. Import from this module to use your dependency:

    import DIRECTORY.DEPENDENCY

Pre-installed packages

The following Python packages are automatically installed alongside your function during deployment. If you are using any of these packages in your function code, we recommend that you include the following versions in your requirements.txt file:

Python 3.7

cmd/python/functions_framework_compat/converter/requirements.txt 
aiohttp==3.8.1
aiosignal==1.2.0
async-timeout==4.0.2
attrs==21.4.0
cachetools==4.2.4
certifi==2021.10.8
chardet==4.0.0
charset-normalizer==2.0.10
click==8.0.3
Flask==2.0.2
frozenlist==1.2.0
google-api-core==2.3.2
google-api-python-client==2.34.0
google-auth==2.3.3
google-auth-httplib2==0.1.0
google-cloud-core==2.2.1
google-cloud-trace==1.5.1
googleapis-common-protos==1.54.0
grpcio==1.43.0
grpcio-status==1.43.0
httplib2==0.20.2
idna==3.3
itsdangerous==2.0.1
Jinja2==3.0.3
MarkupSafe==2.0.1
multidict==5.2.0
opencensus==0.8.0
opencensus-context==0.1.2
packaging==21.3
proto-plus==1.19.8
protobuf==3.19.1
pyasn1==0.4.8
pyasn1-modules==0.2.8
pyparsing==3.0.6
pytz==2021.3
PyYAML==6.0
requests==2.27.1
rsa==4.8
setuptools==60.3.1
six==1.16.0
uritemplate==4.1.1
urllib3==1.26.7
Werkzeug==2.0.2
wrapt==1.13.3
yarl==1.7.2

Python 3.8 and later

cmd/python/functions_framework/converter/requirements.txt 
click==8.1.7
cloudevents==1.9.0
deprecation==2.1.0
Flask==2.2.5
functions-framework==3.0.0
gunicorn==20.1.0
importlib-metadata==6.7.0
itsdangerous==2.1.2
Jinja2==3.1.2
MarkupSafe==2.1.3
packaging==23.1
typing_extensions==4.7.1
watchdog==1.0.2
Werkzeug==2.2.3
zipp==3.15.0



* `pip` (latest version)
* `setuptools` (latest version)
* `wheel` (determined by product requirements)

In addition, the Python runtime includes a number of system packages in the execution environment. If your function uses a dependency that requires a system package that is not listed, you can request a package.