Specify dependencies in Python

There are two ways to specify dependencies for Cloud Run 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 shouldn't include these files.

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

Specify 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 Run 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

Package local dependencies

You can also package and deploy dependencies alongside your function. This approach is useful if your dependency is not available using the pip package manager or if your Cloud Run 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 Run functions.

Copied dependencies

Copied dependencies are dependencies whose source is included directly in your source code package and rebuilt alongside your own code. Use the GOOGLE_VENDOR_PIP_DEPENDENCIES build environment variable to create copied pip dependencies and avoid installing them during deployment.

Create copied dependencies

Ensure that python3 is installed on your development system.
Declare your application dependencies in a requirements.txt file in the root directory of your development tree.
Declare Functions Framework as a requirement by including functions-framework on a separate line in your requirements.txt file.
Download your function's dependencies to your local directory. The steps to do this depend on whether the dependency is a Python wheel (*.whl) file or a tar file (*.tar.gz).
1. If the dependency is a Python wheel (*.whl), download it into the root directory of your development tree with this pip command:
```
python3 -m pip download -r requirements.txt --only-binary=:all: \
   -d DIRECTORY \
   --python-version PYTHON_RUNTIME_VERSION \
   --platform manylinux2014_x86_64 \
   --implementation cp
```
  Replace:
  - DIRECTORY: the name of the local directory to download to.
  - PYTHON_RUNTIME_VERSION: the Python version to use for compatibility checks. For example 311 for Python 3.11.
    This version must match one of the supported Python runtimes.
  The resulting directory structure should look like this:
```
myfunction/
├── main.py
└── requirements.txt
└── DIRECTORY
   ├── dependency1.whl
   └── dependency2.whl
```
2. If the dependency is a tar file (*.tar.gz):
  1. If the dependency is written in Python, use pip to download it:
```
python3 -m pip download -r requirements.txt \
   -d DIRECTORY
```
  2. If a dependency consists of code written in C or C++, you must download and compile it separately.
Deploy your function and its copied dependencies:
```
gcloud functions deploy FUNCTION_NAME \
  --runtime PYTHON_RUNTIME_NAME \
  --set-build-env-vars GOOGLE_VENDOR_PIP_DEPENDENCIES=DIRECTORY
```
Replace:
- FUNCTION_NAME: the name of the Cloud Run functions function you're deploying.
- PYTHON_RUNTIME_NAME: the name of one of the supported Python runtimes to run your deployed function under - for example python311. This must be the same Python runtime version as you've used in your local development environment.
- DIRECTORY: the name of the directory containing your copied dependencies.

For more details about using buildpacks, see Build a function with buildpacks.

Use private dependencies

You can use private dependencies from Artifact Registry or from other repositories.

Private dependencies from Artifact Registry

An Artifact Registry Python repository can host private dependencies for your Python function. When deploying to Cloud Run, 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:

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

If your build needs multiple repositories, use an Artifact Registry virtual repository to safely control the order that pip searches your repositories.

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 copied 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:

Copy your dependency into a local directory:
```
pip install -t DIRECTORY DEPENDENCY
```
Add an empty __init__.py file to the DIRECTORY directory to turn it into a module.
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 3.8 and later

click==8.1.7
cloudevents==1.11.0
deprecation==2.1.0
Flask==2.2.5
functions-framework==3.8.1
gunicorn==23.0.0
importlib-metadata==6.7.0
itsdangerous==2.1.2
jinja2==3.1.4
MarkupSafe==2.1.5
packaging==24.0
typing_extensions==4.7.1
watchdog==3.0.0
Werkzeug==2.2.3
zipp==3.15.0

Python 3.7

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

* `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.