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AWS Lambda

pydantic integrates well with AWS Lambda functions. In this guide, we'll discuss how to setup pydantic for an AWS Lambda function.

Installing Python libraries for AWS Lambda functions

There are many ways to utilize Python libraries in AWS Lambda functions. As outlined in the AWS Lambda documentation, the most common approaches include:

All of these approaches can be used with pydantic. The best approach for you will depend on your specific requirements and constraints. We'll cover the first two cases more in-depth here, as dependency management with a container image is more straightforward. If you're using a container image, you might find this comment helpful for installing pydantic.


If you use pydantic across multiple functions, you may want to consider AWS Lambda Layers, which support seamless sharing of libraries across multiple functions.

Regardless of the dependencies management approach you choose, it's beneficial to adhere to these guidelines to ensure a smooth dependency management process.

Installing pydantic for AWS Lambda functions

When you're building your .zip file archive with your code and dependencies or organizing your .zip file for a Lambda Layer, you'll likely use a local virtual environment to install and manage your dependencies. This can be a bit tricky if you're using pip because pip installs wheels compiled for your local platform, which may not be compatible with the Lambda environment.

Thus, we suggest you use a command similar to the following:

pip install \
    --platform manylinux2014_x86_64 \  # (1)!
    --target=<your_package_dir> \  # (2)!
    --implementation cp \  # (3)!
    --python-version 3.10 \  # (4)!
    --only-binary=:all: \  # (5)!
    --upgrade pydantic  # (6)!
  1. Use the platform corresponding to your Lambda runtime.
  2. Specify the directory where you want to install the package (often python for Lambda Layers).
  3. Use the CPython implementation.
  4. The Python version must be compatible with the Lambda runtime.
  5. This flag ensures that the package is installed pre-built binary wheels.
  6. The latest version of pydantic will be installed.


no module named 'pydantic_core._pydantic_core'


no module named `pydantic_core._pydantic_core`

error is a common issue that indicates you have installed pydantic incorrectly. To debug this issue, you can try the following steps (before the failing import):

  1. Check the contents of the installed pydantic-core package. Are the compiled library and its type stubs both present?
from importlib.metadata import files
print([file for file in files('pydantic-core') if'_pydantic_core')])
[PackagePath('pydantic_core/_pydantic_core.pyi'), PackagePath('pydantic_core/')]

You should expect to see two files like those printed above. The compile library file will be a .so or .pyd with a name that varies according to the OS and Python version.

  1. Check that your lambda's Python version is compatible with the compiled library version found above.
import sysconfig
#> ''

You should expect to see the same suffix here as the compiled library, for example here we see this suffix indeed matches

If these two checks do not match, your build steps have not installed the correct native code for your lambda's target platform. You should adjust your build steps to change the version of the installed library which gets installed.

Most likely errors:

  • Your OS or CPU architecture is mismatched (e.g. darwin vs x86_64-linux-gnu). Try passing correct --platform argument to pip install when installing your lambda dependencies, or build inside a linux docker container for the correct platform. Possible platforms at the moment include --platform manylinux2014_x86_64 or --platform manylinux2014_aarch64, but these may change with a future Pydantic major release.

  • Your Python version is mismatched (e.g. cpython-310 vs cpython-312). Try passing correct --python-version argument to pip install, or otherwise change the Python version used on your build.

No package metadata was found for email-validator

Pydantic uses version from importlib.metadata to check what version of email-validator is installed. This package versioning mechanism is somewhat incompatible with AWS Lambda, even though it's the industry standard for versioning packages in Python. There are a few ways to fix this issue:

If you're deploying your lambda with the serverless framework, it's likely that the appropriate metadata for the email-validator package is not being included in your deployment package. Tools like serverless-python-requirements remove metadata to reduce package size. You can fix this issue by setting the slim setting to false in your serverless.yml file:

    dockerizePip: non-linux
    slim: false
    fileName: requirements.txt

You can read more about this fix, and other slim settings that might be relevant here.

If you're using a .zip archive for your code and/or dependencies, make sure that your package contains the required version metadata. To do this, make sure you include the dist-info directory in your .zip archive for the email-validator package.

This issue has been reported for other popular python libraries like jsonschema, so you can read more about the issue and potential fixes there as well.

Extra Resources

More Debugging Tips

If you're still struggling with installing pydantic for your AWS Lambda, you might consult with this issue, which covers a variety of problems and solutions encountered by other developers.

Validating event and context data

Check out our blog post to learn more about how to use pydantic to validate event and context data in AWS Lambda functions.