This repository contains the boilerplate code needed to create a containerized evaluation function written in Lean 4 Language.
This chapter helps you to quickly creating a new Lean 4 evaluation function using this template repository.
-
In GitHub, choose
Use this template
>Create a new repository
in the repository toolbar. -
Choose the owner, and pick a name for the new repository.
If you want to deploy the evaluation function to Lambda Feedback, make sure to choose the Lambda Feedback organization as the owner.
-
Set the visibility to
Public
orPrivate
.If you want to use GitHub deployment protection rules, make sure to set the visibility to
Public
. -
Click on
Create repository
.
Clone the new repository to your local machine using the following command:
git clone <repository-url>
When deploying to Lambda Feedback, set the evaluation function name in the config.json
file. Read the Deploy to Lambda Feedback section for more information.
You're ready to start developing your evaluation function. Head over to the Development section to learn more.
You can run the evaluation function either using the pre-built Docker image or build and run the binary executable.
The pre-built Docker image comes with shimmy
installed.
Shimmy is a small application that listens for incoming HTTP requests, validates the incoming data and forwards it to the underlying evaluation function. Learn more about shimmy in the Documentation.
The pre-built Docker image is available on the GitHub Container Registry. You can run the image using the following command:
docker run -p 8080:8080 ghcr.io/lambda-feedback/my-lean-evaluation-function:latest
To run the evaluation function, you need to build the binary first. See the Building the Evaluation Function section for more information.
After the function has been built, you can choose between running the function itself, ore using shimmy to run the function.
Raw Mode
Use the following command to run the evaluation function directly:
.lake/build/bin/evaluation request.json response.json
This will run the evaluation function using the input data from request.json
and write the output to response.json
.
Shimmy
To have a more user-friendly experience, you can use shimmy
to run the evaluation function.
Shimmy is a small application that listens for incoming HTTP requests, validates the incoming data and forwards it to the underlying evaluation function. Learn more about shimmy in the Documentation.
To run the evaluation function using shimmy
, use the following command:
shimmy -c ".lake/build/bin/evaluation" -i file
This chapter will guide you through the development process of the evaluation function.
In order to develop, test and build the evaluation function, you need to have the following tools installed:
The lean toolchain will automatically install the right version of Lean 4 for you as soon as you're working on the evaluation function. You can find the toolchain version in the
lean-toolchain
file.
.github/workflows/
build.yml # builds the public evaluation function image
deploy.yml # deploys the evaluation function to Lambda Feedback
src/ # evaluation function source code
Evaluation.lean # module containing the evaluation function
Main.lean # module containing the `main` function used to build the executable
tests/ # testing code
Main.lean # module containing tests and `main` function run by `lake test`
util/ # utility source code
Testing.lean # module containing utility for running tests
config.json # evaluation function deployment configuration file
lakefile.lean # Lake build configuration
lean-toolchain # Lean toolchain version configuration
To build the evaluation function, run the following command:
lake build
This will build a binary executable using the default of the evaluation function, which is configured in lakefile.lean
.
The executable can be found in .lake/build/bin/
.
To run the tests, run the following command:
lake test
This will use the lake test executor to run the module declared as test runner in lakefile.lean
. You can find the test module in tests/Main.lean
.
To build the Docker image, run the following command:
docker build -t my-lean-evaluation-function .
The Dockerfile accepts the following build arguments:
LEAN_VERSION
: (optional) the version of Lean 4 to use.
This section guides you through the deployment process of the evaluation function. If you want to deploy the evaluation function to Lambda Feedback, follow the steps in the Lambda Feedback section. Otherwise, you can deploy the evaluation function to other platforms using the Other Platforms section.
Deploying the evaluation function to Lambda Feedback is simple and straightforward, as long as the repository is within the Lambda Feedback organization.
After configuring the repository, a GitHub Actions workflow will automatically build and deploy the evaluation function to Lambda Feedback as soon as changes are pushed to the main branch of the repository.
Configuration
The deployment configuration is stored in the config.json
file. Choose a unique name for the evaluation function and set the EvaluationFunctionName
field in config.json
.
The evaluation function name must be unique within the Lambda Feedback organization, and must be in
lowerCamelCase
. You can find a example configuration below:
{
"EvaluationFunctionName": "compareStringsWithLean"
}
If you want to deploy the evaluation function to other platforms, you can use the Docker image to deploy the evaluation function.
Please refer to the deployment documentation of the platform you want to deploy the evaluation function to.
If you need help with the deployment, feel free to reach out to the Lambda Feedback team by creating an issue in the template repository.
If you want to pull changes from the template repository to your repository, follow these steps:
- Add the template repository as a remote:
git remote add template https://github.com/lambda-feedback/evaluation-function-boilerplate-lean.git
- Fetch changes from all remotes:
git fetch --all
- Merge changes from the template repository:
git merge template/main --allow-unrelated-histories
Make sure to resolve any conflicts and keep the changes you want to keep.