Active Learning API

Simple API that hosts code for running bayesian optimization. Currently only 1D bayesian optimization has been implemented but the plan is to expand to 2D.

Documentation

We use FastAPI. The documentation is automatically generated from the code. The documentation can be viewed at /docs once the service is up and running, for example http://localhost/docs

There is current one endpoint run_one_1d_bayesian_optimization that runs a bayesian optimization in one dimension and returns distribution and the next suggestion.

Examples

Please see examples/example_1d.py which a very small example using the code and plotting it

Setup and Run

This can be done it two different ways: With Python or with Docker.

Python

Create a virtual env

python3 -m venv ./venv
source venv/bin/activate

Install Requirements and Run

pip install -r requirements.txt
cd src && uvicorn main:app --reload

Local pytest

To run local pytests you need to

1. add src to python path export PYTHONPATH=$PYTHONPATH:./src
2. run pytests: pytest

Docker

1. Make sure docker is installed on your system.
2. Use docker-compose up in the main directory to start up the application.
3. You will now be able to access it on http://localhost:80

You can use docker to start up the image with

1. docker build -t active_learning:latest .
2. docker run -p 80:80 -it active_learning:latest