When optimizing parameters of learning algorithms (hyperparameters), one has to try different hyperparameter configurations on different datasets with a given learning algorithm (and learning task, eg. classification) in order to explore the optimization space. Because this is expensive, this project will allow to interpolate between already-computed performance values for the learning algorithm. By searching the OpenML database for the given learning algorithm and the closest hyperparameters, we can approximate the performance of the algorithm.
In order to start the docker image, you have to clone this repo and then build it, as you would normally.
See docker/rebuild-omlbotlookup.sh and docker/run-omlbotlookup.sh for examples.
docker/mysqldata should contain the actual database as an .sql file, which the API looks into. docker/mysqldata/README gives instructions on how to obtain and pre-process the database.sql file to minimize startup time of the container.
Internally the container exposes the port 8000 but to not collide with other ports we map it to 8746 on the host in the examplary run-omlbotlookup.sh file.
An example can be found in example/access_api.R
This project is MIT licensed, see the LICENSE file.