Programmatic Hyperparameter Tuning for TensorFlow Object Detection API

Question

I am using the TF Object Detection API. I have a custom data set. I am training using SLURM jobs and calling the API scripts from within there. I am looking to try and tune hyperparameters found in the pipeline.config files. Unfortunately, in the documentation, this kind of process is not outlined. It seems like the process is to either use the sample configs or tune the hyperparameters by hand.

Tuning by hand is somewhat feasible, for example adjusting for two parameters for three values (batch size and steps) results in nine different .configs, but adding another hyperparameter to that boosts it up to twenty-seven files I need to keep track of. This does not seem like a good way to do it, particularly because it limits the values I can try and is clumsy.

It seems like there are libraries out there that hook into Keras and other more high-level frameworks, but I have found nothing that looks like it can take the results of the Object Detection API and actually optimize it.

Is it possible to do this with a pre-built library I don't know about? I would like to avoid having to edit the API implementation or coding this myself to minimize errors.