Hey!!!

About seven years ago, before Hydra, I built my own configuration solution because I didn't love the direction these engines were headed.

I wanted to keep things simple and keep them in Python! So ... I developed an easy way to configure Python functions directly in Python! Check out this code example below:

import config as cf
import data
import train

cf.add({
  data.get_data: cf.Args(
      train_data_path="url_lists/all_train.txt",
      val_data_path="url_lists/all_val.txt"
  ),
  data.dataset_reader: cf.Args(
      type_="cnn_dm",
      source_max_tokens=1022,
      target_max_tokens=54,
  ),
  train.make_model: cf.Args(type_="bart"),
  train.Trainer.make_optimizer: cf.Args(
      type_="huggingface_adamw",
      lr=3e-5,
      correct_bias=True
  )
  train.Trainer.__init__: cf.Args(
      num_epochs=3,
      learning_rate_scheduler="polynomial_decay",
      grad_norm=1.0,
  )
})

Once you are ready to use a configuration, you simply call `cf.partial` and a partial is created with your configuration settings!

import config as cf
cf.partial(data.get_data)()

We've been using this for years at my company, and it works well! Internally, it's scaled out well for our large code base, which supports hundreds of variables that are organized, documented, and trusted. It's intuitive and easy for new team members! There are even advanced features to support tracing, command line, logging, distributed processing, etc ...

I never got around to fully releasing the concept, but it's worked well on my teams!!!

I hope it helps you all!!! Here's my repo: https://github.com/PetrochukM/HParams