yamlpyconfig

Library for parsing yaml config files in ML training pipelines. Based on Artur Kuzin talk "DL Pipelines tips & tricks".

Benefits:

1. Config files have yaml format.
2. All parameters in python code have form 'cfg.par_name'.
3. Any parameter could be set in command line prompt.

There are 3 levels of configuration:

1. Default config file is set during initialization:

   • yconf = YamlPyConfig('default_config', , conf_path='./configs')
   • Config file 'default_config.yaml' should be placed in './configs' folder.
   • python train_example.py

2. Custom config file could be set with parameter --config:

   • python train_example.py --config custom_config

3. Custom parameters could be set with appropriate keys:

   • python train_example.py --config custom_config --epochs 100 --lr 0.3

Installing Dependencies :

pip install -r requirements.txt