preface

When we write Python projects, especially deep learning projects, it can be confusing to configure a lot of hyperparameters, so we can write the configuration into a YML file and use the HB-config library to manage it easily.

To show respect: github.com/hb-research… . Look at the author of a sentence to summarize the library functions:

  • hb-config is utility for easy to configure your python project especially Deep Learning experiments.

The installation

The installation steps are simple and simply invoke the PIP command

$ pip install hb-config
Copy the code

use

Yml configuration file. If you don’t know how to write yML file, you can learn it in five minutes.

project: "hb-config"
example: true
people:
  name: "wangda"
  sex:  "male"
  
Copy the code

Using Jupyter, call Config from the library to pass the filename:

from hbconfig import Config
Config("myConfig/config.yml")
print(Config)
Copy the code

Results print:

Read config file name: myConfig/config
{
    "project": "hb-config",
    "example": true,
    "people": {
        "name": "wangda",
        "sex": "male"
    }
}
Copy the code

It is important to note that the PyYAML library version may be too high to report an error. Just drop it to 5.4.1.

The values

  1. Take the project value:

     print(Config.project)
Copy the code

    Results print:

     hb-config
Copy the code

  2. Take the example value:

     print(Config. example)
Copy the code

    Results print:

     True
Copy the code

  3. Take a person’s name:

     print(Config.people.name)
Copy the code

    Results print:

     wangda
Copy the code

  4. Take the object:

     print(Config.people)
Copy the code

    Results print:

     {
     "name": "wangda",
     "sex": "male",
     "get_tag": "people"
 }	
Copy the code