Porting from argparse to hydra

This is a reminder of what I looked up when converting Python's argparse settings to hydra. Finally, I write a convenient function that is unique to hydra.

Introduction

When writing a machine learning experiment script in python, I used argparse as a setting method for hyperparameters. However, as the experiment became more complicated, there was a desire to have a considerable number of lines and to structure the settings only with the file that describes the argparse settings. .. .. So, read ymym's "Recommendation of hyperparameter management-Let's manage hyperparameters with Hydra + MLflow-" and read "This It was! ”, So I decided to use it. Then, I investigated various things when migrating the experimental settings written in argparse to hydra, so I summarized them.

What is hydra?

"A framework for elegantly configuring complex applications" It seems to be "a framework for elegantly managing the settings of complex applications". Development is centered on facebook research.

Github/OfficialDocument

How to use hydra

For detailed settings, please refer to the above article by ymym and Official Tutorial. The first feature is that if you want to structurally describe the settings in a yaml file and overwrite the contents, you can overwrite them on the command line at runtime.

Various writing styles

Defining and loading options

--hydra describes the settings in a yaml file and gives it as a decorator to the function that executes it.

argparse

# main.py
def main():
    parser = parser.ArgumentParser(...)
    parser.add_argument('--hoge', type=int, default=1)
    cfg = parser.parse_args()
    print(cfg.hoge) # 1

hydra

# config.yaml
hoge: 1

# main.py
@hydra.main(config_path='config.yaml')
def main(cfg):
    print(cfg.hoge) # 1

Overwrite default

--Specify with = --No space between key and value

argparse

# shell
python main.py --hoge 2

hydra

# shell
python main.py hoge=1

nargs --It should be noted that the settings parsed from hydra are not just list because they are all described in omegaconf.

argparse

# main.py
parser.add_argument('--hoge', type=int, nargs=3, default=[1, 2, 3])

# shell
python main.py --hoge 4 5 6

hydra

# config.yaml
hoge:
  - 1
  - 2
  - 3

# shell
python main.py hoge=[1,2,3]

required=True --Specify ??? for value --If you do not specify it, you will get the error ʻomegaconf.errors.MissingMandatoryValueof OmegaConf. --However, this is not evaluated at runtime, but is evaluated when the key is accessed, and an error occurs. Therefore, even if you execute it without specifying it, the code up to the line that accesses the key by specifying it with???` will be executed. (Please let me know if there is a way to evaluate at runtime with the settings on the hydra side)

argparse

# main.py
parser.add_argument('--hoge', type=int, required=True)

hydra

# config.yaml
hoge: ???

choices --There seems to be no function to specify a list of allowed values for a specific key so far. (I think it is doubtful whether functions will be added in the future.) --Similar functions can be achieved by structuring the config. --For details, it is better to refer to Here and there.

argparse

# main.py
parser.add_argument('--hoge', type=int, default=1, choices=[1, 2])
print(cfg.hoge)

# shell
python main.py --hoge 2
# 2

hydra

├── config
|   ├── config.yaml
│   └── choice
│       ├── a.yaml
│       └── b.yaml
└── main.py

# config.yaml
choice: a

# choice/a.yaml
hoge: 1

# choice/b.yaml
hoge: 2

# main.py
print(cfg.hoge)

# shell
python main.py choice=b
# 2

--help

argparse

# shell
python main.py --help

hydra

# shell
python main.py --cfg job

Convenient function only in hydra

Since it will be almost an introduction to the tutorial, embed a link to the target tutorial in the section name. Please refer to that for details.

Multi-run Try to execute at the same time with multiple settings with the config set in the choices above.

# shell
python main.py hoge=a,b

Tab Completion (https://hydra.cc/docs/tutorial/tab_completion)

If you execute the following command once before executing the script, you can use tab completion when specifying options from the command line.

eval "$(python main.py -sc install=bash)"

Create Log Directory

hydra automatically creates a structured log directory based on the date and time without any settings, and uses that as the working directory at runtime, and the files generated at runtime and saved. Save files etc.

import os

@hydra.main()
def main(_cfg):
    print("Working directory : {}".format(os.getcwd()))

$ python main.py
Working directory : /home/omry/dev/hydra/outputs/2019-09-25/15-16-17

$ python main.py
Working directory : /home/omry/dev/hydra/outputs/2019-09-25/15-16-19

However, do not specify the working directory without permission! I think there are people who say that (myself), so let's do specify the directory for hydra yourself.

Summary

I have just started using it, so if there are any mistakes in the article or if you know a better way, I would appreciate it if you could teach me. If you use hydra, the code will be refreshed, so please try it! (Especially for large code)

Finally, if you want to know more about how to use it, please contact Official document and ymym's blog. Please refer to / 09 034644).