Eliminate config boilerplate (Advanced)¶

Audience: Users looking to modularize their code for a professional project.

Pre-reqs: You must have read (Control it all from the CLI).

What is a yaml config file?¶

A yaml is a standard configuration file that describes parameters for sections of a program. It is a common tool in engineering, and it has recently started to gain popularity in machine learning.

# file.yaml
car:
    max_speed:100
    max_passengers:2
plane:
    fuel_capacity: 50
class_3:
    option_1: 'x'
    option_2: 'y'

Print the config used¶

Before or after you run a training routine, you can print the full training spec in yaml format using --print_config:

python main.py fit --print_config

which generates the following config:

seed_everything: null
trainer:
    logger: true
    ...
    terminate_on_nan: null
model:
    out_dim: 10
    learning_rate: 0.02
data:
    data_dir: ./
ckpt_path: null

Write a config yaml from the CLI¶

To have a copy of the configuration that produced this model, save a yaml file from the –print_config outputs:

python main.py fit --model.learning_rate 0.001 --print_config > config.yaml

Run from a single yaml¶

To run from a yaml, pass a yaml produced with --print_config to the --config argument:

python main.py fit --config config.yaml

when using a yaml to run, you can still pass in inline arguments

python main.py fit --config config.yaml --trainer.max_epochs 100

Compose yaml files¶

For production or complex research projects it’s advisable to have each object in its own config file. To compose all the configs, pass them all inline:

$ python trainer.py fit --config trainer.yaml --config datamodules.yaml --config models.yaml ...

The configs will be parsed sequentially. Let’s say we have two configs with the same args:

# trainer.yaml
trainer:
    num_epochs: 10


# trainer_2.yaml
trainer:
    num_epochs: 20

the ones from the last config will be used (num_epochs = 20) in this case:

$ python trainer.py fit --config trainer.yaml --config trainer_2.yaml