🧪 Example Hydra App

This repo hosts an example Hydra app.

Usage

In a Notebook

Create a model in a Notebook or another script by calling create_model. This is a custom function that reads the default configuration and returns an initialized class instance.

>>> from hydra_app import create_model
>>> 
>>> net = create_model()
>>> print(net)
NeuralNetwork(num_hidden=[200, 200], num_latent=20, dropout=0.2, lr=0.0001)

Override base configuration by supplying an additional config.

>>> from omegaconf import OmegaConf
>>> 
>>> experiment_conf = OmegaConf.load("experiment.yaml")
>>> 
>>> net = create_model(experiment_conf)
>>> print(net)
NeuralNetwork(num_hidden=[200, 200], num_latent=16, dropout=0.0, lr=0.1)

From the command line

Run:

>>> python -m hydra_app
NeuralNetwork(num_hidden=[200, 200], num_latent=20, dropout=0.2, lr=0.0001)

The above command uses the default configuration. To override it, you can check the default options using --help:

>>> python -m hydra_app --help

For example, load the "other" model which is specified in conf/model/other.yaml.

>>> python -m hydra_app model=other
NeuralNetwork(num_hidden=[100, 100], num_latent=10, dropout=0.1, lr=0.0003)

Reading additional config files

You can also read an additional file that overrides the default configuration.

The app will automatically create a config folder in the current working directory. Create an experiment sub-folder and place inside a config file (such as test.yaml). See below the directory structure:

.
└── config/
    └── experiment/
        └── test.yaml

Then, from the command line, call the app specifying the name of the config file (without extension):

>>> python -m hydra_app +experiment=test
NeuralNetwork(num_hidden=[100, 100], num_latent=10, dropout=0.1, lr=0.1)

Note that you can extend the available config options by reproducing the configuration structure inside the config folder in your current working directory. For example, adding a model folder with some model YAMLs:

.
└── config/
    ├── model/
    │   ├── network1.yaml
    │   └── network2.yaml
    └── experiment/
        └── test.yaml

will make it possible to select those model configs:

>>> python -m hydra_app model=network2 --help

Multirun

If we want to run a job multiple times, we can run the application with -m (or --multirun):

>>> python -m hydra_app -m model=other,another
NeuralNetwork(num_hidden=[100, 100], num_latent=10, dropout=0.1, lr=0.0003)
NeuralNetwork(num_hidden=[100, 100, 20], num_latent=2, dropout=0.1, lr=0.5)

Alternatively, we can force the multirun mode on the Hydra config (see multirun.yaml).

>>> python -m hydra_app +experiment=multirun
NeuralNetwork(num_hidden=[200, 200], num_latent=20, dropout=0.2, lr=0.1)
NeuralNetwork(num_hidden=[200, 200], num_latent=20, dropout=0.2, lr=0.1)
NeuralNetwork(num_hidden=[100, 100], num_latent=10, dropout=0.1, lr=0.1)
NeuralNetwork(num_hidden=[100, 100], num_latent=10, dropout=0.1, lr=0.1)
NeuralNetwork(num_hidden=[100, 100, 20], num_latent=2, dropout=0.1, lr=0.1)
NeuralNetwork(num_hidden=[100, 100, 20], num_latent=2, dropout=0.1, lr=0.1)

Finally, we can access the job number and mode from the HydraConfig object. This can be useful to report the current job, or to make sure that a certain task runs on a specific mode.

from hydra.core.hydra_config import HydraConfig
from hydra.types import RunMode

@hydra.main(config_path="conf", config_name="main")
def main(config: AppConfig) -> None:
    hydra_config = HydraConfig.get()
    if hydra_config.mode != RunMode.MULTIRUN:
        raise ValueError("Must run on multirun mode.")
    print(f"Hydra job #{hydra_config.job.num + 1}")