lightray 0.1.0

Distribute a LightningCLI hyperparameter search with Ray Tune

lightray

Easily integrate a LightningCLI with RayTune hyperparameter optimization

Getting Started

Extend your custom LightningCLI to a Trainable compatible with RayTune.

Imagine you have a LightningCLI parser for a

from lightning.pytorch as pl

class CustomCLI(pl.cli.LightningCLI):
    def add_arguments_to_parser(self, parser):
        parser.link_arguments(
            "data.init_args.parameter", 
            "model.init_args.parameter", 
            apply_on="parse"
        )

class DataModule(pl.LightningDataModule):
    def __init__(self, hidden_dim: int, learning_rate: float, parameter: int):
        self.parameter = parameter
        self.hidden_dim = hidden_dim
        self.learning_rate = learning_rate
    
    def train_dataloader(self):
        ...

class DataModule(pl.LightningModule):
    def __init__(self, parameter: int):
        self.parameter = parameter
    
    def training_step(self):
        ...

Launching a hyperparameter tuning job with RayTune using this LightningCLI is as simple as

from ray import tune
from raylightning import run

# define search space
search_space = {
    "model.init_args.hidden_dim": tune.choice([32, 64])
    "model.init_args.learning_rate": tune.loguniform(1e-4, 1e-2)
}

# define scheduler
scheduler = ASHAScheduler(max_t=50, grace_period=1, reduction_factor=2)

# pass command line style arguments as if you were
# running your `LightningCLI` from the command line
args = ["--config", "/path/to/config.yaml"]
results = run(
    args=args
    cli_cls=simple_cli,
    name="tune-test",
    metric="val_loss",
    objective="min",
    search_space=search_space,
    scheduler=scheduler,
    storage_dir=storage_dir,
    address=None,
    num_samples=num_samples,
    workers_per_trial=1,
    gpus_per_worker=1.0,
    cpus_per_gpu=4.0,
    temp_dir=None,
)

s3 storage works out of the box. Make sure you have set the AWS_ENDPOINT_URL, AWS_ACCESS_KEY_ID, and AWS_SECRET_ACCESS_KEY environment variables set. Then, simply pass an s3 path (e.g. s3://{bucket}/{folder} to the storage_dir argument.