Ray Tune

W&B integrates with Ray by offering two lightweight integrations.

TheWandbLoggerCallback function automatically logs metrics reported to Tune to the Wandb API.
The setup_wandb() function, which can be used with the function API, automatically initializes the Wandb API with Tune’s training information. You can use the Wandb API as usual. such as by using run.log() to log your training process.

Configure the integration

from ray.air.integrations.wandb import WandbLoggerCallback

Wandb configuration is done by passing a wandb key to the config parameter of tune.run() (see example below). The content of the wandb config entry is passed to wandb.init() as keyword arguments. The exception are the following settings, which are used to configure the WandbLoggerCallback itself:

Parameters

project (str): Name of the Wandb project. Mandatory. api_key_file (str): Path to file containing the Wandb API KEY. api_key (str): Wandb API Key. Alternative to setting api_key_file. excludes (list): List of metrics to exclude from the log. log_config (bool): Whether to log the config parameter of the results dictionary. Defaults to False. upload_checkpoints (bool): If True, model checkpoints are uploaded as artifacts. Defaults to False.

Example

from ray import tune, train
from ray.air.integrations.wandb import WandbLoggerCallback


def train_fc(config):
    for i in range(10):
        train.report({"mean_accuracy": (i + config["alpha"]) / 10})


tuner = tune.Tuner(
    train_fc,
    param_space={
        "alpha": tune.grid_search([0.1, 0.2, 0.3]),
        "beta": tune.uniform(0.5, 1.0),
    },
    run_config=train.RunConfig(
        callbacks=[
            WandbLoggerCallback(
                project="<your-project>", api_key="<your-api-key>", log_config=True
            )
        ]
    ),
)

results = tuner.fit()

setup_wandb

from ray.air.integrations.wandb import setup_wandb

This utility function helps initialize Wandb for use with Ray Tune. For basic usage, call setup_wandb() in your training function:

from ray import tune
from ray.air.integrations.wandb import setup_wandb


def train_fn(config):
    # Initialize wandb
    wandb = setup_wandb(config)
    run = wandb.init(
        project=config["wandb"]["project"],
        api_key_file=config["wandb"]["api_key_file"],
    )

    for i in range(10):
        loss = config["a"] + config["b"]
        run.log({"loss": loss})
        tune.report(loss=loss)
    run.finish()


tuner = tune.Tuner(
    train_fn,
    param_space={
        # define search space here
        "a": tune.choice([1, 2, 3]),
        "b": tune.choice([4, 5, 6]),
        # wandb configuration
        "wandb": {"project": "Optimization_Project", "api_key_file": "/path/to/file"},
    },
)
results = tuner.fit()

Example code

We’ve created a few examples for you to see how the integration works:

Colab: A simple demo to try the integration.
Dashboard: View dashboard generated from the example.

Guides

Integrations

Reference

Configure the integration

Parameters

Example

setup_wandb

Example code

​Configure the integration

​Parameters

​Example

​setup_wandb

​Example code

Configure the integration

Parameters

Example

setup_wandb

Example code