Ray Tune
W&B integrates with Ray by offering two lightweight integrations.
One is the WandbLoggerCallback, which automatically logs metrics reported to Tune to the Wandb API. The other one is the @wandb_mixin decorator, which can be used with the function API. It automatically initializes the Wandb API with Tune’s training information. You can just use the Wandb API like you would normally do, e.g. using wandb.log() to log your training process.
WandbLoggerCallback
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
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 that should be excluded from the log.
log_config (bool) – Boolean indicating if the config parameter of the results dict should be logged. This makes sense if parameters will change during training, e.g. with PopulationBasedTraining. Defaults to False.
Example
from ray import tune, train
from ray.tune.logger import DEFAULT_LOGGERS
from ray.air.integrations.wandb import WandbLoggerCallback
def train_fc(config):
for i in range(10):
train.report({"mean_accuracy":(i + config['alpha']) / 10})
search_space = {
'alpha': tune.grid_search([0.1, 0.2, 0.3]),
'beta': tune.uniform(0.5, 1.0)
}
analysis = tune.run(
train_fc,
config=search_space,
callbacks=[WandbLoggerCallback(
project="<your-project>",
api_key="<your-name>",
log_config=True
)]
)
best_trial = analysis.get_best_trial("mean_accuracy", "max", "last")
wandb_mixin
ray.tune.integration.wandb.wandb_mixin(func)
This Ray Tune Trainable mixin helps initializing the Wandb API for use with the Trainable class or with @wandb_mixin for the function API.
For basic usage, just prepend your training function with the @wandb_mixin decorator:
from ray.tune.integration.wandb import wandb_mixin
@wandb_mixin
def train_fn(config):
wandb.log()
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 WandbTrainableMixin itself:
Parameters
api_key_file (str) – Path to file containing the Wandb API KEY.
api_key (str) – Wandb API Key. Alternative to setting api_key_file.
Wandb’s group, run_id and run_name are automatically selected by Tune, but can be overwritten by filling out the respective configuration values.
Please see here for all other valid configuration settings: https://docs.wandb.com/library/init
Example:
from ray import tune
from ray.tune.integration.wandb import wandb_mixin
@wandb_mixin
def train_fn(config):
for i in range(10):
loss = self.config["a"] + self.config["b"]
wandb.log({"loss": loss})
tune.report(loss=loss)
tune.run(
train_fn,
config={
# 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"},
},
)
Example Code
We've created a few examples for you to see how the integration works: