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Optuna
DVCLive allows you to add experiment tracking capabilities to your Optuna projects.
Usage
Include the
DVCLiveCallback
in the callbacks list passed to study.optimize:
from dvclive.optuna import DVCLiveCallback
...
study.optimize(
objective, n_trials=7, callbacks=[DVCLiveCallback()])If you are using an ML Framework inside the objective function, you can
instead use the corresponding DVCLive integration for the ML Framework. See the
example bellow.
Each trial will create a DVC Experiment, tracking the associated
metrics and parameters.
Parameters
-
metric_name- (metricby default) - Name assigned to the metric to be optimized. -
**kwargs- Any additional arguments will be used to instantiate a newLiveinstance.
Examples
Optuna callback
import optuna
from dvclive.optuna import DVCLiveCallback
def objective(trial):
x = trial.suggest_float("x", -10, 10)
return (x - 2) ** 2
study = optuna.create_study()
study.optimize(
objective, n_trials=7, callbacks=[DVCLiveCallback()])Optuna with ML Framework
In the
Optuna and Keras example
you can use the dvclive.keras callback:
from dvclive import Live
from dvclive.keras import DVCLiveCallback
...
with Live() as live:
live.log_params(trial.params)
model.fit(
x_train,
y_train,
validation_data=(x_valid, y_valid),
shuffle=True,
batch_size=BATCHSIZE,
epochs=EPOCHS,
verbose=False,
callbacks=[DVCLiveCallback(live=live)]
)