Keras Tuner

tip

If you are not already using ClearML, see Getting Started for setup instructions.

Integrate ClearML into code that uses Keras Tuner. By specifying ClearMLTunerLogger (see kerastuner.py) as the Keras Tuner logger, ClearML automatically logs scalars and hyperparameter optimization.

ClearMLTunerLogger

Take a look at keras_tuner_cifar.py example script, which demonstrates the integration of ClearML in a code that uses Keras Tuner.

The script does the following:

1. Creates a Hyperband object, which uses Keras Tuner's Hyperband tuner. It finds the best hyperparameters to train a network on a CIFAR10 dataset.
2. When the Hyperband object is created, instantiates a ClearMLTunerLogger object and assigns it to the Hyperband logger. The ClearMLTunerLogger class provides the required binding for ClearML automatic logging.

tuner = kt.Hyperband(
    build_model,
    project_name='kt examples',
    logger=ClearMLTunerLogger(),
    objective='val_accuracy',
    max_epochs=10,
    hyperband_iterations=6
)

When the script runs, it logs:

• Tabular summary of hyperparameters tested and their metrics by trial ID
• Scalar plot showing metrics for all runs
• Summary plot
• Output model with configuration and snapshot location.

Scalars

ClearML logs the scalars from training each network. They appear in the experiment's page in the ClearML web UI, under SCALARS.

Summary of Hyperparameter Optimization

ClearML automatically logs the parameters of each experiment run in the hyperparameter search. They appear in tabular form in PLOTS.

Artifacts

ClearML automatically stores the output model. It appears in ARTIFACTS > Output Model.

Model details, such as snap locations, appear in the MODELS tab.

The model configuration is stored with the model.

Configuration Objects

Hyperparameters

ClearML automatically logs the TensorFlow Definitions, which appear in CONFIGURATION > HYPERPARAMETERS.

Configuration

The Task configuration appears in CONFIGURATION > General.