The keras_tensorboard.py example demonstrates the integration of ClearML into code which uses Keras and TensorBoard.
The example does the following:
- Trains a simple deep neural network on the Keras built-in MNIST dataset.
- Builds a sequential model using a categorical crossentropy loss objective function.
- Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.
- During script execution, it creates an experiment named
Keras with TensorBoard examplewhich is associated with the
The loss and accuracy metric scalar plots appear in the RESULTS > SCALARS, along with the resource utilization plots, which are titled :monitor: machine.
Histograms for layer density appear in RESULTS > PLOTS.
ClearML automatically logs command line options generated with
argparse, and TensorFlow Definitions.
Command line options appear in CONFIGURATIONS > HYPER PARAMETERS > Args.
TensorFlow Definitions appear in TF_DEFINE.
Text printed to the console for training progress, as well as all other console output, appear in RESULTS > CONSOLE.
In the experiment code, a configuration dictionary is connected to the Task by calling the Task.connect method.
It appears in CONFIGURATIONS > CONFIGURATION OBJECTS.