Keras with TensorBoard - Jupyter Notebook¶
The ClearML_keras_TB_example.ipynb example demonstrates ClearML in automatic logging of code running in Jupyter Notebook that uses Keras and TensorBoard. It trains a simple deep neural network on the Keras built-in MNIST dataset. It 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. When the script runs, it creates an experiment named
Keras with TensorBoard example which is associated with the
Colab notebooks project.
clearml GitHub repository, this example includes a clickable icon to open the notebook in Google Colab.
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 TensorFlow Definitions which appear in CONFIGURATIONS > HYPER PARAMETERS > TF_DEFINE.
Text printed to the console for training progress, as well as all other console output, appear in RESULTS > LOG.
The configuration appears in CONFIGURATIONS > CONFIGURATION OBJECTS > General.