The ClearML_keras_TB_example.ipynb example demonstrates ClearML automatically logging code, which is running in Jupyter Notebook and is using Keras and TensorBoard.
The example script 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, creates an experiment named
Keras with TensorBoard examplewhich is associated with the
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 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 > CONSOLE.
The configuration appears in CONFIGURATIONS > CONFIGURATION OBJECTS > General.