clearml-docs/docs/guides/frameworks/keras/keras_tensorboard.md

title
Keras with TensorBoard

The example below demonstrates the integration of ClearML into code which uses Keras and TensorBoard. View it in script or in Jupyter Notebook.

:::note The example in Jupyter Notebook includes a clickable icon to open the notebook in Google Colab. :::

The example script does the following:

1. Trains a simple deep neural network on the Keras built-in MNIST dataset.
2. Builds a sequential model using a categorical cross entropy loss objective function.
3. Specifies accuracy as the metric, and uses two callbacks: a TensorBoard callback and a model checkpoint callback.
4. During script execution, creates an experiment named Keras with TensorBoard example, which is associated with the examples project (in script) or the Colab notebooks project (in Jupyter Notebook) .

Scalars

The loss and accuracy metric scalar plots appear in SCALARS, along with the resource utilization plots, which are titled :monitor: machine.

Histograms

Histograms for layer density appear in PLOTS.

Hyperparameters

ClearML automatically logs command line options generated with argparse, and TensorFlow Definitions.

Command line options appear in CONFIGURATION > HYPER PARAMETERS > Args.

TensorFlow Definitions appear in TF_DEFINE.

Console

Text printed to the console for training progress, as well as all other console output, appear in CONSOLE.

Configuration Objects

In the experiment code, a configuration dictionary is connected to the Task by calling the Task.connect method.

task.connect_configuration(
   name="MyConfig" 
   configuration={'test': 1337, 'nested': {'key': 'value', 'number': 1}}
)

It appears in CONFIGURATION > CONFIGURATION OBJECTS > MyConfig.