How can we utilize the TensorBoard

Tensorboard is an interactive visualization tool that reads log files and helps you track experiment metrics.

For it to work with Keras's model.fit() method you need to add Tensorboard callback to model.fit() method.

log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(
                                      log_dir=log_dir, 
                                      histogram_freq=1)
model.fit(x=x_train, 
          y=y_train, 
          epochs=5, 
          validation_data=(x_test, y_test), 
          callbacks=[tensorboard_callback])

When you have your own training loop you can define a summary writer and use it to write a summary like this:

current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
train_log_dir = 'logs/gradient_tape/' + current_time + '/train'
test_log_dir = 'logs/gradient_tape/' + current_time + '/test'
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
test_summary_writer = tf.summary.create_file_writer(test_log_dir)

for epoch in range(EPOCHS):
for (x_train, y_train) in train_dataset:
        train_step(model, optimizer, x_train, y_train)
      with train_summary_writer.as_default():
        tf.summary.scalar('loss', train_loss.result(), step=epoch)
        tf.summary.scalar('accuracy', train_accuracy.result(), step=epoch)

In any case, you will need to provide a log directory to run Tensorboard. (It doesn't pop up by itself when you train your network. You will have to run it separately. You can run it while training to monitor metrics at the end of each loop).

tensorboard --logdir yourmodel/logs/dir

If you want to make it work with Jupyter notebook you will have to load the Extention and then run magic command %tensorboard --logdir yourmodel/logs/dir

The code is taken from Tensorboard's getting started link. I hope it helps you understand how it works. Otherwise, you can visit these links. 1, 2

As per my experience, Tensorboard not behaving correctly on Windows machine. For example, if you want to see or put a break-point on a particular condition during model training, at least I faced lots of challenges.

On Ubuntu it's working perfectly fine for the cases where I faced issue on Windows, although I still have to explore a lot on its functionality.

So as per my suggestion, try Tensorboard on Ubuntu.