How to “reset” tensorboard data after killing tensorflow instance

Note: The solution you’ve posted (erase TensorBoard’s log files and kill the process) will work, but it isn’t preferred, because it destroys historical information about your training.

Instead, you can have each new training job write to a new subdirectory (of your top-level log directory). Then, TensorBoard will consider each job a new “run” and will create a nice comparison view so you can see how the training differed between iterations of your model.

In the following an example from https://www.tensorflow.org/tensorboard/get_started:

model = create_model()
...
model.compile(...)

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(..., callbacks=[tensorboard_callback])