tf tensorboard
Consider you are sitting as the only person in a nuclear power plant and you want to know whats going on. Instead of debugging each emergency on an old computer in assembler you want to have a convenient overview. Basically working in a more relaxing way.
Kursk NPP / Sergey Pyatakov / CC-BY-SA 3.0
This is where the TensorBoard comes in. It provides a graphical interface to many parameters and one can follow parameters, learning curves and other settings. In my opinion the use of tensorflow is quite complicated and is surely missing components for easier installation, import of data, control of workflows lets say compared to lets say the H20.ai engine. But maybe tensorflow was not designed for "the masses" in mind, but rather for massively parallel in-house GPU and cluster use.
The datails of the TensorBoard are outlined in the tf whitepaper. Basically nodes write information to local log files which is then picked up by the external TensorBoard software. Graphical output is then provided on different statistics, such as learning rate, cross-entropies and other figures of merit.
Figure from TensorFlow white paper
To start the external tensorboard application, we simply open a new command line terminal and start the application by using the /tmp/ path that holds the current running checkpoint or events.out.tfevents files.
vm@ubuntu:~/tensorflow$ tensorboard --logdir /tmp/cifar10_train/
Starting TensorBoard on port 6006
(You can navigate to http://localhost:6006)
Live Tensorboard - A life tensorboard for the cifar example with Tesla K20 and K40