For this purpose, follow the following steps: $ tensorboard -log-dir=logsīut, you want to be able to open and view the Tensorboard on your local machine browser. Then with the following command, Tensorboard will be launched on the 6006 port of the server you are connecting with an SSH connection to it. For using this toolkit, some lines of code must be added to the Python source code for logging like as follows: import datetime # Create a TensorBoard callback profile_logs = 'logs/' + ().strftime('%Y%m%d-%H%M%S') tboard_callback = tf.(log_dir = profile_logs, histogram_freq = 1, profile_batch = '500,520') model.fit(training_data, training_labels, epochs=15, callbacks=) This toolkit visualizes metrics like accuracy and loss, model graph (operations and layers), and many others that you know about them (if you don’t know you can learn from here).

Tensorboard is TF’s visualization toolkit. It has eased the life of ML developers, especially Deep Learning developers.

TensorFlow (TF) is one of the popular Machine Learning (ML) frameworks.

Remote Tensorboard Viewing on Your Local Browser

Tensorboard docker on mac