TensorFlow comes prepackaged with TensorBoard, so it will already be installed. It runs as a locally served web application accessible via the browser at http://0.0.0.0:6006. Conveniently, there is no server-side code or configurations required.

Depending on where your paths are, you may be able to run it directly, as follows:

tensorboard --logdir=/tmp/tensorlogs

If your paths are not correct, you may need to prefix the application accordingly, as shown in the following command line:

tf_install_dir/ tensorflow/tensorboard --
logdir=/tmp/tensorlogs

On Linux, you can run it in the background and just let it keep running, as follows:

nohup tensorboard --logdir=/tmp/tensorlogs &

Some thought should be put into the directory structure though. The Runs list on the left side of the dashboard is driven by subdirectories in the logdir location. The following image shows two runs--MNIST_Run1 and MNIST_Run2. Having an organized runs folder will allow plotting successive runs side by side to see differences:

When initializing writer, you will pass in the directory for the log as the first parameter, as follows:

   writer = tf.summary.FileWriter("/tmp/tensorlogs",   
   sess.graph) 

Consider saving a base location and appending run-specific subdirectories for each run. This will help organize outputs without expending more thought on it. We'll discuss more about this later.