¿Cómo funciona TensorBoard?

How does TensorBoard work?

TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.

Visualize the Computational Graph You can access this view by clicking on the Graphs view on in TensorBoard. It should look like the image below. You can see that you have a nice flow from train_inputs to loss and predictions flowing through the hidden layers 1 to 5.

Run %load_ext tensorboard to enable the %tensorboard magic command. Start and view your TensorBoard by running %tensorboard –logdir $experiment_log_dir , where experiment_log_dir is the path to a directory in DBFS dedicated to TensorBoard logs.

Note: Having TensorFlow installed is not a prerequisite to running TensorBoard, although it is a product of the TensorFlow ecosystem, TensorBoard by itself can be used with PyTorch.

Tensorboard does not show real-time updates while training without Tensorflow installation.

The following works for me:

1. CTRL + Z halts the on-going TensorBoard process.
2. Check the id of this halted process by typing in the terminal. jobs -l.
3. kill this process, otherwise you can’t restart TensorBoard with the default port 6006 (of course, you can change the port with –port=xxxx ) kill -9 #PROCESS_ID.

Examining loss using TensorBoard Now, start TensorBoard, specifying the root log directory you used above. Wait a few seconds for TensorBoard’s UI to spin up. You may see TensorBoard display the message “No dashboards are active for the current data set”. That’s because initial logging data hasn’t been saved yet.

Setup

1. # Load the TensorBoard notebook extension. %load_ext tensorboard.
2. from datetime import datetime. from packaging import version.
3. import tensorboard. tensorboard.
4. # Define the model. model = keras.
5. (train_images, train_labels), _ = keras. datasets.
6. # Define the Keras TensorBoard callback.
7. %tensorboard –logdir logs.
8. !

So, both TensorFlow and PyTorch provide useful abstractions to reduce amounts of boilerplate code and speed up model development. The main difference between them is that PyTorch may feel more “pythonic” and has an object-oriented approach while TensorFlow has several options from which you may choose.

TensorBoard is a visualization toolkit for machine learning experimentation. TensorBoard allows tracking and visualizing metrics such as loss and accuracy, visualizing the model graph, viewing histograms, displaying images and much more.

Once that is done you have to set a log directory. This is where TensorBoard will store all the logs. It will read from these logs in order to display the various visualizations. In the event that you want to reload the TensorBoard extension, the command below will do the magic — no pun intended.

Install TensorBoard through the command line to visualize data you logged Now, start TensorBoard, specifying the root log directory you used above. Argument logdir points to directory where TensorBoard will look to find event files that it can display.

Then when running TensorBoard you can click the different runs on and off provided you ran TensorBoard in the parent directory. If you know you don’t care about a previous run and want it out of your life, then yes, you need to remove the event files and restart TensorBoard.

How to use TensorBoard with PyTorch 1 Installation. PyTorch should be installed to log models and metrics into TensorBoard log directory. 2 Using TensorBoard in PyTorch. 3 Log scalars. 4 Run TensorBoard. Now, start TensorBoard, specifying the root log directory you used above. 5 Share TensorBoard dashboards. 6 Learn More