Thread by @gusthema

For developers, a good debugger and profiler are fundamental tools for their productivity.

On the ML world, TensorBoard can help you with that by enabling:

- Visualizing metrics, model, histograms of weights or biases
- Displaying images, text and audio data
- Profiling

1/5🧵

You can load TensorBoard directly on Colab using a magic word to load the extension and another one to load the tool.

The nice part is that this does not require installing anything on your computer.

2/5🧵

To visualize your training data, you'll need to create a callback and use it on the fit method.

The callback just needs the directory where the log will be written.

3/5🧵

If you want to share your results with other researchers/friends/co-workers, you can upload your logs to TensorBoard.dev

This enables others to use your model and also to replicate experiments more easily.

You can upload your logs straight from colab.

4/5🧵

You can follow the full example here tensorflow.org/tensorboard/ge… and try multiple other features.

A cool feature that you can try is the Embedding Projector here: tensorflow.org/tensorboard/te…

5/5