🤹Tensorboard

Tensorboard provides a suite of visualization tools to make it easier to understand, debug, and optimize your experiment runs. It includes logging features for scalar, histogram, model structure, embeddings, and text & image visualization. The workspace comes pre-installed with jupyter_tensorboard extension that integrates Tensorboard into the Jupyter interface with functionalities to start, manage, and stop instances. You can open a new instance for a valid logs directory, as shown below:

If you have opened a Tensorboard instance in a valid log directory, you will see the visualizations of your logged data:

Tensorboard can be used in combination with many other ML frameworks besides Tensorflow. By using the tensorboardX library you can log basically from any python based library. Also, PyTorch has a direct Tensorboard integration as described here.

If you prefer to see the tensorboard directly within your notebook, you can make use of following Jupyter magic:

%load_ext tensorboard
%tensorboard --logdir /workspace/path/to/logs