Jupyter notebooks are a convenient way to develop and debug machine learning models, visualize the behavior of trained models, or even manage the training lifecycle of a model manually. Determined makes it easy to launch and manage notebooks. By default, each notebook is assigned a single GPU. However, this can be modified, see Example: CPU-Only Notebooks for details.
Determined will schedule a Jupyter notebook in a containerized environment on the cluster and proxy HTTP requests to and from the notebook container through the Determined master. The lifecycle management of Jupyter notebooks in Determined is left up to the user—once a Jupyter notebook has been scheduled onto the cluster, it will remain scheduled indefinitely until the user explicitly shuts down the notebook. Once a notebook has been terminated, it is not possible to reactivate it. However, new notebooks can easily be configured to restore the state of a previous notebook—see Saving and Restoring Notebook State for more information.