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. PEDL makes it easy to launch and manage notebooks. By default, each notebook is assigned a single GPU, but this can easily be changed -- CPU-Only Notebooks.
PEDL will schedule a Jupyter notebook in a containerized environment on the cluster and proxy HTTP requests to and from the notebook container through the PEDL master. The lifecycle management of Jupyter notebooks in PEDL 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.