PEDL offers the ability to easily launch Jupyter notebooks attached to one or more slots in the cluster. Jupyter notebooks offer a convenient interface to develop and debug machine learning models, visualize the behavior of trained models, or even manage the training lifecycle of a model manually.
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 new 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 and the slot freed, it is not possible to reactivate it. However, new notebooks can easily be configured to restore the state from a previous notebook -- see Saving and Restoring Notebook State for more information.
To launch a notebook, start by installing the PEDL command line interface on a development machine.
Once the CLI is installed, try launching your first notebook with the
pedl notebook start command:
$ pedl notebook start Scheduling notebook unique-oyster (id: 5b2a9ea4-a6bb-4d2b-b42b-25e4064a3220)... [DOCKER BUILD 🔨] Step 1/11 : FROM nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04 [DOCKER BUILD 🔨] [DOCKER BUILD 🔨] ---> 9918ba890dca [DOCKER BUILD 🔨] Step 2/11 : RUN rm /etc/apt/sources.list.d/* ... [DOCKER BUILD 🔨] Successfully tagged nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04-73bf63cc864088137a477ce62f39ffe8 [PEDL] 2019-04-04T17:53:22.076591700Z [I 17:53:22.075 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret [PEDL] 2019-04-04T17:53:23.067911400Z [W 17:53:23.067 NotebookApp] All authentication is disabled. Anyone who can connect to this server will be able to run code. [PEDL] 2019-04-04T17:53:23.073644300Z [I 17:53:23.073 NotebookApp] Serving notebooks from local directory: / disconnecting websocket Jupyter Notebook is running at: http://localhost:8080/proxy/5b2a9ea4-a6bb-4d2b-b42b-25e4064a3220-notebook-0/lab/tree/Notebook.ipynb?reset
After the notebook has been scheduled onto the cluster, the PEDL CLI will open a web browser window pointed to that notebook's URL. Back in the terminal, you can use the
pedl notebook list command to see this notebook as part of those currently
RUNNING on the PEDL cluster:
$ pedl notebook list Id | Entry Point | Registered Time | State --------------------------------------+--------------------------------------------------------+------------------------------+--------- 0f519413-2411-4b3c-adbc-9b1b60c96156 | ['jupyter', 'notebook', '--config', '/etc/jupyter.py'] | 2019-04-04T17:52:48.1961129Z | RUNNING 5b2a9ea4-a6bb-4d2b-b42b-25e4064a3220 | ['jupyter', 'notebook', '--config', '/etc/jupyter.py'] | 2019-04-04T17:53:20.387903Z | RUNNING 66da599e-62d2-4c2d-91c4-01a04045e4ab | ['jupyter', 'notebook', '--config', '/etc/jupyter.py'] | 2019-04-04T17:52:58.4573214Z | RUNNING
Since the lifecycle management of Jupyter notebooks in PEDL is left up to the user, this notebook will continue running on the slot until it is explicitly shut down. To shut down the notebook and free up the slot, you can use the
pedl notebook kill command:
$ pedl notebook kill 5b2a9ea4-a6bb-4d2b-b42b-25e4064a3220 Killed notebook 5b2a9ea4-a6bb-4d2b-b42b-25e4064a3220
PEDL also makes it easy to modify the dependencies that are installed into the notebook's environment:
$ pedl notebook start --config environment.tensorflow=1.13.1
More generally, notebooks may be supplied an optional notebook configuration to configure the notebook's enviornment. In addition to the
--config flag, configuration may also be supplied via a YAML file (
$ cat > config.yaml << EOL description: test-notebook resources: slots: 2 environment: python: "3.6.9" tensorflow: "1.13.1" keras: "2.2.4" bind_mounts: - host_path: /data/notebook_scratch container_path: /scratch EOL $ pedl notebook start --config-file config.yaml
Saving and Restoring Notebook State¶
It is only possible to save and restore notebook state on PEDL clusters that are configured with a shared filesystem available to all agents.
To ensure that your work is saved even if your notebook gets terminated, it is recommended to launch all notebooks with a shared filesystem directory bind-mounted into the notebook container and work on files inside of the bind mounted directory. For example, a user
jimmy with a shared filesystem home directory at
/shared/home/jimmy could use the following configuration to launch a notebook:
$ cat > config.yaml << EOL bind_mounts: - host_path: /shared/home/jimmy container_path: /shared/home/jimmy EOL $ pedl notebook start --config-file config.yaml
Working on a notebook file within the shared bind mounted directory will ensure that your code and Jupyter checkpoints are saved on the shared filesystem as opposed to on an ephemeral container filesystem. If your notebook gets terminated, launching another notebook and loading the previous notebook file will effectively restore the session of your previous notebook. To restore the full notebook state (in addition to code), you can use Jupyter's
Revert to Checkpoint functionality.
By default, Jupyter Lab will take a checkpoint every 120 seconds in an
.ipynb_checkpoints folder in the same directory as the notebook file. To modify this setting, click on
Advanced Settings Editor, and change the value of