Jupyter Notebooks is currently an experimental feature.
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/or debug model definition code, visualize results of trained model(s), or even manage the entire training lifecycle of a model manually.
Under the hood, 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.
To launch a notebook, you'll first need to install the PEDL command line interface on a development machine.
Once the PEDL 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 notebooks URL. Back on 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 remain scheduled on the slot 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
One powerful PEDL feature is the ability to easily modify the desired dependencies of your notebook environment:
$ pedl notebook --config environment.tensorflow=1.10.0
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: "1.10.0" keras: "2.2.4" EOL $ pedl notebook start --config-file config.yaml