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. HPE Machine Learning Development Environment makes it easy to launch and manage notebooks.
HPE Machine Learning Development Environment Notebooks have the following benefits:
Jupyter Notebooks run in containerized environments on the cluster. We can easily manage dependencies using images and virtual environments. The HTTP requests are passed through the master proxy from and to the container.
Jupyter Notebooks can be automatically terminated if they are idle for a configurable duration to release resources. A notebook instance is considered to be idle if it is not receiving any HTTP traffic and it is not otherwise active (as defined by the
notebook_idle_typeoption in the task configuration). To enable this behavior by default, set
notebook_timeoutoption in your master config. To enable it for a particular notebook, set
idle_timeoutoption in the notebook config.
After a Notebook is terminated, it is not possible to restore the files that are not stored in the persistent directories. You need to ensure that the cluster is configured to mount persistent directories into the container and save files in the persistent directories in the container. See Save and Restore Notebook State for more information.
If you open a Notebook tab in JupyterLab, it will automatically open a kernel that will not be shut down automatically so you need to manually terminate the kernels.
There are two ways to access notebooks in HPE Machine Learning Development Environment: the command-line interface (CLI) and the WebUI. To install the CLI, see Installation.
The following command will automatically start a notebook with a single GPU and open it in your browser.
$ det 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 [Determined] 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 [Determined] 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. [Determined] 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 HPE Machine Learning Development
Environment CLI will open a web browser window pointed to that notebook’s URL. Back in the terminal,
you can use the
det notebook list command to see that this notebook is one of those currently
RUNNING on the HPE Machine Learning Development Environment cluster:
$ det 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
--context option adds a folder or file to the notebook environment, allowing its contents to
be accessed from within the notebook.
det notebook start --context folder/file
--config-file option can be used to create a notebook with an environment specified by a
det notebook start --config-file config.yaml
For more information on how to write the notebook configuration file, see Notebook Configuration.
Useful CLI Commands¶
A full list of notebook-related commands can be found by running:
det notebook --help
To view all running notebooks:
det notebook list
To kill a notebook, you need its ID, which can be found using the
det notebook kill <id>
Notebooks can also be started from the WebUI. You can click the “Tasks” tab to take you to a list of the tasks currently running on the cluster.
From here, you can find running notebooks. You can reopen, kill, or view logs for each notebook.
To create a new notebook, click “Launch Notebook”. If you would like to use a CPU-only notebook, click the dropdown arrow and select “Launch CPU-only Notebook”.
Notebooks can be passed a notebook configuration option to control the notebook environment. For example, to launch a notebook that uses two GPUs:
$ det notebook start --config resources.slots=2
Alternatively, a YAML file can also be used to configure the notebook, using the
$ cat > config.yaml <<EOL description: test-notebook resources: slots: 2 bind_mounts: - host_path: /data/notebook_scratch container_path: /scratch idle_timeout: 30m EOL $ det notebook start --config-file config.yaml
See Job Configuration Reference for details on the supported configuration options.
Finally, to configure notebooks to run a predefined set of commands at startup, you can include a
startup hook in a directory specified with the
$ mkdir my_context_dir $ echo "pip3 install pandas" > my_context_dir/startup-hook.sh $ det notebook start --context my_context_dir
Example: CPU-Only Notebooks
By default, each notebook is assigned a single GPU. This is appropriate for some uses of notebooks
(e.g., training a deep learning model) but unnecessary for other tasks (e.g., analyzing the training
metrics of a previously trained model). To launch a notebook that does not use any GPUs, set
$ det notebook start --config resources.slots=0
Save and Restore Notebook State¶
It is only possible to save and restore notebook state on HPE Machine Learning Development Environment 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.
By default, clusters that are launched by
det deploy aws/gcp up create a Network file system
that is shared by all the agents and automatically mounted into Notebook containers.
For example, a user
jimmy with a shared filesystem home directory at
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 $ det notebook start --config-file config.yaml
To launch a notebook with
det deploy local cluster-up, a user can add the
flag, which mounts the user’s home directory into the task containers by default:
$ det deploy local cluster-up --auto-bind-mount="/shared/home/jimmy" $ det notebook start
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 rather than 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
By default, JupyterLab will take a checkpoint every 120 seconds in an
folder in the same directory as the notebook file. To modify this setting, click on
Advanced Settings Editor and change the value of
Use the HPE Machine Learning Development Environment CLI in Notebooks¶
The HPE Machine Learning Development Environment CLI is installed into notebook containers by
default. This allows users to interact with HPE Machine Learning Development Environment from inside
a notebook—e.g., to launch new deep learning workloads or examine the metrics from an active or
historical HPE Machine Learning Development Environment experiment. For example, to list HPE Machine
Learning Development Environment experiments from inside a notebook, run the notebook command