Hosting Custom Jupyter Notebook Servers for Data Science

Question

Pulumi · Accepted Answer

Hosting custom Jupyter Notebook servers is a common requirement for data science teams that want an environment to run their experiments and analyses. Pulumi can help you to automate the provisioning of the necessary cloud infrastructure to host Jupyter Notebook servers for your data science projects. To achieve this, we will use a cloud provider's resources that support Jupyter Notebook sessions, such as OCI (Oracle Cloud Infrastructure) and its Data Science service.

In the Pulumi Registry Results provided, there is the `oci.DataScience.NotebookSession` which is specifically intended for this purpose. This resource allows you to create and manage notebook sessions within the Oracle Cloud Infrastructure. Here, we will focus on creating a custom Jupyter Notebook session using Oracle Cloud's data science service.

Before we begin with the code, let's outline the key steps we'll follow:

1. Set up a project - to organize and manage the resources we create.
2. Create a notebook session - to establish an environment for running Jupyter Notebook.
3. Configure the notebook session details - such as the compute shape, which defines the CPU and memory resources allocated to your Jupyter Notebook server.
4. Optionally, set up access and security parameters - such as virtual cloud network (VCN) and subnet configurations to ensure secure and flexible access to the notebook server.

Here's how you can use Pulumi with Python to set up your custom Jupyter Notebook server:

```python
import pulumi
import pulumi_oci as oci

# Create a new Data Science project
data_science_project = oci.datascience.Project("dataScienceProject",
    compartment_id=oci.config.require("compartment_id"),
    description="Data Science project for Jupyter Notebook servers",
    display_name="JupyterNotebookProject",
)

# Set up notebook session configuration details
# Here you can specify the shape (CPU and memory) and other configurations
notebook_session_config_details = oci.datascience.NotebookSessionConfigurationDetailsArgs(
    shape="VM.Standard.E2.2",  # Example shape name. Please choose as per your requirement
    subnet_id="your-subnet-id",  # Use the appropriate subnet ID from your OCI setup
    block_storage_size_in_gbs=50,  # Allocate 50GB of block storage for the notebook session
)

# Create a new Data Science Notebook Session
notebook_session = oci.datascience.NotebookSession("notebookSession",
    compartment_id=oci.config.require("compartment_id"),
    project_id=data_science_project.id,
    display_name="JupyterNotebookSession",
    notebook_session_configuration_details=notebook_session_config_details,
)

# Export the notebook session url so it can be accessed
pulumi.export("notebook_session_url", notebook_session.notebook_session_url)
```

To utilize the above code, you will need to have Oracle Cloud Infrastructure (OCI) setup, including a compartment and a subnet. The `oci.config.require("compartment_id")` call pulls your OCI compartment ID from the Pulumi configuration, and you are expected to replace `"your-subnet-id"` with the actual Subnet ID that you have set up in OCI.

The notebook session shape specified as `VM.Standard.E2.2` is just an example, and you should select a shape that fits your data processing needs and budget. Oracle Cloud Infrastructure offers various compute shapes so you can choose one according to the scale of data processing or computational workloads that your data science team needs.

Finally, the `pulumi.export` statement outputs the URL of your notebook session, which you and your team can use to access the Jupyter Notebook after OCI provisions the instance.

To run this program, you'll need to install the Pulumi CLI, configure your OCI credentials with Pulumi, have the Python language runtime, and the Pulumi `oci` Python package installed. Once you've set these up, you can run `pulumi up` from your command line in the directory where this Pulumi program is saved to provision the resources defined in your code.