1. Managed Jupyter Notebooks for Model Development on GCP.


    Managed Jupyter Notebooks on Google Cloud Platform (GCP) provide a convenient way for data scientists and machine learning practitioners to set up a secure and configurable environment for developing and running machine learning models. This environment comes with many popular frameworks and libraries pre-installed, such as TensorFlow and scikit-learn, and it can be easily accessed and managed through GCP's console or via the command line.

    To deploy such an environment using Pulumi, a powerful infrastructure as code tool, one can use the Google Cloud Platform (GCP) provider to provision and manage resources. Below is a Pulumi Python program that sets up a Managed Jupyter Notebook instance on GCP.

    The key resources in this program are:

    • gcp.notebooks.Instance: This resource is used to create and manage the actual Jupyter Notebook instance on GCP. By specifying various properties like machine type, disk type, and service account, you can configure the instance to suit your needs.

    Here's how you would write the Pulumi program to create a managed Jupyter Notebook environment:

    import pulumi import pulumi_gcp as gcp # Create a new GCP Jupyter Notebook instance. notebook_instance = gcp.notebooks.Instance("my-notebook-instance", # Provide the location, machine type, and other properties required for the instance. location="us-central1", machine_type="n1-standard-4", boot_disk_size_gb=100, data_disk_size_gb=200, # Providing a service account to be used by the instance. service_account="your-service-account", # Optional: Use a custom container image for the Notebook environment. # container_image={ # "repository": "gcr.io/your-project-id/your-custom-container-image", # "tag": "latest" # }, # Enable GPU support if needed, specify the type and count of GPUs. # accelerator_config={ # "type": "nvidia-tesla-k80", # "core_count": 1, # }, # Specify the network and subnet if a custom network configuration is needed. network="projects/your-project-id/global/networks/default", subnet="projects/your-project-id/regions/us-central1/subnetworks/default", # Optionally, enable VPC network only with no public IP addresses. # no_public_ip=True, # no_proxy_access=True, ) # The following output will give you the URL needed to access your Jupyter Notebook instance. pulumi.export('instance_url', pulumi.Output.concat("https://notebooks.googleapis.com/v1/projects/", gcp.config.project, "/locations/", notebook_instance.location, "/instances/", notebook_instance.name))

    In the above code:

    • Replace us-central1 with the region where you want to deploy your instance.
    • Adjust machine_type, boot_disk_size_gb, and data_disk_size_gb to meet your computational and storage requirements.
    • Replace your-service-account with the service account that should run the notebook instance.
    • Optionally, you can use custom container images by specifying the container_image configuration.
    • If GPU support is needed for your model development, you can specify the GPU type and core count in the accelerator_config.
    • Set the network and subnet to the desired network configurations.
    • Uncomment and set no_public_ip and no_proxy_access to True if you want the instance to be accessible only from within the VPC network.
    • Replace your-project-id with your actual GCP project ID.

    Make sure to replace placeholder values with actual values specific to your GCP environment. After deploying this code using Pulumi, you will get an output with the instance URL that you can use to access your Jupyter Notebook environment.

    Keep in mind that you need appropriate permissions and a configured GCP project for Pulumi to deploy these resources. If you are running this code outside of the GCP Cloud Shell, make sure you have installed the Pulumi CLI and logged in using pulumi login, and have your GCP credentials set up correctly.