1. Allocating Static IPs for AI Platform Notebooks and Instances


    In a cloud environment, a static IP address is a fixed IP address that's manually assigned to a computer or other device. Static IPs are commonly used for servers that need a permanent address, such as an AI Platform Notebook instance in Google Cloud Platform (GCP), which will allow it to have a consistent IP address for accessing services, databases, or for whitelisting purposes.

    Here's how you might set up a GCP AI Platform Notebook with a static IP using Pulumi's Python programming model:

    1. Create a Static IP Address: You will need to reserve a static IP address in the region where you want to deploy your AI Platform Notebook. This is so that the address does not change over time.

    2. Provision an AI Platform Notebook Instance: After that, you will create an AI Platform Notebook instance, which is a managed service that offers an integrated JupyterLab environment fully configured for machine learning.

    3. Associate Static IP: Finally, you will need to ensure that the AI Platform Notebook instance uses the static IP address you created. However, it is important to note that GCP AI Platform Notebook instances might not natively support directly associating a static IP with the instance itself, as it typically uses ephemeral IPs. Instead, you will need to create a VM instance with the static IP and install the AI Platform Notebooks environment on it afterwards, or set up a networking configuration that allows for a static IP to be routed to your Notebook instance.

    Below is a program that achieves the first two steps using Pulumi. For the third step, manual configuration or a more complex network setup may be required. Also, assume that you have authenticated and set up the gcp provider with your project details in your environment.

    import pulumi import pulumi_gcp as gcp # Step 1: Reserve a static IP address in GCP reserved_ip = gcp.compute.Address("static-ip", # The region where you want to reserve the IP. This should match the location of your notebook. region="us-west1", # Optionally, include additional configurations such as labels or description. description="Reserved IP for AI Platform Notebook" ) # Step 2: Create an AI Platform Notebook instance # Replace the placeholders with valid values for `vm_image_project`, `vm_image_family` # or set `vm_image_name` directly if you know the exact image notebook_instance = gcp.notebooks.Instance("notebook-instance", location="us-west1", # VM image configuration for the notebook vm_image={ "project": "deeplearning-platform-release", "image_family": "tf-latest-cpu", # Or 'image_name' if you know the exact name }, machine_type="n1-standard-4", # Choose the appropriate machine type # Set to 'True' if you don't want an external IP to be associated with this instance no_public_ip=False, # Additional configurations such as network, subnetwork, labels, and so forth. ) # Export the IP address and notebook instance details pulumi.export("reserved_static_ip", reserved_ip.address) pulumi.export("notebook_instance_name", notebook_instance.name)

    In the example above, we initiate two resources: a gcp.compute.Address to reserve a static IP address and a gcp.notebooks.Instance to create an AI Platform Notebook instance. We also export important values such as the reserved IP address and the name of the notebook instance, which you can use to refer to these resources later.

    Please be aware that you might need to handle some additional configuration to ensure your notebook uses this static IP, which could involve setting up a Compute Engine VM with the AI Platform Notebooks environment manually or configuring networking rules to route traffic through the reserved IP.

    For further learning, you may want to visit the following pages: