1. Interactive AI Research with GCP Notebooks.


    To accomplish your goal of setting up an interactive AI research environment using GCP Notebooks, you'll need to create a GCP Notebook instance which is a managed service providing an environment to run JupyterLab notebooks for data science and machine learning tasks.

    Here's how you can use Pulumi to automate this process:

    1. GCP Notebooks Instance: You'll use the Instance resource to create a virtual machine that is pre-installed with machine learning frameworks such as TensorFlow or PyTorch along with JupyterLab.

    2. Configuration: You need to configure the Instance with the desired machine type, location, VM image for the notebook, and other specifications like disk size and access control.

    3. Networking: You may also need to configure networking settings to determine how the instance can be accessed.

    As you're a novice, let's go through this step by step in the Pulumi program, creating a simple JupyterLab instance suitable for AI research. Below is a Python program that creates a GCP Notebook instance using Pulumi:

    import pulumi import pulumi_gcp as gcp # Define the configuration for your GCP Notebook Instance # Replace the placeholders with your desired configuration notebook_instance_name = "ai-research-notebook" project = gcp.config.project location = gcp.config.region machine_type = "n1-standard-4" # Adjust the machine type based on your needs vm_image_project = "deeplearning-platform-release" vm_image_family = "tf2-latest-cpu" # TensorFlow 2 image family # Create a GCP Notebook Instance notebook_instance = gcp.notebooks.Instance("notebookInstance", project=project, location=location, name=notebook_instance_name, machine_type=machine_type, vm_image={ "project": vm_image_project, "image_family": vm_image_family } ) # Export the notebook instance URL so it can be easily accessed pulumi.export('notebook_instance_url', pulumi.Output.concat( "https://notebooks.googleapis.com/v1beta1/", project, "/locations/", location, "/instances/", notebook_instance.name))

    In this code, we import the Pulumi GCP package and use the gcp.notebooks.Instance class to create a new notebook instance. We define several variables for our project and location, as well as our instance settings, such as machine type and the VM image.

    After we've defined and created our instance, we use pulumi.export to output the URL of the notebook instance. This URL will serve as an endpoint for us to access our JupyterLab environment.

    Keep in mind that more complex configurations may require additional networking and security settings, but this script provides a basic setup to get started with AI research using GCP Notebooks. Make sure you have the right permissions and the GCP provider configured before running this Pulumi program.