1. Prototyping Large Language Models with GCP Notebooks.


    To prototype Large Language Models using GCP Notebooks with Pulumi, you would need to utilize Google Cloud Platform resources such as AI Platform Notebooks. With Pulumi, you can describe and deploy cloud infrastructure using general-purpose programming languages such as Python.

    In this case, you will create an AI Platform Notebook instance in GCP that can be used to work with large language models. The Notebook instance will be a virtual machine configured to run JupyterLab, which is an interactive development environment for working with notebooks, code, and data.

    Below is a Pulumi program written in Python that sets up an AI Platform Notebook instance in GCP:

    import pulumi import pulumi_gcp as gcp # We will start by creating a new GCP Notebook instance which can be used for prototyping an LLM. # The instance will consist of predefined machine type with necessary resource allocations such as vCPUs and memory. # It will also have the required permissions to access different GCP services that might be necessary to train # and handle a large language model. # Configuration for the Notebook instance notebook_instance_name = "llm-notebook-instance" project = "your-gcp-project-id" # Please replace with your own GCP project ID location = "us-west1" # Choose the region that fits your needs machine_type = "n1-standard-4" # You can change the machine type based on your requirement vm_image_project = "deeplearning-platform-release" vm_image_family = "common-cpu-notebooks" # You can choose different image families based on whether you need CPUs or GPUs # Create GCP Notebook instance notebook_instance = gcp.notebooks.Instance(notebook_instance_name, project=project, location=location, machine_type=machine_type, vm_image={ "project": vm_image_project, "image_family": vm_image_family, }, boot_disk_size_gb=50, # Adjust the boot disk size as necessary data_disk_size_gb=100, # Adjust the data disk size which will be used to store models, datasets, etc. noRemoveDataDisk=False, ) # Export the Notebook instance URL which can be used to access the JupyterLab environment pulumi.export('notebook_instance_url', pulumi.Output.concat( "https://console.cloud.google.com/ai/platform/notebooks/instances/detail/", notebook_instance_name, "?project=", project, ))

    This program defines a Pulumi project that creates a single instance of gcp.notebooks.Instance. This is the AI Platform Notebooks service that allows you to create and manage interactive notebook instances directly in Google Cloud.

    Let's go through the key parts of this program:

    1. We set our project and region, defining where our resources will be located in GCP.
    2. The machine_type variable determines the computing resources available to your Notebook instance - it can be adjusted based on the size and scope of your models.
    3. For the vm_image_project and vm_image_family, we select appropriate images provided by GCP's Deep Learning VM Image family. If you require GPUs for your large language model, ensure you select an image family with GPU support and specify an appropriate machine type.
    4. We set both a boot disk and a data disk with specified sizes. The data disk is particularly important for storage-intensive tasks such as training large models.
    5. After creating the instance, the program exports the URL for the Notebook instance which can be used to access your JupyterLab environment.

    To use this program, you will need to have Pulumi installed and configured for GCP access. You should replace placeholder values such as your-gcp-project-id with actual values from your GCP setup. Once deployed, Pulumi will provide the URL for the Notebook instance as an output, which you can use to start working on prototyping with Large Language Models.

    Ensure you have the necessary quotas and permissions in GCP for creating AI Platform Notebook instances and that the Pulumi CLI is authenticated against your Google Cloud account.