1. Accelerated Computing for AI with GPU-Enabled Droplets


    Accelerated computing is highly beneficial for AI workloads as it can greatly reduce the time to train and infer from AI models by leveraging the parallel processing capabilities of GPUs. In the context of cloud infrastructure, GPU-enabled instances or "droplets" can be provisioned to provide the necessary computational power for such tasks.

    In this guide, we'll look at how to set up a GPU-enabled virtual machine on Google Cloud Platform (GCP) using Pulumi, an Infrastructure as Code tool. Pulumi allows us to define and manage cloud resources using familiar programming languages.

    The Pulumi resource we'll use is gcp.notebooks.Instance, which represents a Cloud AI Platform Notebook instance in GCP. This resource can be configured to have one or more GPUs attached, making it suitable for accelerated computing tasks.

    Below is a Pulumi program that sets up a GPU-enabled virtual machine for AI workloads. This program is written in Python, and it creates a new instance with a GPU accelerator.

    The provided program does the following:

    • Imports necessary modules.
    • Defines a GCP Notebook instance with a GPU accelerator.
    • Exports the instance URL for easy access.

    Here's the program:

    import pulumi import pulumi_gcp as gcp # Define the GPU-enabled Google Cloud AI Platform Notebook instance gpu_notebook_instance = gcp.notebooks.Instance("gpu-enabled-instance", location="us-west1", # Change to your preferred region machine_type="n1-standard-4", # Specify the machine type project=pulumi.config.require("gcp:project"), # Specify your GCP project ID accelerator_config=gcp.notebooks.InstanceAcceleratorConfigArgs( type="nvidia-tesla-k80", # Specify GPU type core_count=1, # Number of GPU cores ), install_gpu_driver=True, # Automatically install NVIDIA GPU driver ) # Export the URL of the Notebook instance to access the JupyterLab interface pulumi.export("notebook_instance_url", pulumi.Output.concat( "https://console.cloud.google.com/ai-platform/notebooks/instances/", gpu_notebook_instance.name, "?project=", gpu_notebook_instance.project ))

    In the code above:

    • We're creating a Notebook instance with the gcp.notebooks.Instance resource. The instance is located in the us-west1 region, though you may change this to your desired GCP region.
    • The machine_type is set to n1-standard-4. You can select a different machine type based on the amount of CPU and RAM you need.
    • For the GPU accelerator, type is set to nvidia-tesla-k80, which is an available GPU option in GCP. There are other types of GPUs that you can use depending on your budget and requirements, such as nvidia-tesla-t4 or nvidia-tesla-p100.
    • core_count refers to how many GPU cores you want. We have set it to 1 here, but you can increase this number if your workload requires more GPU power.
    • Setting install_gpu_driver to True means that the appropriate NVIDIA GPU driver will be automatically installed on the instance.
    • In the end, we export the URL that you can use to access your notebook instance directly from the Google Cloud Console.

    To run this program:

    1. Ensure you have Pulumi CLI installed and configured with your GCP credentials.
    2. Save this script to a file named __main__.py.
    3. Run pulumi up in the same directory as your script to deploy the notebook instance.

    After the script executes successfully, you will receive a URL as an output. You can use this URL to navigate to your JupyterLab interface and start running your AI workloads with GPU acceleration.