1. GPU-Accelerated Machine Learning on DigitalOcean


    To set up a GPU-Accelerated Machine Learning environment on DigitalOcean, you will need a Droplet (Virtual Machine) that has GPU capabilities. DigitalOcean doesn't have specific GPU offering as typical cloud providers do, but you could utilize their compute-optimized Droplets with powerful CPUs and optimize your software stack to perform machine learning tasks. Additionally, you could set up a project to organize your resources and manage your infrastructure efficiently.

    Below you'll find a Pulumi program written in Python that will create a new DigitalOcean Droplet that's suitable for compute-intensive tasks. Note that while DigitalOcean doesn't offer GPU specialized Droplets, the example will focus on creating a high-CPU Droplet which could be leveraged for parallelizable tasks such as machine learning computation that doesn't explicitly require a GPU.

    Understanding the resources:

    • Droplet: This is the primary resource for computing on DigitalOcean. It represents a virtual server in the cloud.

    • Project: A DigitalOcean project is a way to organize and manage your resources. This is helpful for grouping your infrastructure in a way that makes sense for your application or environment.

    • Region: When creating resources on DigitalOcean, it's necessary to specify the region where your resources will reside. This could be important for latency and data residency considerations.

    • Image: Images are the base operating systems or snapshots that your Droplet will be based on. For machine learning applications, it's common to start with an OS that can easily be configured with machine learning libraries, like Ubuntu.

    • Size: Specifies the size of the Droplet, which determines the resources (CPU, Memory) available to your application. For machine learning tasks, you would choose a more powerful size.

    Now, let's look at the Pulumi program which will set up your machine learning infrastructure:

    import pulumi import pulumi_digitalocean as digitalocean # Create a new DigitalOcean project for organizing resources project = digitalocean.Project("ml-project", name="gpu-accelerated-ml", description="Project for GPU Accelerated ML", purpose="Machine Learning/Artificial Intelligence", environment="Production") # Define the specification for a compute-optimized Droplet # Note: Replace 'droplet_size_slug' with the slug of the desired size from DigitalOcean's size list droplet_size_slug = 'c-32' # This is an example slug for a compute-optimized Droplet region = 'nyc3' # Set this to your desired region # Create a new Droplet in the project droplet = digitalocean.Droplet("ml-droplet", image="ubuntu-20-04-x64", size=droplet_size_slug, region=region, tags=["machine-learning", "gpu"], project=project.id) # Export the IP address of the Droplet pulumi.export('ip', droplet.ipv4_address)

    In this program, replace 'droplet_size_slug' with the appropriate slug for the Droplet size you want to use. You can find the compute-optimized Droplet sizes in the DigitalOcean documentation.

    Once this script is executed with Pulumi, it will create a new compute-optimized Droplet associated with the specified project, which can be used to run machine learning applications. Remember that while GPUs are not provided as part of these Droplets, high-CPU options can still be advantageous for certain types of parallel compute tasks typical in ML workloads.

    To run this script, you'll need to have Pulumi installed and configured to access your DigitalOcean account. Save the script in a file named __main__.py, and execute it by running pulumi up in the same directory. Pulumi will handle provisioning the resources as defined in the script.