1. Disaster Recovery for AI Workflows on DigitalOcean Droplets


    Disaster recovery is an important aspect of maintaining AI workflows, as it ensures that your application's data and operation can survive various failure scenarios. DigitalOcean Droplets are virtual machines that can run applications and store data. For disaster recovery on DigitalOcean, you would typically need a strategy that includes backing up your data, replicating it to another region, and possibly having machines ready to take over in case the primary ones fail.

    In this Pulumi program in Python, we will create Droplets in DigitalOcean that serve as the primary hosts for your AI workflows. We will also create snapshots as backups of our Droplets periodically. This provides us with the ability to restore the system state to the point when the snapshot was taken. Additionally, we could include a routine to clone these Droplets and deploy them to a different region for regional redundancy.

    Here's what we're going to do in this program:

    1. Create two DigitalOcean Droplets as our primary and standby servers.
    2. Set up SSH keys for secure access to the Droplets.
    3. Configure backups and monitoring for the Droplets.
    4. Create snapshots periodically for disaster recovery.
    5. Export the necessary information such as the Droplet IPs, snapshot IDs, etc.

    Let's write the code for this setup.

    import pulumi import pulumi_digitalocean as digitalocean # Step 1: Configure Your SSH Key for DigitalOcean so you can access the Droplets securely ssh_key = digitalocean.SshKey("my-ssh-key", public_key="ssh-rsa YOUR_SSH_PUBLIC_KEY_HERE") # Step 2: Create two droplets in different regions for our AI Workflows. # The first will be the primary, and the second as the standby. primary_droplet = digitalocean.Droplet("primary-ai-droplet", image="ubuntu-18-04-x64", region="nyc3", size="s-1vcpu-1gb", backups=True, monitoring=True, ssh_keys=[ssh_key.id], tags=["ai-workflow"]) standby_droplet = digitalocean.Droplet("standby-ai-droplet", image="ubuntu-18-04-x64", region="sfo2", size="s-1vcpu-1gb", backups=True, monitoring=True, ssh_keys=[ssh_key.id], tags=["ai-workflow-standby"]) # Step 3: Create snapshot of the primary droplet periodically for disaster recovery # In a real scenario, you may want this to be triggered by some CI/CD process # or a scheduled time rather than upon each deployment. primary_snapshot = digitalocean.DropletSnapshot("primary-snapshot", name="primary-droplet-snapshot", droplet_id=primary_droplet.id) # Step 4: Export the Droplet IPs and snapshot IDs as the program's outputs pulumi.export("primary_droplet_ip", primary_droplet.ipv4_address) pulumi.export("standby_droplet_ip", standby_droplet.ipv4_address) pulumi.export("primary_snapshot_id", primary_snapshot.id)

    How to use the code:

    1. Replace YOUR_SSH_PUBLIC_KEY_HERE with your actual SSH public key.
    2. Run the code using the Pulumi CLI by typing pulumi up in your terminal. Ensure you have the correct DigitalOcean token configured.
    3. After deployment, you will see the output, including the primary and standby Droplet IPs and snapshot ID.


    • SSH keys are set up and associated with the Droplets for secure access.
    • Two Droplets are created, one as the primary and the other as standby, in different regions.
    • We enable backups during the Droplet creation, which DigitalOcean uses to take automatic snapshots of your Droplet at regular intervals.
    • We request additional snapshots manually for additional restore points. The snapshot process could be automated further with real-time triggers or schedules.
    • The Droplet information is exported, so it can be easily accessed and used when necessary.

    Remember, disaster recovery is a broad topic and might require additional strategies such as database replication, setting up failover mechanisms, and regular testing of the recovery process. This program is a starting point and should be expanded to meet your actual operational and disaster recovery requirements.