1. Storing AI Training Datasets in MySQL on DigitalOcean


    If you need to store AI training datasets in a MySQL database on DigitalOcean, using Pulumi to automate infrastructure deployment is a great option. Pulumi allows you to define infrastructure using familiar programming languages such as Python, which makes the process more intuitive and maintainable.

    Here’s what we will do to store the datasets:

    1. We'll create a DigitalOcean droplet, which is a virtual machine where we can run our MySQL server.
    2. We'll set up a MySQL database within the droplet.
    3. Lastly, we'll ensure that our data is securely stored and accessible for our AI application.

    Before running this code, make sure you have Pulumi installed, and you have configured the Pulumi DigitalOcean provider by setting up the necessary access token. This code assumes that you've completed these prerequisites.

    Below is a Python program for Pulumi that demonstrates how to set up a MySQL database on DigitalOcean:

    import pulumi import pulumi_digitalocean as do from pulumi.command import local # Configure the DigitalOcean provider with access token (ensure this is set in your environment) pulumi.Config.require_secret('digitalocean:token') # Create a DigitalOcean droplet for our MySQL database server droplet = do.Droplet("ai-training-db-droplet", image="mysql-8-x64", region="nyc3", size="s-1vcpu-1gb", tags=["ai-training"], ) # The following command runs once the droplet is created to set up MySQL. # Adjust the command as per your database setup requirements. # Output the IP address of the Droplet ip_address = droplet.ipv4_address # A provisioner to run a remote command that sets up MySQL # Replace 'your_user' and 'your_password' with the desired database username and password. init_mysql = local.Command("init-mysql", create=f'ssh root@{droplet.ipv4_address.apply(lambda ip: ip)} "mysql -u root -e \'CREATE DATABASE ai_training; CREATE USER \'your_user\'@\'%\' IDENTIFIED BY \'your_password\'; GRANT ALL ON ai_training.* TO \'your_user\'@\'%\'; FLUSH PRIVILEGES;\'"', environment={ "MYSQL_ROOT_PASSWORD": "my_password" # Replace 'my_password' with the root password for MySQL. }, # Using 'depends_on' to ensure that the database is initialized after the droplet is created. opts=pulumi.ResourceOptions(depends_on=[droplet]), triggers=[droplet.ipv4_address], ) # Output the IP address so that we know how to connect to MySQL pulumi.export('ip_address', ip_address) pulumi.export("setup_output", init_mysql.stderr)

    Here's what each part of this program does:

    • DigitalOcean Droplet: We're creating a new virtual machine where our database will run. In this example, we're using a pre-configured image "mysql-8-x64", which has MySQL pre-installed.

    • MySQL Setup: We're running a remote command that will create a new MySQL database ai_training and a user that you'll use to access the database. The database will hold your AI training datasets. Be sure to replace 'your_user' and 'your_password' with the desired credentials.

    • Output Export: After the deployment, Pulumi will output the IP address of your droplet and any standard error from running the initialization command. With the IP address, you can connect to the MySQL server using a MySQL client.

    To run this program:

    1. Save the code in a file named __main__.py.
    2. Run pulumi up from the command line in the same directory as your program.
    3. Confirm the preview looks correct and select yes to deploy the resources.

    After the Pulumi program finishes executing, you'll have a MySQL database running on a DigitalOcean droplet, ready to store your AI training datasets. Remember to secure your database and manage access appropriately, especially if you are dealing with sensitive data.