Backup and Restore for AI Training VMs

Question

Pulumi · Accepted Answer

In the context of an AI training environment that typically requires significant computational resources and storage capacity, ensuring that the underlying virtual machines (VMs) and their data are properly backed up and can be restored is crucial. This can help mitigate risks like data loss due to hardware failure, accidental deletion, or other disasters.

For the purpose of this explanation and code, I'll be using AWS as the cloud provider, although similar principles would apply to Azure, GCP, or other cloud providers. AWS offers several services that can help with the backup and restoration process of your AI training VMs:

- **Amazon EC2**: Elastic Compute Cloud (EC2) is the AWS service providing scalable virtual private servers. EC2 instances can be used to run the machine learning models.
- **Amazon EBS**: Elastic Block Store (EBS) provides block level storage volumes for use with EC2 instances. EBS volumes store the data used by the VMs and can be backed up using snapshots.
- **AWS Backup**: This is a fully managed backup service that makes it easy to centralize and automate the backup of data across AWS services.

To create backups of your VMs, you can create EBS snapshots, and for more comprehensive backup strategy, you can use AWS Backup to manage and automate backups not just of EBS volumes but also EC2 instances themselves.

Below is a Pulumi program in Python that sets up an EC2 instance, an EBS volume attached to it, and a backup policy to regularly backup the EBS volume. In case you need to restore the environment, you would create a new EBS volume from a snapshot and attach it to an EC2 instance.

```python
import pulumi
import pulumi_aws as aws

# Create an EC2 instance for AI training
training_instance = aws.ec2.Instance("aiTrainingInstance",
    instance_type="t2.medium", # Replace with the desired instance type
    ami="ami-0abcdef1234567890", # Replace with the desired Amazon Machine Image ID
    tags={
        "Name": "AI Training Instance",
    },
)

# Create an EBS volume and attach it to the instance
ebs_volume = aws.ebs.Volume("aiTrainingEbsVolume",
    size=50, # Size in Gibibytes. Adjust as needed.
    availability_zone=training_instance.availability_zone,
    tags={
        "Name": "AI Training Data Volume",
    },
)

# Attach the EBS volume to the EC2 instance
volume_attachment = aws.ec2.VolumeAttachment("aiTrainingVolumeAttachment",
    instance_id=training_instance.id,
    volume_id=ebs_volume.id,
    device_name="/dev/sdh", # The device name may vary based on the instance type and OS.
)

# Define a backup vault to store the backups
backup_vault = aws.backup.Vault("aiTrainingBackupVault", {})

# Define a backup plan
backup_plan = aws.backup.Plan("aiTrainingBackupPlan",
    backup_vault_name=backup_vault.name,
    rules=[
        aws.backup.PlanRuleArgs(
            rule_name="Daily",
            target_vault_name=backup_vault.name,
            schedule="cron(0 0 * * ? *)",  # Daily backups
            start_window=120,  # The backup can start up to 2 hours after the scheduled time
            completion_window=360,  # The backup needs to complete within 6 hours
            lifecycle=aws.backup.PlanRuleLifecycleArgs(
                delete_after=4320,  # Retention: 180 days (4320 hours), adjust as needed.
            ),
            recovery_point_tags={
                "Name": "AI Training Data Backup",
            },
            copy_actions=[
                aws.backup.PlanRuleCopyActionArgs(
                    destination_vault_arn=backup_vault.arn,
                    lifecycle=aws.backup.PlanRuleCopyActionLifecycleArgs(
                        copy_after=15,  # Copy the backup to another vault after 15 days
                    ),
                ),
            ],
        ),
    ],
)

# Create a backup selection to specify which resources to backup
backup_selection = aws.backup.Selection("aiTrainingBackupSelection",
    plan_id=backup_plan.id,
    name="ai-training-selection",
    resources=[ebs_volume.arn],
)

# Export the URLs which can be used to access the backups
pulumi.export('backup_vault_arn', backup_vault.arn)
pulumi.export('backup_plan_id', backup_plan.id)
```

This program creates an EC2 instance with an attached EBS volume. Then it establishes a backup plan that makes daily backups of the volume. These backups are retained for 180 days before being deleted. Adjust the frequencies, window timings, and retention policies according to your requirements.

To restore from a backup, you would typically perform actions based on the AWS Backup service's "Restore" feature, programmatically or through the AWS console. The process involves selecting the recovery points and initiating the restoration, which will create a new EBS volume based on the snapshot, which can then be attached to an EC2 instance.