Efficient Model Checkpoint Storage on AWS EBS

Question

Pulumi · Accepted Answer

To efficiently store model checkpoints on AWS EBS (Elastic Block Store), we can use Pulumi to provision an EBS Volume that can be attached to an EC2 instance where your machine learning model is running. EBS volumes are ideal for use cases that require persistent storage with consistent and low-latency performance. They can be attached or detached from EC2 instances as needed and are perfect for scenarios like storing checkpoints during the training of machine learning models, where you might require the ability to persist data beyond the lifetime of a single EC2 instance.

Here's how you can create an EBS volume and manage its lifecycle including creating snapshots for backup purposes:

1. Create an EBS Volume with desired configurations like size, type, and IOPS (if using provisioned IOPS SSD).
2. Attach the created EBS Volume to an EC2 instance where your model is training.
3. (Optional) Create snapshots of the EBS Volume at regular intervals for backup.

The following Pulumi program in Python accomplishes these steps:

```python
import pulumi
import pulumi_aws as aws

# Create an EBS Volume
ebs_volume = aws.ebs.Volume("ModelCheckpointVolume",
    size=50,  # Size in GiB
    type="gp3",  # General Purpose SSD
    iops=3000,  # Only used if you choose provisioned IOPS SSD
    tags={
        "Name": "ModelCheckpointVolume"
    }
)

# Provide the ID of the EC2 instance to which you want to attach this volume
# Replace `instance_id` with your actual EC2 instance ID
volume_attachment = aws.ec2.VolumeAttachment("ModelCheckpointVolumeAttachment",
    instance_id="i-xxxxxxxxxxxxxxxx",  # Replace with your instance ID 
    volume_id=ebs_volume.id,
    device_name="/dev/sdh"
)

# The following section is for snapshot automation which can be
# triggered by a Lambda function or manually via Pulumi
import datetime

# Take a snapshot of the EBS Volume for backup
model_checkpoint_snapshot = aws.ebs.Snapshot("ModelCheckpointSnapshot",
    volume_id=ebs_volume.id,
    description="Snapshot of the model checkpoint EBS Volume",
    # Only include the next line if you want to tag the time the snapshot was initiated
    tags={"CreatedOn": datetime.datetime.now().isoformat()}
)

# Export the EBS Volume ID and Snapshot ID for easy access if needed.
pulumi.export("ebs_volume_id", ebs_volume.id)
pulumi.export("ebs_snapshot_id", model_checkpoint_snapshot.id)
```

In the program above:

- We first create an EBS Volume `ModelCheckpointVolume` which will be used to store our model checkpoints. You can adjust the size, type, and IOPS based on your model's requirements.
- Next, we attach this volume to an EC2 instance using the `VolumeAttachment` resource. Make sure to replace `i-xxxxxxxxxxxxxxxx` with your actual EC2 instance ID and change `/dev/sdh` to a device name that's appropriate for your instance type and OS.
- Optionally, we can create a snapshot of this volume using the `Snapshot` resource. This can be used to back up your checkpoints periodically. The creation time can be tagged for easy reference.

Remember to configure AWS credentials or have the AWS CLI installed and configured before running the program so that Pulumi can authenticate with your AWS account.

By incorporating the snapshot step, you have a simple backup system for your data. You can create snapshots periodically during or after model training to ensure that you have consistent restore points. These snapshots could help you recover an earlier state of your model if something goes wrong with your training process at a later stage.