1. Persistent Model Training Data with AWS EBS


    Creating persistent storage for training machine learning models is crucial, as it ensures your data is available even after machine instances are terminated or fail. Amazon Elastic Block Store (AWS EBS) provides the capability of attaching persistent storage volumes to AWS EC2 instances that you can use to store and retrieve any amount of data. These volumes are particularly useful for storing input datasets, training outputs, models, and logs, which you want to persist and possibly share between different EC2 instances.

    Here's how to use Pulumi to set up an AWS EBS volume that can be used for persistent model training data:

    1. Create an EBS volume in the desired availability zone and with the necessary size and IOPS (Input/Output Operations Per Second) configuration for your workload.
    2. Launch an EC2 instance that will be used for your model training.
    3. Attach the EBS volume to the EC2 instance where the model training will occur.

    Below is a Pulumi program in Python that creates an EBS volume and attaches it to an EC2 instance. This program assumes that you have AWS access credentials configured.

    import pulumi import pulumi_aws as aws # Creating an EBS volume with the desired size and performance characteristics. ebs_volume = aws.ebs.Volume("modelTrainingVolume", # Specify the size of the volume (in GiBs). Adjust this size according to your needs. size=50, # Options are "standard", "io1", "io2", "gp2", "gp3", "st1", "sc1". Choose based on performance and price requirements type="gp3", # Specify the availability zone in which to create the volume. availability_zone="us-west-2a", # If you need high IOPS, uncomment and set the value below. # iops=4000, # Following is optional and encrypts the volume with a specified KMS key. # kms_key_id="your-kms-key-id", encrypted=True, tags={ "Name": "model-training-volume", }) # Launch an EC2 instance to perform the model training. training_instance = aws.ec2.Instance("trainingInstance", # The AMI ID changes depending on the region. Use the correct one for your desired region ami="ami-0c55b159cbfafe1f0", instance_type="t2.micro", tags={ "Name": "training-instance", }) # Attach the EBS volume to the EC2 instance. volume_attachment = aws.ec2.VolumeAttachment("modelTrainingVolumeAttachment", instance_id=training_instance.id, volume_id=ebs_volume.id, device_name="/dev/sdh") # The device name can vary based on the instance type and OS. # Export the IDs of the resources so they can be easily referenced as needed. pulumi.export("ebs_volume_id", ebs_volume.id) pulumi.export("training_instance_id", training_instance.id)

    In this program, we start by creating an EBS volume with aws.ebs.Volume, specifying its size, type, availability zone, and tags. Adjust these settings based on your storage amount and performance needs. You can also encrypt your volume by enabling the encrypted attribute and optionally specifying a KMS key ID.

    We then create an EC2 instance with aws.ec2.Instance, where we provide the AMI ID and the instance type. Be sure to replace the ami with the correct Amazon Machine Image ID for your desired AWS region.

    Lastly, we attach the EBS volume to the instance with aws.ec2.VolumeAttachment. Notice that we specify the device name where the volume will be attached, which in the case of AWS EC2, is often "/dev/sdh". After this process, the EBS volume will be available to the EC2 instance, and you can start using it for your data persistence needs in model training.

    To apply this Pulumi program, simply run pulumi up through the Pulumi CLI and confirm the operations. Once your infrastructure is provisioned, you can SSH into the EC2 instance and mount your EBS volume to use it for model training data storage.