1. GCP Storage as a Backend for AI Model Checkpoints


    When training machine learning models, it can be crucial to save checkpoints at regular intervals. These checkpoints allow you to resume training from a particular state if the process is interrupted, which is especially useful when working with large datasets or training complex models on limited compute resources.

    To use Google Cloud Storage (GCS) as a backend for AI model checkpoints, you need to setup a GCS bucket to store the checkpoint files. Here is how you can do this using Pulumi with the GCP provider.

    1. Setting up a Bucket: The gcp.storage.Bucket resource creates a new bucket in GCS. This bucket will serve as the container for your AI model checkpoints.

    2. Setting Bucket Properties: You can define specific properties for your bucket, such as versioning to keep a history of changes or lifecycle rules to manage object lifespans within the bucket.

    3. Creating Bucket Objects: The gcp.storage.BucketObject resource is used for adding objects (files) to your bucket. In the context of AI checkpoints, each checkpoint can be uploaded as an object.

    4. Access Control: Depending on your requirements, you may need to set access control for the bucket or individual objects using resources like gcp.storage.BucketACL or gcp.storage.ObjectAccessControl.

    Here is a Python program using Pulumi to set up a bucket and a dummy object to emulate saving a checkpoint:

    import pulumi import pulumi_gcp as gcp # Assuming you've configured GCP for Pulumi inside your environment # and have authorized Pulumi for interacting with your GCP account. # Create a GCP Storage Bucket to hold AI Model Checkpoints. ai_checkpoint_bucket = gcp.storage.Bucket('ai-checkpoint-bucket', location='US', # You can choose a region that is geographically close to your computations. storage_class='STANDARD', # 'NEARLINE', 'COLDLINE' or 'ARCHIVE' for less frequent access. versioning=gcp.storage.BucketVersioningArgs( enabled=True # To keep history of your checkpoints ) ) # If needed, set the Bucket Access Control List (ACL) for broader access configurations. bucket_acl = gcp.storage.BucketACL('ai-checkpoint-bucket-acl', bucket=ai_checkpoint_bucket.name, predefined_acl='private' # Use 'public-read,' 'public-read-write' if necessary ) # Example of adding a dummy checkpoint file to the bucket. # In practice, you would have your training script upload checkpoints instead. ai_checkpoint_object = gcp.storage.BucketObject('ai-checkpoint-object', bucket=ai_checkpoint_bucket.name, name='checkpoint_1.pkl', source=pulumi.FileAsset('path_to_checkpoint/checkpoint_1.pkl') # Local file path ) # Export the bucket and object URLs for external access if necessary. bucket_url = pulumi.Output.concat("gs://", ai_checkpoint_bucket.url) object_url = pulumi.Output.concat("gs://", ai_checkpoint_bucket.url, "/", ai_checkpoint_object.name) pulumi.export('ai_checkpoint_bucket_url', bucket_url) pulumi.export('ai_checkpoint_object_url', object_url)

    This program performs the following steps:

    • It creates a new GCS bucket specifically for storing AI model checkpoints.
    • It sets the bucket to use the standard storage class, which is cost-effective for frequently accessed data. You may change the storage class to 'NEARLINE', 'COLDLINE', or 'ARCHIVE' if you access your checkpoints less frequently, which could reduce costs.
    • It enables versioning on the bucket, which ensures that you keep a history of each checkpoint. This could be beneficial if you need to rollback to a previous version of a checkpoint.
    • It sets the access permissions to 'private', which means only authorized users can access the checkpoints. Change the predefined ACL if you require different access permissions.
    • It simulates the uploading of a dummy checkpoint file from a local path. In a real-world scenario, you would have your training script upload each checkpoint after it's created.
    • It exports the URLs of your bucket and the dummy object so you can access them if needed. Use these URLs to retrieve your checkpoints for further analysis or resuming training.

    To run this Pulumi program, save the code in a file with a .py extension, like gcp_storage_setup.py, and execute it using the Pulumi CLI:

    pulumi up

    This command will prompt you to review the proposed infrastructure changes and approve them before any resources are created on GCP. Once approved, Pulumi will provision the resources, and the bucket for your AI checkpoints will be ready to use.