1. Secure Model Checkpoint Storage with GCP Buckets


    To create a secure storage solution for model checkpoints using Google Cloud Platform (GCP) Buckets with Pulumi, we will create a GCP Storage Bucket with proper access control policies to ensure the data is kept secure. We will also enable versioning for the bucket to preserve and track every version of the model checkpoints.

    Below is a step-by-step Pulumi Python program to achieve this:

    1. Create a GCP Storage Bucket: We define a bucket in which the model checkpoints will be stored.

    2. Enable Versioning: To keep a history of all the model checkpoints, we enable versioning on the bucket.

    3. Define Access Control: We set access control policies on the bucket to restrict who can access the bucket and its contents. For this example, we will keep it simple by setting the bucket to private, meaning only authenticated users with specific permissions can access it.

    4. Encryption: We apply encryption to the bucket to secure the data at rest. Google Cloud Storage always encrypts your data, but you can supply a Cloud KMS key if you want to manage encryption yourself.

    5. Bucket Lifecycle Rules: We can add rules to manage the lifecycle of the objects within the bucket, such as automatically deleting old checkpoints after a certain period of time.

    Let's get into the code for creating a secure model checkpoint storage bucket:

    import pulumi import pulumi_gcp as gcp # Replace 'my-model-checkpoints' with a unique name for your bucket checkpoint_bucket = gcp.storage.Bucket('my-model-checkpoints', # Choose the appropriate location for your bucket location='US', # Enable versioning to keep a history of the model checkpoints versioning={ 'enabled': True }, # Set uniform bucket-level access to strengthen access control uniform_bucket_level_access=True, # Bucket will be private, so only authenticated users with designated roles can access it predefined_acl='private', # Define encryption with a management key if needed # encryption={ # 'kms_key_name': 'YOUR_KMS_KEY_NAME' # }, # Sample lifecycle rules for automatically managing objects lifecycle_rules=[ { 'action': { 'type': 'Delete', }, 'condition': { 'age': 365, # Deletes objects older than 365 days 'withState': 'LIVE', # Applies to non-archived items only }, }, ], ) # Export the bucket name and URL as stack outputs to easily identify and access the bucket pulumi.export('bucket_name', checkpoint_bucket.name) pulumi.export('bucket_url', checkpoint_bucket.self_link)


    • gcp.storage.Bucket: This is the Pulumi class used for creating a Google Cloud Storage Bucket.
    • location: Configures the physical location where the bucket data will be stored. Change this to a location that is close to where the users of the bucket are located.
    • versioning: This setting helps in retaining historical versions of the objects in the bucket.
    • uniform_bucket_level_access: Setting this to True disables object-level ACLs and applies bucket-level access policies uniformly.
    • predefined_acl: The predefined access control policy for the bucket. Setting this to 'private' means only explicitly specified users and groups can access the bucket.
    • lifecycle_rules: If desired, you can manage the lifecycle of objects within the bucket. Here, an example is provided to delete objects older than 365 days.

    Remember, if you have a specific Cloud KMS key for encryption, you can uncomment and replace 'YOUR_KMS_KEY_NAME' with your KMS key name to manage the encryption.

    Once you have deployed this code with Pulumi, it creates a secure storage bucket for your model checkpoints, which are not publicly accessible, version-controlled, and lifecycle managed. To deploy the Pulumi program, you need to have the Pulumi CLI installed and have authenticated with GCP.

    This Pulumi program assumes that you are comfortable with Python code and that you have prepared the GCP environment for this deployment (e.g., you have the necessary permissions to create and manage storage buckets and possibly KMS keys).