Provisioning Short-Lived Certificates for AI Model Training Clusters

Question

Pulumi · Accepted Answer

To provision short-lived certificates for AI model training clusters, we will use a managed certificate authority (CA) service that can issue certificates with a shorter lifespan. These certificates can be used to ensure secure communications between the components of your AI model training cluster.

For this task, I'll demonstrate how to provision short-lived certificates using Google Cloud's Certificate Authority Service (CAS) with Pulumi's GCP provider. The service enables you to create and manage your private CAs and issue certificates for your internal resources.

Below is a Pulumi program written in Python that will:

1. Create a certificate authority (CA) pool which is a container for CAs.
2. Create a certificate authority (CA) which will issue the certificates.
3. Set up a certificate template that specifies the configurations for certificates issued by the CA, including the maximum lifetime of the certificates to achieve the short-lived aspect.

Google CAS allows you to specify the maximum lifetime of a certificate when issuing it. For this example, we will set it to a short duration that you might use for a cluster that only needs certificates valid for the duration of a training job.

Please note that in a real-world scenario, you'd also need to handle the distribution of these certificates to your cluster nodes and the rotation of them as they expire.

```python
import pulumi
import pulumi_gcp as gcp

# Create a new CA pool.
ca_pool = gcp.certificateauthority.CertificateAuthorityPool("ai-model-training-ca-pool",
    location="us-central1",
)

# Create a new Certificate Authority (CA) in the CA pool.
certificate_authority = gcp.certificateauthority.CertificateAuthority("ai-model-training-ca",
    location="us-central1",
    pool=ca_pool.name,
    key_spec=gcp.certificateauthority.CertificateAuthorityKeySpecArgs(
        algorithm="RSA_PSS_2048_SHA256",
    ),
    lifecycle_rule=gcp.certificateauthority.CertificateAuthorityLifecycleRuleArgs(
        # Automatically delete the CA after it has been DISABLED for 30 days.
        action="DELETE",
        condition=gcp.certificateauthority.CertificateAuthorityLifecycleRuleConditionArgs(
            days_since_disabled=30,
        ),
    ),
    config=gcp.certificateauthority.CertificateAuthorityConfigArgs(
        subject_config=gcp.certificateauthority.CertificateAuthorityConfigSubjectConfigArgs(
            subject=gcp.certificateauthority.CertificateAuthorityConfigSubjectConfigSubjectArgs(
                common_name="ai-model-training-cluster",
            ),
        ),
        x509_config=gcp.certificateauthority.CertificateAuthorityConfigX509ConfigArgs(
            key_usage=gcp.certificateauthority.CertificateAuthorityConfigX509ConfigKeyUsageArgs(
                base_key_usage=gcp.certificateauthority.CertificateAuthorityConfigX509ConfigKeyUsageBaseKeyUsageArgs(
                    digital_signature=True,
                    key_encipherment=True,
                ),
                extended_key_usage=gcp.certificateauthority.CertificateAuthorityConfigX509ConfigKeyUsageExtendedKeyUsageArgs(
                    server_auth=True,
                    client_auth=True,
                ),
            ),
        ),
    ),
)

# Create a certificate template with the desired maximum lifetime for the certificates.
certificate_template = gcp.certificateauthority.CertificateTemplate("ai-model-training-template",
    location="us-central1",
    predefined_values=gcp.certificateauthority.CertificateTemplatePredefinedValuesArgs(
        # Set the maximum lifetime of the certificates.
        # This duration should be aligned with your training job durations.
        # Example: "86400s" represents a 24-hour lifetime.
        validity_period="86400s",  # 24 hours
    ),
)

# Export some information about the created resources.
pulumi.export("ca_pool_id", ca_pool.id)
pulumi.export("certificate_authority_name", certificate_authority.name)
pulumi.export("certificate_template_name", certificate_template.name)
```

In this program:

- We create a CA pool, a container for one or more CAs. Setting the location to `us-central1` as an example, which you may adjust based on your region.
- Then, we create a CA within the CA pool. We specify an RSA 2048-bit key (`RSA_PSS_2048_SHA256`) and configure the subject of the CA. The `lifecycle_rule` ensures that the CA is automatically deleted after a certain period once it is disabled, helping to manage resources that are no longer in use.
- We define a certificate template (`ai-model-training-template`) to issue certificates with a maximum lifetime of 24 hours (`86400s`). This is an important parameter to ensure the short-lived nature of certificates.

Remember to add adequate permissions and enable the appropriate APIs in your Google Cloud Project to use the Certificate Authority Service.

This program sets the stage for issuing and deploying certificates for your AI model training clusters. You can now implement additional logic to issue certificates using this CA and template, distribute them to cluster nodes, and replace them as they expire or the cluster scales.