Nvidia DGX Cloud is designed to accelerate AI and HPC workloads with its cutting-edge GPU technology and scalable multi-cloud architecture. A key component of its Kubernetes orchestration is Kyverno, a Kubernetes-native policy engine. Kyverno plays a critical role in ensuring that workloads in DGX environments adhere to best practices for security and compliance.

Enforcing Kubernetes Pod Security Standards with Kyverno

In the DGX Kubernetes environment, Kyverno is used to enforce Kubernetes Pod Security Standards (PSS). These standards define baseline, restricted, and privileged profiles to help maintain a secure posture for Kubernetes workloads. By leveraging Kyverno, NVIDIA ensures that:

• Workloads adhere to Pod Security Standards appropriate for their security requirements.
• Policies are enforced consistently across clusters, reducing the likelihood of misconfigurations or security vulnerabilities.
• Automated security checks occur seamlessly during the deployment process.

 

Handling Exclusions and Exceptions

As outlined in Nvidia’s DGX BasePOD Deployment Guide, Kyverno includes preconfigured exclusions and exceptions. These are tailored to accommodate specific features or operators installed in the DGX Kubernetes environment. For example:

• Certain operator workloads that require elevated privileges are exempt from baseline or restricted profiles.
• Configured exclusions ensure compatibility with GPU operators and other NVIDIA-specific components, without compromising overall security.

This approach balances security enforcement with the flexibility required for GPU-accelerated workloads and Kubernetes operators.

Streamlined Operations and Compliance

Kyverno’s integration within DGX Kubernetes simplifies policy management, enabling platform teams to:

• Automate compliance with Pod Security Standards.
• Ensure consistent security configurations across multi-cloud environments.
• Reduce manual overhead by automating exception handling for specific workloads or operators.

The recent updates in Nvidia’s release notes highlight the importance of these features, emphasizing streamlined operations and improved security posture for AI and HPC workloads.

Why Kyverno?

Kyverno is a natural fit for DGX Cloud due to its Kubernetes-native design, which allows policies to be defined and enforced using YAML configurations familiar to Kubernetes operators. This reduces the learning curve and operational complexity, enabling platform teams to focus on scaling AI workloads securely and efficiently.

What Else Could Kyverno Be Used For in NVIDIA DGX Cloud?

In addition to enforcing Kubernetes Pod Security Standards, Kyverno’s flexibility and Kubernetes-native design make it a powerful tool for addressing other governance and operational needs in the Nvidia DGX Cloud. Here are some potential use cases where Kyverno can further enhance the DGX platform:

1. Validating GPU Resource Requests

Kyverno can ensure that workloads requesting GPU resources are properly configured. For instance, the Nvidia GPU validation policy can verify that:

• Workloads include appropriate node selectors for GPU-enabled nodes.
• Resource requests and limits for GPUs are correctly defined. This prevents misconfigurations that could lead to performance bottlenecks or underutilization of GPU resources.

2. Ensuring Namespace-Specific Policies

Kyverno can enforce policies based on namespaces, ensuring that different teams or applications using the DGX Cloud adhere to specific guidelines. For example:

• Restricting access to GPU resources for certain namespaces.
• Enforcing security policies tailored to sensitive workloads or experimental environments.
• Orchestrating network policies and micro-segmentation for workloads

3. Audit and Compliance Reporting

Kyverno’s policies can be extended to include audit and compliance checks for GPU workloads, such as:

• Ensuring workloads meet specific security benchmarks.
• Verifying that deployed configurations are aligned with organizational policies.

This is particularly useful for regulated industries leveraging DGX Cloud for AI workloads, such as healthcare or finance.

4. Cost Optimization

Kyverno can enforce policies that ensure optimal resource allocation and cost control. For example:

• Preventing over-provisioning of GPUs for workloads with minimal requirements.
• Enforcing limits on idle resources in GPU-enabled nodes.

5. Governance for Operators and Add-Ons

Kyverno can manage policies for operators and add-ons running in DGX Kubernetes environments, ensuring they are configured securely and do not interfere with core operations.

Conclusion

With Kyverno enforcing Kubernetes Pod Security Standards, Nvidia DGX Cloud delivers a secure, compliant, and optimized environment for AI and HPC workloads. Its ability to handle preconfigured exceptions ensures flexibility while maintaining a robust security posture. By leveraging Kyverno for these additional use cases, Nvidia DGX Cloud users can achieve greater operational efficiency, enhanced security, and streamlined compliance. The flexibility of Kyverno’s policy engine allows platform teams to continuously evolve and adapt their governance strategies, ensuring the DGX platform remains a robust and secure foundation for cutting-edge AI workloads. Additionally, Nirmata Control Hub can automate security, governance, and compliance of the Nvidia DGX Cloud.