10 Things I Learned About Running GenAI on Kubernetes at KubeCon 2024

KubeCon North America 2024 was an eye-opening experience for anyone working with Kubernetes and Generative AI/ML workloads. By attending sessions and talking to experts from different companies, I gained valuable insights into the real-world challenges and solutions for running AI/ML on Kubernetes. Here are my top 10 takeaways, based on what I learned from the sessions and conversations. These points highlight both the common struggles companies face and the creative solutions they’ve found.

1. How to handle GPU scarcity in cloud environments?

Challenge: GPUs are in high demand, with limited availability and competition for on-demand instances. Long-term contracts reduce flexibility. Solution: Use AWS Capacity Blocks to reserve GPU capacity, though they come with constraints. Consider hybrid approaches where on-prem GPUs supplement cloud resources.

2. What strategies can optimize GPU resource utilization in Kubernetes?

Challenge: Inefficient GPU sharing leads to underutilized resources in multi-tenant environments. Solution: Leverage multi-instance GPUs (MIGs) and Kubernetes GPU time-slicing features for secure and efficient multi-tenancy. Use tools like Ray for cross-cloud GPU optimization.

3. How to reduce the operational complexity of managing GPU clusters?

Challenge: Managing GPU-specific dependencies like drivers, plugins, and monitoring tools increases risks. Solution: Automate the setup and management using tools like NVIDIA Data Center GPU Manager (DCGM) for monitoring and KubeVirt for GPU virtualization.

4. How to ensure high availability and fault tolerance during AI/ML training jobs?

Challenge: Faults in one pod can disrupt the entire training job due to Gang Scheduling. Solution: Use checkpointing to save intermediate progress and design workflows to recover gracefully from failures, minimizing resource wastage.

5. How to improve model initialization times in large-scale AI deployments?

Challenge: Repeatedly downloading large models increases startup time and disk usage. Solution: Use KServe’s ModelCars to minimize initialization by lazy-loading model components on demand and avoiding redundant downloads.

6. How to address hardware and software failures in GPU nodes?

Challenge: Failures such as overheating, ECC errors, and filesystem corruption disrupt operations. Solution: Implement real-time monitoring with tools like Prometheus and AlertManager integrated with DCGM logs to detect and escalate issues early.

7. What are the best practices for training large language models on Kubernetes?

Challenge: Distributed training is resource-intensive and prone to failures. Solution: Optimize models before deployment through pruning and quantization. Ensure synchronized execution with KubeFlow and monitor performance using Kubeflow Pipelines.

8. How to reduce power consumption for large AI/ML workloads in on-prem data centers?

Challenge: AI/ML workloads are power-intensive. Solution: Shift to fine-tuning pre-trained models instead of training from scratch. Optimize workload scheduling to increase GPU utilization efficiency and reduce idle times.

9. How to manage scalability challenges with extremely large models?

Challenge: Scaling workloads with large models increases latency and resource consumption. Solution: Use OCI-compliant image registries for efficient storage and distribution of models, combined with prefetching and lazy loading for scalability.

10. How to improve Kubernetes’ native support for AI/ML workloads?

Challenge: Kubernetes lacks built-in features for fault-tolerant GPU scheduling and multi-cluster management. Solution: Advocate for enhanced open-source collaboration with GPU vendors, integrate multi-cluster scheduling frameworks, and adopt solutions like KEDA for auto-scaling AI/ML workloads.

Conclusion

Generative AI/ML workloads on Kubernetes are both exciting and challenging, offering immense potential for innovation while exposing the need for better tools and strategies. From addressing GPU scarcity and optimizing resource usage to tackling scalability and fault tolerance, companies are actively experimenting with solutions to overcome these hurdles. However, this is still an evolving space. Many organizations are exploring new approaches and technologies to make Kubernetes an even better fit for AI/ML workloads. As the ecosystem matures and open-source contributions grow, we can expect to see more robust and streamlined solutions for running AI at scale. For now, these challenges highlight the importance of collaboration, learning, and innovation within the Kubernetes community.