Are you tired of juggling multiple Kubernetes clusters, desperately trying to match your ML/AI workloads to the right resources? A smart K8s fleet manager like the Elotl Nova policy-driven multi-cluster orchestrator simplifies the use of multiple clusters by presenting a single K8s endpoint for workload submission and by choosing a target cluster for the workload based on placement policies and candidate cluster available capacity.  Nova is autoscaler-aware, detecting if workload clusters are running either the K8s cluster autoscaler or the Elotl Luna intelligent cluster autoscaler.

In this blog, we examine how Nova policies combined with its autoscaler-awareness can be used to achieve a variety of "right place, right size" outcomes for several common ML/AI GPU workload scenarios. When Nova and Luna team up you can:

1. Reduce the latency of critical ML/AI workloads by scheduling on available GPU compute.
2. Reduce your bill by directing experimental jobs to sunk-cost clusters.
3. Reduce your costs via policies that select GPUs with the desired price/performance.

Kubernetes (K8s) workloads are given exclusive access to their allocated GPUs by default.  With NVIDIA GPU time-slicing, GPUs can be shared among K8s workloads by interleaving their GPU use.  For cloud K8s clusters running non-demanding GPU workloads, configuring NVIDIA GPU time-slicing can significantly reduce GPU costs. Note that NVIDIA GPU time-slicing is intended for non-production test/dev workloads, as it does not enforce memory and fault isolation.

Using NVIDIA GPU time-slicing in a cloud Kubernetes cluster with a cluster autoscaler (CA) that is aware of the time-slicing configuration can significantly reduce costs. A time-slice aware “smart” CA prevents initial over-allocation of instances and optimizes instance selection, and reduces the risk of exceeding quotas and capacity limits.  Also, on GKE, where GPU time-slicing is expected to be configured at the control plane level, a smart CA facilitates using time-slicing on GPU resources that are dynamically allocated.

How do I efficiently run my AI or Machine Learning (ML) workloads in my Kubernetes clusters?

Operating Kubernetes clusters with GPU compute manually presents several challenges, particularly in the allocation and management of GPU resources. One significant pain point is the potential for wasted spend, as manually allocated GPUs may remain idle during periods of low workload. In dynamic or bursty clusters, predicting the optimal GPU requirements becomes challenging, leading to suboptimal resource utilization and increased costs. Additionally, manual allocation necessitates constant monitoring of GPU availability, requiring administrators be aware of the GPU availability in clusters spread across different zones or regions. Once the GPU requirements are determined for a given workload, the administrator needs to manually add nodes when demand surges and remove them during periods of inactivity.

There are many GPU types, each with different capabilities, running on different nodes types. The combination of these three factors makes manual GPU nodes management increasingly convoluted. Different workloads may require specific GPU models, leading to complexities in node allocation. Manually ensuring the correct GPU nodes for diverse workloads becomes a cumbersome task, especially when dealing with multiple applications with varying GPU preferences. This adds another layer of operational overhead, demanding detailed knowledge of GPU types, and again availability, and continuous adjustments to meet workload demands.

Luna, an intelligent node autoscaler, addresses these pain points by automating GPU node allocation based on workload demands. Luna is aware of GPU availability, as such, it can dynamically choose and allocate needed GPU nodes, eliminating the need for manual intervention. This optimizes resource utilization and reduces wasted spend by scaling GPU resources in line with the workload. Moreover, Luna can allocate specific nodes as defined by the workload requirements, ensuring precise resource allocation tailored to the application's needs. This makes Luna perfectly suited for the most complex compute jobs like AI and ML workloads.

Furthermore, Luna's core functionality includes the automatic allocation of alternative GPU nodes in cases where preferred GPUs are unavailable, bolstering its flexibility and resilience. This ensures that workloads with specific GPU preferences can seamlessly transition to available alternatives, maintaining uninterrupted operation. Controlled through annotations within the workload, users can specify cloud instance types to use or avoid, either by instance family or via regular expressions, along with desired GPU SKUs. This capability enables dynamic allocation based on GPU availability and workload demands, simplifying cluster management and promoting efficient scaling and resource utilization without the need for constant manual adjustments.

Lastly, the advantages of Luna extend beyond resource optimization and workload adaptability in a single specific cloud. When organizations leverage various cloud providers, flexibility is paramount. An intelligent autoscaler designed to support GPU management within multiple cloud providers empowers users with the freedom to choose the most suitable cloud platform for their specific needs. With Luna enterprises are not locked into a single cloud provider, offering them the agility to transition workloads seamlessly between different cloud environments based on cost-effectiveness, performance, or specific features. Currently Luna supports four cloud providers: Amazon AWS with EKS, Google Cloud with GKE, Microsoft Azure with AKS, and Oracle Cloud Infrastructure with OKE. By providing a unified and agnostic approach to GPU resource management, Luna becomes a strategic asset, enabling organizations to harness the benefits of diverse cloud ecosystems without compromising efficiency or incurring cloud vendor lock-in.

In summary, manually managing GPU compute in Kubernetes clusters poses challenges related to wasted spend, manual addition, monitoring, and removal of nodes. Luna addresses these pain points by:

•     Streamlining GPU node allocation according to workload demands
•     Optimizing resource utilization by dynamically choosing and allocating nodes
•     Adapting to fluctuations in GPU availability seamlessly
•     Unify operations over multiple clusters and cloud providers: Amazon EKS, Google GKE, Azure AKS, and Oracle OKE

Luna simplifies cluster node management, reduces operational overhead, and ensures efficient GPU resource utilization.

To delve deeper into Luna's powerful features and capabilities, explore the Luna product page for details. For step-by-step guidance, consult our Documentation. Ready to experience the seamless management of GPU workloads firsthand? Try Luna today with our free trial and witness the efficiency and flexibility it brings to your cloud environments.

Author:
Justin Willoughby (Principal Solutions Architect, Elotl)

Contributors:
Henry Precheur (Senior Staff Engineer, Elotl)
Anne Holler (Chief Scientist, Elotl)