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.

In this brief summary blog, we delve into the intriguing realm of GPU cost savings in the cloud through the use of Luna, an Intelligent Autoscaler. If you're passionate about harnessing the power of Deep Learning (DL) while optimizing expenses, this summary is for you. Join us as we explore how innovative technologies are revolutionizing the landscape of resource management in the realm of Deep Learning. Let's embark on a journey where efficiency meets intelligence, promising both technical insights and a practical solution.

Deep Learning has and continues to transform many industries such as AI, Healthcare, Finance, Retail, E-commerce, and many others. Some of the challenges with DL include its high cost and operational overhead:

1. Compute Costs: Deep learning models require significant computational resources, which lead to high costs, especially for complex or large-scale projects. This is even more true when the compute remains provisioned when it’s not needed.
2. Instance Management: Managing cloud instances for training, inference, and experimentation creates operational overhead. This includes provisioning and configuring virtual machines, monitoring resource usage, and optimizing instance types for performance and cost efficiency.
3. Infrastructure Scaling: Scaling deep learning workloads in the cloud involves dynamically adjusting compute resources to meet demand. This requires optimizing resource allocation to minimize costs while ensuring sufficient capacity.

Open-source platforms like Ray and Ludwig have broadened DL accessibility, yet DL model’s intensive GPU resource demands present financial hurdles. Addressing this, Elotl Luna emerges as a solution, streamlining compute for Kubernetes clusters without the need for manual scaling which often results in wasted spend.