GPU as a Service on Hybrid Cloud Platforms

Introduction

As AI and machine learning workloads grow in size and complexity, leveraging GPU resources efficiently is critical. GPU as a Service (GaaS) in a hybrid cloud environment allows organizations to utilize powerful GPU resources on-demand, seamlessly blending on-premises infrastructure with cloud platforms. This chapter explores the core services necessary for offering GPU as a Service on a hybrid cloud platform, with a focus on AI and machine learning applications.

1. Scheduling and Orchestration

GPU/CPU Checkpointing

• Definition: GPU/CPU checkpointing allows for the state of the computational task to be saved at intervals, ensuring that work isn’t lost in case of a failure. This is especially important in long-running AI and machine learning tasks where progress needs to be preserved.
• Use Case Scenario for AI: In training large-scale deep learning models, checkpointing ensures that if a node fails, the training can resume from the last saved checkpoint rather than starting over.
• GPU-Specific Considerations: NVIDIA GPUs leverage optimized libraries like CUDA for checkpointing, allowing for efficient state capture and restoration, minimizing downtime.
• Operational Execution: Integration with orchestration frameworks such as Kubernetes ensures that checkpointing is done automatically, with minimal user intervention.