GPU Scheduling and Bin-Packing in Kubernetes: Pack More AI onto Every GPU
Blog post from Cast AI
The text discusses the challenges and solutions associated with GPU scheduling and utilization in Kubernetes environments. It highlights the inefficiencies of the default Kubernetes scheduler, which results in low GPU utilization of around 5% across production clusters by spreading workloads too thinly across nodes. To address this, it outlines advanced techniques such as GPU-aware bin-packing, the use of NVIDIA’s device plugin, and Dynamic Resource Allocation (DRA), which together improve GPU utilization by placing more AI work on fewer GPUs. The NVIDIA device plugin allows GPUs to be advertised as integer resources, while DRA introduces attribute-based scheduling, allowing for more precise resource allocation. Techniques like MIG partitioning and time-slicing are recommended for optimizing GPU usage and enabling fractional sharing of resources. The text also discusses the importance of ongoing consolidation to reclaim underutilized resources and suggests using tools like Cast AI for automation. Overall, it emphasizes that achieving higher GPU utilization does not require new hardware but rather the implementation of targeted scheduling and resource management strategies.
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