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Artificial Intelligence, particularly Computer Vision, has revolutionized image segmentation, enabling the detection of various objects. In the energy sector, AI interprets geological objects in different kind of seismic data. However, deploying such solutions efficiently at scale is challenging due to different data sizes, types, and segmentation purposes. Additionally, in a Cloud environment, GPU resources may be limited, with multiple users requesting access simultaneously.
To address this, we optimized our solution focusing on maximizing throughput and resource occupancy for multiple users. By employing tiling techniques and dynamic load balancing, we ensured computational efficiency and minimized waiting times. Moreover, the Multi-Instance GPU (MIG) configuration was enabled, dividing GPU resources into independent partitions, virtually adding more GPUs.
We illustrated this strategy in a multi-user Cloud environment, proving its effectiveness.