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Accelerating Hyperparameter Tuning with Container-Level GPU Virtualization
Jeongkyu Shin, Lablup Inc. | Joongi Kim, Lablup Inc.
Many people think that hyperparameter tuning requires a large number of GPUs to get optimal results quickly. That's generally true, but to what extent? We'll present what we've learned about finding a sweet spot to balance both costs and accuracy by exploiting partitioned GPUs with Backend.AI's container-level GPU virtualization. Our benchmark includes distributed mnist, cifar10 transfer learning and TGS salt identification cases using AutoML with network morphism and ENAS tuner with NNI running on NGC-optimized containers on Backend.AI. You'll get a tangible guide to plan and deploy your GPU infrastructure capacity in a more cost-effective way.