| Use automatic mixed precision in PyTorch | 13 source lines | Zero Tensor Core utilization |
| Use mulitensor optimizer in PyTorch | 1 source line | Many small GPU kernels in the optimizer |

Figure 6: Four simple optimizations applied.

3. Concluding Remarks
Performance tuning for PyTorch in production environments is increasingly important. A capable performance-debugging tool is a key to this process. We demonstrate with a case study on a production model that MAIProf is a powerful infrastructure for identifying optimization opportunities.