Ultralytics
Partitioning Model to Optimize Inference Speed #15000
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I want to experiment with v5l and v5x models by partitioning the models and distributing them across multiple GPU's to run inference cooperatively. My question is has this been done before and/or is it worth doing? Am I likely to see worthwhile performance gains for the effort. Additional tips or guidance would be appreciated. |
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Replies: 1 comment 4 replies
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@S-Mnn partitioning models to distribute inference across multiple GPUs can indeed optimize performance, especially for larger models like YOLOv5l and YOLOv5x. This approach can reduce latency and improve throughput by leveraging parallel processing. However, the complexity of implementation and potential overhead from inter-GPU communication should be considered. While this technique has been explored in various contexts, the performance gains can vary based on the specific hardware and workload. For practical insights, I recommend experimenting with model partitioning and benchmarking the results on your setup. If you encounter any issues, ensure you're using the latest package versions. |
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@glenn-jocher Hey sorry to bother you, do you have insights or resources on finding optimal model partitioning points for YOLOv5. Thanks! |
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@S-Mnn for optimal model partitioning points in YOLOv5, consider focusing on the network's layers where computational loads are heaviest, such as the CSP bottleneck layers. Experimentation and profiling tools can help identify these points. For more detailed guidance, refer to the Ultralytics documentation and relevant research papers. |
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@glenn-jocher I'm looking to get information on hardware memory consumption with an table output like the built-in profiling:
Except with additional column for either GPU and/or CPU memory, does such an implementation currently exist? |
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Currently, there isn't a built-in feature for detailed hardware memory consumption profiling in YOLOv5. However, you can use external tools like NVIDIA's |
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@S-Mnn partitioning models to distribute inference across multiple GPUs can indeed optimize performance, especially for larger models like YOLOv5l and YOLOv5x. This approach can reduce latency and improve throughput by leveraging parallel processing. However, the complexity of implementation and potential overhead from inter-GPU communication should be considered. While this technique has been explored in various contexts, the performance gains can vary based on the specific hardware and workload. For practical insights, I recommend experimenting with model partitioning and benchmarking the results on your setup. If you encounter any issues, ensure you're using the latest package versions.