버전 차이 질문

[horcrux22]

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Question

ultralytics 버전을 8.1.0 에서 8.2.14로 변경 후
train 진행했을때 (같은옵션기준) 그래픽카드의 전력이 기존에 비해 더 먹는것 같은데 이유가 있나요 ?

gpu : rtx a6000
cpu : i9-10940X
cuda : 12.4

Additional

No response

[github-actions]

👋 Hello @horcrux22, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

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[glenn-jocher]

Hello! Thank you for reaching out. 🙌

It's possible that the increased GPU power usage you're observing after upgrading from YOLOv8 8.1.0 to 8.2.14 could be due to several factors such as changes in the model's architecture, optimization techniques, or dependency updates that utilize more GPU resources for efficiency improvements.

To be sure, I recommend checking the release notes for version 8.2.14 to see if there are any known updates or changes that might affect GPU utilization. Additionally, reviewing your training parameters or reducing the batch size may help mitigate the increased power usage.

Here's a quick example on how to check training-related GPU metrics, which might help in understanding GPU utilizations better:

nvidia-smi dmon

Feel free to ask if you need more specific guidance on mitigating the GPU usage or anything else! 🚀