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ultralytics 8.0.141 create new SettingsManager (#3790)

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Glenn Jocher 2023-07-23 16:03:34 +02:00 • committed by GitHub
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docs/yolov5/tutorials/multi_gpu_training.md
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@ -4,7 +4,7 @@ description: Learn how to train datasets on single or multiple GPUs using YOLOv5
keywords: YOLOv5, multi-GPU Training, YOLOv5 training, deep learning, machine learning, object detection, Ultralytics
---
📚 This guide explains how to properly use **multiple** GPUs to train a dataset with YOLOv5 🚀 on single or multiple machine(s).
📚 This guide explains how to properly use **multiple** GPUs to train a dataset with YOLOv5 🚀 on single or multiple machine(s).
UPDATED 25 December 2022.
## Before You Start
@ -136,9 +136,9 @@ cd .. && rm -rf app && git clone https://github.com/ultralytics/yolov5 -b master
cp data/coco.yaml data/coco_profile.yaml
# profile
python train.py --batch-size 16 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0
python -m torch.distributed.run --nproc_per_node 2 train.py --batch-size 32 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0,1
python -m torch.distributed.run --nproc_per_node 4 train.py --batch-size 64 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0,1,2,3
python train.py --batch-size 16 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0
python -m torch.distributed.run --nproc_per_node 2 train.py --batch-size 32 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0,1
python -m torch.distributed.run --nproc_per_node 4 train.py --batch-size 64 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0,1,2,3
python -m torch.distributed.run --nproc_per_node 8 train.py --batch-size 128 --data coco_profile.yaml --weights yolov5l.pt --epochs 1 --device 0,1,2,3,4,5,6,7
```
@ -188,4 +188,4 @@ If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralyti
## Credits
I would like to thank @MagicFrogSJTU, who did all the heavy lifting, and @glenn-jocher for guiding us along the way.
I would like to thank @MagicFrogSJTU, who did all the heavy lifting, and @glenn-jocher for guiding us along the way.