Mutli GPU Training

[Venky0892]

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Hi, I trying to train around 200K images via Tesla V100, I have 4 of these in my compute instance. I'm training with this command "python -m torch.distributed.launch --nproc_per_node 4" as mentioned in the documentation. But I could only able see just one GPU is active, I'm not sure why? I checked the GPU usage metric in Wandb tool. Can someone help me with this? If I run normal training without distributed launch I could see all the GPU are being used.

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[glenn-jocher]

@Venky0892 see Multi-GPU Training tutorial for correct commands:

YOLOv5 Tutorials

• Train Custom Data  🚀 RECOMMENDED
• Tips for Best Training Results  ☘️ RECOMMENDED
• Weights & Biases Logging  🌟 NEW
• Roboflow for Datasets, Labeling, and Active Learning  🌟 NEW
• Multi-GPU Training
• PyTorch Hub  ⭐ NEW
• TFLite, ONNX, CoreML, TensorRT Export 🚀
• Test-Time Augmentation (TTA)
• Model Ensembling
• Model Pruning/Sparsity
• Hyperparameter Evolution
• Transfer Learning with Frozen Layers  ⭐ NEW
• Architecture Summary  ⭐ NEW

Good luck 🍀 and let us know if you have any other questions!

[Venky0892]

Hi @glenn-jocher
I referred to the documentation for Multi GPU Training:
Here the Command : "$ python -m torch.distributed.launch --nproc_per_node 4 train.py --batch 128 --data furniture.yaml --weights yolov5s.pt --device 0,1,2,3

I want to use four GPU's so gave --nproc_ per_node = 4

I could only see GPU 0 is active and running. I'm not sure it could be great if you can assits me in this

[github-actions]

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[glenn-jocher]

@Venky0892 The command you provided looks correct, so it's strange that only GPU 0 is active. Would you mind checking if the nvidia-smi command correctly shows all 4 GPUs under normal circumstances? Additionally, could you please verify that your system meets the requirements outlined in the Multi-GPU Training tutorial in our documentation? Thank you!