why training custom data use CPU instead of GPU

[amieruddin]

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Question

Hi,
i have 20k of images and already do data preparation(follow guideline by ultalytics).

I don't make any changes on any yolov5 file/hyp. I just use default setting by yolov5.

When training was started, i noticed that the training use all CPU memory (86%) rather then GPU (6%).

My device spec as below :
OS : Windows 10
GPU : NVIDIA RTX 3090
CPU : i9

Command for training : python train.py --img 640 --batch 16 --epochs 1500 --data dataset.yaml --weights yolov5s.pt --device 0

My question is why my device trained the yolo model using CPU instead of GPU? is that any line i need to make changes?
Need help to train the yolo on gpu..Thank you.

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[github-actions]

👋 Hello @amieruddin, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[niiita]

Hi, I have the same question!^^
How can I train on GPU instead of CPU?

[CCRcmcpe]

You should use the cuda version of torch.

Uninstall your current version, and follow https://pytorch.org/get-started/locally/

[CCRcmcpe]

@amieruddin I mean torch version. It's not related to the CUDA installed on your system.

[amieruddin]

im using both CUDA toolkit v.11.3 and Pytorch v.11.3..

based on my installation before...if i install Pytorch not same version with cuda toolkit...it will get error message when training

[amieruddin]

based on your link to https://pytorch.org/get-started/locally/ , it show latest version of PyTorch is 11.3.

@CCRcmcpe , is current PyTorch version 11.3 not support GPU to training YOLOv5?

[CCRcmcpe]

@amieruddin You have a huge misunderstanding. Latest pytorch version is 1.11.0, which uses CUDA 11.3. There's no pytorch 11.3. And you should choose compute platform CUDA and install.

[amieruddin]

@CCRcmcpe , I choose Pytorch Build : LTS 1.8.2 and install using the command :

pip3 install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html

but it show error like this.

then when i check pip freeze : it show only torch downgrade to torch==1.8.2+cu111.
torchaudio and torchvision unsuccessful to install torchvision==0.9.2+cu111 torchaudio===0.8.2

[CCRcmcpe]

@amieruddin That is strange. It shows you already have the torch version which supports cuda. Maybe torch is not choosing device properly. The error is caused by not running pip using elevated privileges.
To solve torch installation issue, run this with admin privilege:

pip3 install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -I -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html

Then in python interactive, run:

import torch

torch.cuda.is_available()
torch.cuda.current_device()
torch.cuda.device_count()

And post your output.

[CCRcmcpe]

@amieruddin Updated the instructions as it seems not clear.

[CCRcmcpe]

@amieruddin Alright. Seems you are already using GPU to train. My fault. The reason your GPU usage is so low is because your small batch size does not match your GPU performance. Increase your batch size to 32 or higher to solve the problem.

[amieruddin]

Thank you @CCRcmcpe, actually there is nothing wrong with my training result/model...because after training, I run the model and it work fine.

I just need help to train the model using GPU instead of CPU. Currently I set the epoch to 1500, and it takes me 5 days to finish the training. I want to speed up the time taken.

I try your suggestion to increase the batch size. I try batch size 32 & 64, it still use CPU, not GPU.
the command : python train.py --img 640 --batch 64 --epochs 1500 --data dataset.yaml --weights yolov5s.pt --device 0

After run this command, task manager show it use 100% of the CPU.
Also i got the error 'out of memory' at terminal

[glenn-jocher]

@amieruddin 👋 Hello! Thanks for asking about CUDA memory issues. YOLOv5 🚀 can be trained on CPU, single-GPU, or multi-GPU. When training on GPU it is important to keep your batch-size small enough that you do not use all of your GPU memory, otherwise you will see a CUDA Out Of Memory (OOM) Error and your training will crash. You can observe your CUDA memory utilization using either the nvidia-smi command or by viewing your console output:

CUDA Out of Memory Solutions

If you encounter a CUDA OOM error, the steps you can take to reduce your memory usage are:

• Reduce --batch-size
• Reduce --img-size
• Reduce model size, i.e. from YOLOv5x -> YOLOv5l -> YOLOv5m -> YOLOv5s > YOLOv5n
• Train with multi-GPU at the same --batch-size
• Upgrade your hardware to a larger GPU
• Train on free GPU backends with up to 16GB of CUDA memory:

AutoBatch

You can use YOLOv5 AutoBatch (NEW) to find the best batch size for your training by passing --batch-size -1. AutoBatch will solve for a 90% CUDA memory-utilization batch-size given your training settings. AutoBatch is experimental, and only works for Single-GPU training. It may not work on all systems, and is not recommended for production use.

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

[github-actions]

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