Multi-GPU, subprocess.CalledProcessError

[DeanZag]

I am trying to perform a DL training using YOLO V5 on a HPC with two GPUs. I have followed all steps by Yolo V5 Multi-GPU DistributedDataParallel Mode. The command I have used to run the training is as follow:

`(base) amrcnw@amrcnw-G482-Z54-00:~/Olive_project/Yolo_V5/yolov5$ python -m torch.distributed.launch --master_port 9963 --nproc_per_node 2 train.py --data ./data.yaml --cfg yolov5s.yaml --weights yolov5s.pt --epochs 3 --batch-size 64 --device 0,1
#*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

---

/home/amrcnw/anaconda3/lib/python3.8/site-packages/torch/cuda/init.py:125: UserWarning:
A100-PCIE-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_61 sm_70 sm_75 compute_37.
If you want to use the A100-PCIE-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))
/home/amrcnw/anaconda3/lib/python3.8/site-packages/torch/cuda/init.py:125: UserWarning:
A100-PCIE-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_61 sm_70 sm_75 compute_37.
If you want to use the A100-PCIE-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

warnings.warn(incompatible_device_warn.format(device_name, capability, " ".join(arch_list), device_name))
Your branch is behind 'origin/master' by 457 commits, and can be fast-forwarded.
(use "git pull" to update your local branch)

Using torch 1.6.0 CUDA:0 (A100-PCIE-40GB, 40536MB)
CUDA:1 (A100-PCIE-40GB, 40536MB)

Traceback (most recent call last):
File "train.py", line 483, in
dist.init_process_group(backend='nccl', init_method='env://') # distributed backend
File "/home/amrcnw/anaconda3/lib/python3.8/site-packages/torch/distributed/distributed_c10d.py", line 422, in init_process_group
store, rank, world_size = next(rendezvous_iterator)
File "/home/amrcnw/anaconda3/lib/python3.8/site-packages/torch/distributed/rendezvous.py", line 172, in _env_rendezvous_handler
store = TCPStore(master_addr, master_port, world_size, start_daemon, timeout)
RuntimeError: Address already in use
Traceback (most recent call last):
File "/home/amrcnw/anaconda3/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/amrcnw/anaconda3/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/amrcnw/anaconda3/lib/python3.8/site-packages/torch/distributed/launch.py", line 261, in
main()
File "/home/amrcnw/anaconda3/lib/python3.8/site-packages/torch/distributed/launch.py", line 256, in main
raise subprocess.CalledProcessError(returncode=process.returncode,
subprocess.CalledProcessError: Command '['/home/amrcnw/anaconda3/bin/python', '-u', 'train.py', '--local_rank=1', '--data', './data.yaml', '--cfg', 'yolov5s.yaml', '--weights', 'yolov5s.pt', '--epochs', '3', '--batch-size', '64', '--device', '0,1']' returned non-zero exit status 1.`

Also I have used the following command which gave me the same error

python -m torch.distributed.launch --nproc_per_node 2 train.py --img 416 --batch 16 --epochs 300 --data ./data.yaml --cfg ./models/yolov5s.yaml --weights '' --name Test_3_10Fit yolov5s.pt

there is an error with subprocess.CalledProcessError: Command. Can someone explain to me what is wrong?

[github-actions]

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[glenn-jocher]

@DeanZag your error is probably related to the warnings displayed on your console about mismatch in your pytorch installation.

For best results I would recommend training in the Docker image:

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

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• Docker Image. See Docker Quickstart Guide

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If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are passing. These tests evaluate proper operation of basic YOLOv5 functionality, including training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu.

[DeanZag]

Hi @glenn-jocher

I had my images arranged by Roboflow and it was working fine on a previous GPU (GTX 1080). Currently, I am running the same images for training on HPC contain x2 NVIDIA RTX A6000 with 2TP memory.

I recloned yolov5 using the following commands

git clone https://github.com/ultralytics/yolov5 # clone repo cd yolov5 pip install -r requirements.txt

Here's the output I got

Nothing is happening aftere "Model Summary: 283 layers, 7063542 parameters, 7063542 gradients, 16.4 GFLOPs" (I did press Ctrl+Z to stop the process .

Not sure what is it wrong as it used to work fine few weeks when I run --evolve?!!

Additional questions:

When run the multiple GPUs command `python -m torch.distributed.launch --master_port 1234 --nproc_per_node 2 train.py --batch-size 64 --data ./data.yaml --weights yolov5s.pt` it display below output. 

Thanks

[glenn-jocher]

@DeanZag your PyTorch install is incompatible with your hardware. This has nothing to do with YOLOv5. Visit https://pytorch.org/get-started/locally/

[DeanZag]

Thanks @glenn-jocher
Just fixed the PyTorch using the following command
conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c nvidia

I had to restart the HPC in order to resolve the NCCL issue and it is working.

surprisingly I have used the original commands to run training
python train.py --img 416 --batch 64 --epochs 300 --data ./data.yaml --cfg models/yolov5s.yaml --weights '' --name yolov5s_HPC_Train_results --cache

It is working fine

Not sure if both GPUs are on or just one?!! since didn't use the Mlti GPUs command
python -m torch.distributed.launch --master_port 1234 --nproc_per_node 2 train.py --batch-size 64 --data ./data.yaml --weights yolov5s.pt

Any ideas?

[glenn-jocher]

@DeanZag current code requires opt-in to any devices you want to use other than device 0:

python -m torch.distributed.run --nproc_per_node 2 train.py --data coco128.yaml --device 0,1

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