YOLOv5 issues with `torch==1.12` on Multi-GPU systems

[glenn-jocher]

Search before asking

• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Training, Multi-GPU

Bug

All GPUs are utilized by torch 1.12 with current YOLOv5 master when a single-GPU command is run, i.e.:

Error does not occur with torch==1.11

@AyushExel FYI

Environment

Docker image

Minimal Reproducible Example

python train.py --epochs 10 --device 7

Additional

Temp workaround is to use torch 1.11

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

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

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):

• Google Colab and Kaggle 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

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[AyushExel]

I wonder how a bug this big made into the release

[UnglvKitDe]

@glenn-jocher I cannot confirm this. Tested on gpu servers with 2,4,8 GPUS (RTX 2080TI/A6000)
EDIT: Maby a workaround is to use CUDA_VISIBLE_DEVICES?

[glenn-jocher]

@AyushExel it's probably not a torch bug but instead related to our specific implementation for selecting devices in select_device() below which relies on defining CUDA_VISIBLE_DEVICES in the workspace before torch reads it.
mode.

yolov5/utils/torch_utils.py

Lines 52 to 86 in 8983324

	def select_device(device='', batch_size=0, newline=True):
	# device = None or 'cpu' or 0 or '0' or '0,1,2,3'
	s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '
	device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'
	cpu = device == 'cpu'
	mps = device == 'mps' # Apple Metal Performance Shaders (MPS)
	if cpu or mps:
	os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False
	elif device: # non-cpu device requested
	os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()
	assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
	f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
	if not cpu and torch.cuda.is_available(): # prefer GPU if available
	devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7
	n = len(devices) # device count
	if n > 1 and batch_size > 0: # check batch_size is divisible by device_count
	assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
	space = ' ' * (len(s) + 1)
	for i, d in enumerate(devices):
	p = torch.cuda.get_device_properties(i)
	s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
	arg = 'cuda:0'
	elif not cpu and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available
	s += 'MPS\n'
	arg = 'mps'
	else: # revert to CPU
	s += 'CPU\n'
	arg = 'cpu'
	if not newline:
	s = s.rstrip()
	LOGGER.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe
	return torch.device(arg)

If I run the reproduce command above in Docker image, python train.py --epochs 10 --device 7, I see this on our server:

[glenn-jocher]

@UnglvKitDe you tested a single-GPU training command above python train.py --epochs 10 --device 7 with torch 1.12 and did not see multi-GPU usage on nvidia-smi?

[UnglvKitDe]

@glenn-jocher

@UnglvKitDe you tested a single-GPU training command above python train.py --epochs 10 --device 7 with torch 1.12 and did not see multi-GPU usage on nvidia-smi?

@glenn-jocher not exactly, but in the end the same. i tested it on coco128 ( torch 1.12 and cuda 11.6). so python train.py --data coco128.yaml --device 0 --epochs 10.

[glenn-jocher]

@UnglvKitDe oh, strange. This is basically the same command I used, but I used device 7. If I try --device 0 I also get the bug though.

We'll ok I'll experiment some more. In my experiments 1.11 works correctly but 1.12 does not (master branch in Docker) with CUDA 11.3

[mjun0812]

It appears that this problem occurred because of a change in the timing of reading environment variables in PyTorch 1.12.

My GPU environment is here.

CUDA:0 NVIDIA RTX A6000, 48685.3125MB
CUDA:1 NVIDIA GeForce RTX 3090, 24268.3125MB

The following code worked correctly in version 1.11.

import os
import torch

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

# print using GPU Info
print(f"Using GPU is CUDA:{os.environ['CUDA_VISIBLE_DEVICES']}")

for i in range(torch.cuda.device_count()):
    info = torch.cuda.get_device_properties(i)
    print(f"CUDA:{i} {info.name}, {info.total_memory / 1024 ** 2}MB")

In version 1.11, the output of this code is below.

Using GPU is CUDA:1
CUDA:0 NVIDIA GeForce RTX 3090, 24268.3125MB

But version 1.12's output is below.

Using GPU is CUDA:1
CUDA:0 NVIDIA RTX A6000, 48685.3125MB
CUDA:1 NVIDIA GeForce RTX 3090, 24268.3125MB

As additional information, the environment variable change worked correctly when done before import torch.

import os

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

import torch


# print using GPU Info
print(f"Using GPU is CUDA:{os.environ['CUDA_VISIBLE_DEVICES']}")

for i in range(torch.cuda.device_count()):
    info = torch.cuda.get_device_properties(i)
    print(f"CUDA:{i} {info.name}, {info.total_memory / 1024 ** 2}MB")

Output is below.

Using GPU is CUDA:1
CUDA:0 NVIDIA GeForce RTX 3090, 24268.3125MB

[glenn-jocher]

@mjun0812 interesting, thanks for the info! Unfortunately we can't specify the environment variables before loading torch. Does re-loading torch after defining the environment variables have any effect? i.e.:

import os
import torch

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

import torch


# print using GPU Info
print(f"Using GPU is CUDA:{os.environ['CUDA_VISIBLE_DEVICES']}")

for i in range(torch.cuda.device_count()):
    info = torch.cuda.get_device_properties(i)
    print(f"CUDA:{i} {info.name}, {info.total_memory / 1024 ** 2}MB")

[mjun0812]

@glenn-jocher Thank you for your reply!
I tried your code and the following code.

Your suggestion code:

import os
import torch

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

import torch

# print using GPU Info
print(f"Using GPU is CUDA:{os.environ['CUDA_VISIBLE_DEVICES']}")

for i in range(torch.cuda.device_count()):
    info = torch.cuda.get_device_properties(i)
    print(f"CUDA:{i} {info.name}, {info.total_memory / 1024 ** 2}MB")

and del torch

import os
import torch

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

del torch
import torch

# print using GPU Info
print(f"Using GPU is CUDA:{os.environ['CUDA_VISIBLE_DEVICES']}")

for i in range(torch.cuda.device_count()):
    info = torch.cuda.get_device_properties(i)
    print(f"CUDA:{i} {info.name}, {info.total_memory / 1024 ** 2}MB")

and importlib.reload()

import os
import torch
import importlib

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

importlib.reload(torch)

# print using GPU Info
print(f"Using GPU is CUDA:{os.environ['CUDA_VISIBLE_DEVICES']}")

for i in range(torch.cuda.device_count()):
    info = torch.cuda.get_device_properties(i)
    print(f"CUDA:{i} {info.name}, {info.total_memory / 1024 ** 2}MB")

The above outputs were the same.

Using GPU is CUDA:1
CUDA:0 NVIDIA RTX A6000, 48685.3125MB
CUDA:1 NVIDIA GeForce RTX 3090, 24268.3125MB

This issue could not be resolved...
I will continue to investigate the issue.

[glenn-jocher]

@mjun0812 too bad. Yes let me know if you find a solution!