CUDA ускорение для обучения нейросети с модулем ultralytics
- Я установил ultralytics
- Начала обучать модель YOLOV8n
- Понял, что CUDA не работает, установил CUDA Toolkit и cuDNN
- Некоторое время догонял что torch тоже нужен не простой а с CUDA
- Установил torch такой командой:
pip install torch==2.0.0+cu117 torchvision==0.15.1+cu117 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu117
- Проверил подхватил ли torch CUDA таким образом:
import torch
print(torch.cuda.is_available())
- Увидел true, уже обрадовался, но не тут то было
- Начал обучать модель вот так:
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
results = model.train(
data='pothole_v8.yaml',
imgsz=1280,
epochs=50,
device=0,
batch=8,
name='yolov8n_v8_50e')
- И по какой-то неведомой мне причине модель начинает обучаться какбуд-то бы заного как только доходит собственно до самого процесса обучения и пишет что нужно использовать какой-то fork для запуска дочерних процессов.
- Вывод в терминал:
PS C:\workspace\mashine lerning> & C:/Users/Firnen/AppData/Local/Programs/Python/Python311/python.exe "c:/workspace/mashine lerning/train.py"
Ultralytics YOLOv8.0.132 Python-3.11.4 torch-2.0.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1060 3GB, 3072MiB)
yolo\engine\trainer: task=detect, mode=train, model=yolov8n.pt, data=pothole_v8.yaml, epochs=50, patience=50, batch=8, imgsz=1280, save=True, save_period=-1, cache=False, device=0, workers=8, project=None, name=yolov8n_v8_50e, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs\detect\yolov8n_v8_50e7
Overriding model.yaml nc=80 with nc=1
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
Model summary: 225 layers, 3011043 parameters, 3011027 gradients
Transferred 319/355 items from pretrained weights
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed
train: Scanning C:\workspace\mashine lerning\pothole_dataset_v8\train\labels.cache... 6960 images, 10 backgrounds, 0 corrupt: 100%|██████████| 6962/6962 [00:00<?, ?it/s]
train: WARNING C:\workspace\mashine lerning\pothole_dataset_v8\train\images\G0012012.jpg: 2 duplicate labels removed
train: WARNING C:\workspace\mashine lerning\pothole_dataset_v8\train\images\G0052120.jpg: 1 duplicate labels removed
Ultralytics YOLOv8.0.132 Python-3.11.4 torch-2.0.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1060 3GB, 3072MiB)
yolo\engine\trainer: task=detect, mode=train, model=yolov8n.pt, data=pothole_v8.yaml, epochs=50, patience=50, batch=8, imgsz=1280, save=True, save_period=-1, cache=False, device=0, workers=8, project=None, name=yolov8n_v8_50e, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs\detect\yolov8n_v8_50e8
Overriding model.yaml nc=80 with nc=1
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
Model summary: 225 layers, 3011043 parameters, 3011027 gradients
Transferred 319/355 items from pretrained weights
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
AMP: checks passed
train: Scanning C:\workspace\mashine lerning\pothole_dataset_v8\train\labels.cache... 6960 images, 10 backgrounds, 0 corrupt: 100%|██████████| 6962/6962 [00:00<?, ?it/s]
train: WARNING C:\workspace\mashine lerning\pothole_dataset_v8\train\images\G0012012.jpg: 2 duplicate labels removed
train: WARNING C:\workspace\mashine lerning\pothole_dataset_v8\train\images\G0052120.jpg: 1 duplicate labels removed
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\spawn.py", line 120, in spawn_main
exitcode = _main(fd, parent_sentinel)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\spawn.py", line 129, in _main
prepare(preparation_data)
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\spawn.py", line 240, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\spawn.py", line 291, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen runpy>", line 291, in run_path
File "<frozen runpy>", line 98, in _run_module_code
File "<frozen runpy>", line 88, in _run_code
File "c:\workspace\mashine lerning\train.py", line 7, in <module>
results = model.train(
^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\ultralytics\yolo\engine\model.py", line 373, in train
self.trainer.train()
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\ultralytics\yolo\engine\trainer.py", line 192, in train
self._do_train(world_size)
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\ultralytics\yolo\engine\trainer.py", line 276, in _do_train
self._setup_train(world_size)
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\ultralytics\yolo\engine\trainer.py", line 240, in _setup_train
self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=RANK, mode='train')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\ultralytics\yolo\v8\detect\train.py", line 60, in get_dataloader
return build_dataloader(dataset, batch_size, workers, shuffle, rank) # return dataloader
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\ultralytics\yolo\data\build.py", line 101, in build_dataloader
return InfiniteDataLoader(dataset=dataset,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\ultralytics\yolo\data\build.py", line 29, in __init__
self.iterator = super().__iter__()
^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\utils\data\dataloader.py", line 442, in __iter__
return self._get_iterator()
^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\utils\data\dataloader.py", line 388, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\utils\data\dataloader.py", line 1043, in __init__
w.start()
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\process.py", line 121, in start
self._popen = self._Popen(self)
^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\context.py", line 224, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\context.py", line 336, in _Popen
return Popen(process_obj)
^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\popen_spawn_win32.py", line 45, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\spawn.py", line 158, in get_preparation_data
_check_not_importing_main()
File "C:\Users\Firnen\AppData\Local\Programs\Python\Python311\Lib\multiprocessing\spawn.py", line 138, in _check_not_importing_main
raise RuntimeError('''
RuntimeError:
An attempt has been made to start a new process before the
current process has finished its bootstrapping phase.
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
- При этом без CUDA все обучается нормально, но долго
- Может кто-то сталкивался с подобным, или может знает куда копать? Потому что пока я в тупике.
Источник: Stack Overflow на русском