torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 86.00 MiB (GPU 0； 31.74 GiB total

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文章讲述了在使用PyTorch时遇到CUDAOutofMemory错误，提出了通过设置PYTORCH_CUDA_ALLOC_CONF的max_split_size_mb来减少内存分配，但可能影响性能。建议参考链接进行详细处理。
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torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 86.00 MiB (GPU 0; 31.74 GiB total capacity; 6.39 GiB already allocated; 78.38 MiB free; 6.47 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

解决办法
逐步调小PYTORCH_CUDA_ALLOC_CONF，但是会损失性能。
参考：https://blog.youkuaiyun.com/MirageTanker/article/details/127998036

set PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:16

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(yolov8) D:\ultralytics-main>yolo task=detect mode=train model=yolov8n.pt data=data/blueberry.yaml batch=8 epochs=200 imgsz=640 workers=8 device=0 amp=False New https://pypi.org/project/ultralytics/8.3.219 available Update with 'pip install -U ultralytics' Ultralytics 8.3.202 Python-3.10.18 torch-2.6.0+cu118 CUDA:0 (NVIDIA GeForce RTX 4050 Laptop GPU, 6140MiB) engine\trainer: agnostic_nms=False, amp=False, augment=False, auto_augment=randaugment, batch=8, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=Non e, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=data/blueberry.yaml, degrees=0.0, deterministic=True, device=0, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epoch s=200, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, ko bj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train7, nbs=64, nms=False, opset=Non e, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, s ave=True, save_conf=False, save_crop=False, save_dir=D:\ultralytics-main\runs\detect\train7, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False , show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None Overriding model.yaml nc=80 with nc=2 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 751702 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]] Model summary: 129 layers, 3,011,238 parameters, 3,011,222 gradients, 8.2 GFLOPs Transferred 319/355 items from pretrained weights Freezing layer 'model.22.dfl.conv.weight' train: Fast image access (ping: 1.21.2 ms, read: 624.7127.7 MB/s, size: 5704.1 KB) train: Scanning data\labels.cache... 42 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 42/42 24.6Kit/s 0.0s val: Fast image access (ping: 0.40.1 ms, read: 391.875.8 MB/s, size: 6828.0 KB) val: Scanning data\labels.cache... 42 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 42/42 16.8Kit/s 0.0s Plotting labels to D:\ultralytics-main\runs\detect\train7\labels.jpg... optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to D:\ultralytics-main\runs\detect\train7 Starting training for 200 epochs... 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3.3it/s 0.9s all 42 11765 0.251 0.147 0.149 0.0519 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 13/200 4.76G 3.548 2.304 1.012 1116 640: 100% ━━━━━━━━━━━━ 6/6 3.4it/s 1.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.5it/s 0.9s all 42 11765 0.265 0.178 0.177 0.0625 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 14/200 4.72G 3.533 2.251 0.9986 957 640: 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.258 0.2 0.192 0.0655 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 15/200 4.59G 3.384 2.166 1.02 460 640: 100% ━━━━━━━━━━━━ 6/6 4.6it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.2it/s 0.9s all 42 11765 0.258 0.2 0.192 0.0655 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 16/200 4.34G 3.504 2.104 0.9965 829 640: 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.3it/s 0.9s all 42 11765 0.302 0.204 0.206 0.0701 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 17/200 5.1G 3.354 2.074 0.9933 623 640: 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.5it/s 0.9s all 42 11765 0.333 0.204 0.206 0.069 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 18/200 4.22G 3.434 2.09 0.9822 744 640: 100% ━━━━━━━━━━━━ 6/6 4.4it/s 1.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.5it/s 0.9s all 42 11765 0.314 0.199 0.193 0.0615 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 19/200 4.31G 3.422 2.004 0.9906 903 640: 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.3it/s 0.9s all 42 11765 0.314 0.199 0.193 0.0615 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 20/200 4.72G 3.507 2.059 0.9847 1114 640: 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.3it/s 0.9s all 42 11765 0.36 0.219 0.226 0.0758 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 21/200 5.14G 3.423 2.019 0.9703 1000 640: 100% ━━━━━━━━━━━━ 6/6 3.1it/s 1.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.377 0.2 0.22 0.0718 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 22/200 4.46G 3.355 1.925 0.9711 1020 640: 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.6it/s 0.8s all 42 11765 0.392 0.191 0.221 0.0744 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 23/200 3.78G 3.305 1.896 0.9725 829 640: 100% ━━━━━━━━━━━━ 6/6 4.2it/s 1.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.6it/s 0.8s all 42 11765 0.392 0.191 0.221 0.0744 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 24/200 4.39G 3.448 1.958 0.9686 931 640: 100% ━━━━━━━━━━━━ 6/6 2.7it/s 2.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.237 0.239 0.226 0.0734 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 25/200 4.54G 3.356 1.894 0.9633 1200 640: 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.221 0.225 0.199 0.0602 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 26/200 3.44G 3.381 1.904 0.96 725 640: 100% ━━━━━━━━━━━━ 6/6 3.7it/s 1.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.3it/s 0.9s all 42 11765 0.265 0.246 0.23 0.0749 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 27/200 4.87G 3.364 1.888 0.9489 634 640: 100% ━━━━━━━━━━━━ 6/6 3.7it/s 1.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.2it/s 1.0s all 42 11765 0.265 0.246 0.23 0.0749 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 28/200 4.83G 3.313 1.876 0.9653 340 640: 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.0it/s 1.0s all 42 11765 0.347 0.246 0.259 0.0869 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 29/200 4.51G 3.313 1.864 0.9579 1098 640: 100% ━━━━━━━━━━━━ 6/6 3.8it/s 1.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.39 0.27 0.297 0.102 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 30/200 4.85G 3.254 1.845 0.9615 662 640: 100% ━━━━━━━━━━━━ 6/6 3.1it/s 1.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.0it/s 1.0s all 42 11765 0.396 0.286 0.305 0.109 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 31/200 3.75G 3.211 1.84 0.967 475 640: 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.396 0.286 0.305 0.109 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 32/200 4.69G 3.284 1.798 0.9477 1248 640: 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.0it/s 1.0s all 42 11765 0.389 0.287 0.303 0.108 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 33/200 4G 3.255 1.801 0.9515 1075 640: 100% ━━━━━━━━━━━━ 6/6 3.3it/s 1.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.0it/s 1.0s all 42 11765 0.374 0.294 0.307 0.106 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 34/200 4.98G 3.243 1.811 0.9488 704 640: 100% ━━━━━━━━━━━━ 6/6 3.2it/s 1.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.386 0.29 0.314 0.113 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 35/200 4.92G 3.361 1.854 0.945 1328 640: 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.386 0.29 0.314 0.113 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 36/200 4.72G 3.179 1.734 0.9461 803 640: 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.365 0.246 0.266 0.0864 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 37/200 4.55G 3.199 1.801 0.9587 333 640: 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 2.9it/s 1.0s all 42 11765 0.364 0.222 0.236 0.0713 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 38/200 4.97G 3.271 1.779 0.9386 841 640: 100% ━━━━━━━━━━━━ 6/6 2.9it/s 2.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.0it/s 1.0s all 42 11765 0.408 0.272 0.291 0.0964 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 39/200 4.96G 3.248 1.78 0.9376 718 640: 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.408 0.272 0.291 0.0964 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 40/200 3.34G 3.225 1.762 0.9413 1172 640: 100% ━━━━━━━━━━━━ 6/6 4.4it/s 1.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.413 0.287 0.3 0.0992 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 41/200 5.12G 3.123 1.722 0.9387 605 640: 100% ━━━━━━━━━━━━ 6/6 2.6it/s 2.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.0it/s 1.0s all 42 11765 0.434 0.299 0.321 0.109 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 42/200 4.64G 3.106 1.733 0.9512 486 640: 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.2it/s 0.7s all 42 11765 0.431 0.311 0.328 0.114 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 43/200 5.05G 3.21 1.779 0.9308 594 640: 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.2it/s 0.7s all 42 11765 0.431 0.311 0.328 0.114 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 44/200 4.89G 3.135 1.688 0.9401 1036 640: 100% ━━━━━━━━━━━━ 6/6 4.8it/s 1.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.1it/s 0.7s all 42 11765 0.443 0.328 0.344 0.122 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 45/200 4.96G 3.108 1.689 0.9419 654 640: 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.2it/s 1.0s all 42 11765 0.464 0.322 0.356 0.131 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 46/200 4.15G 3.192 1.692 0.9359 468 640: 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 2.7it/s 1.1s all 42 11765 0.469 0.326 0.361 0.134 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 47/200 5.17G 3.217 1.744 0.9389 970 640: 100% ━━━━━━━━━━━━ 6/6 3.7it/s 1.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.469 0.326 0.361 0.134 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 48/200 3.94G 3.099 1.664 0.9427 875 640: 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 2.9it/s 1.0s all 42 11765 0.487 0.324 0.37 0.137 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 49/200 5.12G 3.138 1.679 0.9366 877 640: 100% ━━━━━━━━━━━━ 6/6 2.3it/s 2.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.487 0.329 0.374 0.141 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 50/200 5.17G 3.008 1.646 0.9493 586 640: 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.5it/s 0.9s all 42 11765 0.491 0.313 0.365 0.137 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 51/200 4.93G 3.094 1.708 0.9304 839 640: 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.491 0.313 0.365 0.137 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 52/200 4.39G 3.073 1.665 0.9328 342 640: 100% ━━━━━━━━━━━━ 6/6 3.8it/s 1.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 2.9it/s 1.0s all 42 11765 0.498 0.32 0.375 0.143 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 53/200 5.04G 3.099 1.66 0.9349 614 640: 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.479 0.325 0.365 0.13 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 54/200 5.08G 3.154 1.715 0.9196 767 640: 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.3it/s 0.7s all 42 11765 0.452 0.299 0.332 0.112 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 55/200 4.49G 3.084 1.661 0.92 504 640: 100% ━━━━━━━━━━━━ 6/6 5.2it/s 1.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.4it/s 0.7s all 42 11765 0.452 0.299 0.332 0.112 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 56/200 4.6G 3.155 1.681 0.9171 1222 640: 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.4it/s 0.7s all 42 11765 0.427 0.299 0.321 0.109 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 57/200 4.37G 3.061 1.616 0.926 1256 640: 100% ━━━━━━━━━━━━ 6/6 5.1it/s 1.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.1it/s 0.7s all 42 11765 0.43 0.305 0.323 0.11 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 58/200 3.72G 3.125 1.681 0.9149 1413 640: 100% ━━━━━━━━━━━━ 6/6 4.5it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 2.9it/s 1.0s all 42 11765 0.411 0.291 0.303 0.0959 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 59/200 4.93G 3.025 1.612 0.9305 482 640: 100% ━━━━━━━━━━━━ 6/6 2.5it/s 2.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.411 0.291 0.303 0.0959 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 60/200 4.69G 3.051 1.681 0.9289 315 640: 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.2it/s 0.9s all 42 11765 0.436 0.304 0.324 0.101 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 61/200 4.77G 2.941 1.568 0.9289 706 640: 100% ━━━━━━━━━━━━ 6/6 4.3it/s 1.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.472 0.333 0.367 0.127 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 62/200 4.07G 2.969 1.628 0.9296 452 640: 100% ━━━━━━━━━━━━ 6/6 3.4it/s 1.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.2it/s 0.9s all 42 11765 0.496 0.34 0.386 0.142 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 63/200 4.49G 3.089 1.618 0.9182 530 640: 100% ━━━━━━━━━━━━ 6/6 3.3it/s 1.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.496 0.34 0.386 0.142 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 64/200 4.78G 2.911 1.557 0.9358 570 640: 100% ━━━━━━━━━━━━ 6/6 4.2it/s 1.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.488 0.338 0.385 0.142 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 65/200 4.36G 3.034 1.618 0.9175 775 640: 100% ━━━━━━━━━━━━ 6/6 3.7it/s 1.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.2it/s 0.9s all 42 11765 0.504 0.341 0.396 0.149 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 66/200 3.67G 2.992 1.601 0.9221 580 640: 100% ━━━━━━━━━━━━ 6/6 3.8it/s 1.6s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.2it/s 0.9s all 42 11765 0.518 0.351 0.408 0.153 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 67/200 3.62G 3.076 1.626 0.905 532 640: 100% ━━━━━━━━━━━━ 6/6 3.9it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.5it/s 0.9s all 42 11765 0.518 0.351 0.408 0.153 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 68/200 4.82G 3.088 1.649 0.9148 304 640: 100% ━━━━━━━━━━━━ 6/6 3.3it/s 1.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.527 0.356 0.416 0.156 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 69/200 4.73G 3.053 1.603 0.9104 933 640: 100% ━━━━━━━━━━━━ 6/6 3.1it/s 1.9s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.3it/s 0.9s all 42 11765 0.51 0.352 0.404 0.145 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 70/200 4.53G 3.059 1.659 0.919 301 640: 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.7it/s 0.8s all 42 11765 0.509 0.351 0.403 0.145 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 71/200 4.88G 2.999 1.629 0.9262 547 640: 100% ━━━━━━━━━━━━ 6/6 5.2it/s 1.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.3it/s 0.7s all 42 11765 0.509 0.351 0.403 0.145 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 72/200 4.95G 3.057 1.585 0.9244 676 640: 100% ━━━━━━━━━━━━ 6/6 4.7it/s 1.3s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.2it/s 0.7s all 42 11765 0.524 0.347 0.408 0.152 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 73/200 4.77G 2.973 1.592 0.9305 374 640: 100% ━━━━━━━━━━━━ 6/6 5.1it/s 1.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.3it/s 0.7s all 42 11765 0.527 0.351 0.411 0.152 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 74/200 4.44G 2.94 1.544 0.9287 737 640: 100% ━━━━━━━━━━━━ 6/6 4.9it/s 1.2s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.2it/s 0.7s all 42 11765 0.533 0.347 0.408 0.149 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 75/200 5.19G 3.024 1.615 0.9043 729 640: 100% ━━━━━━━━━━━━ 6/6 3.4it/s 1.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.4it/s 0.7s all 42 11765 0.533 0.347 0.408 0.149 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 76/200 3.73G 2.923 1.564 0.9294 647 640: 100% ━━━━━━━━━━━━ 6/6 5.6it/s 1.1s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 4.4it/s 0.7s all 42 11765 0.522 0.349 0.407 0.147 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 77/200 4.68G 2.934 1.578 0.9205 702 640: 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.535 0.355 0.42 0.158 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 78/200 4.35G 2.923 1.564 0.9196 1217 640: 100% ━━━━━━━━━━━━ 6/6 3.5it/s 1.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.1it/s 1.0s all 42 11765 0.535 0.355 0.423 0.161 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 79/200 4.58G 2.968 1.577 0.9069 746 640: 100% ━━━━━━━━━━━━ 6/6 4.4it/s 1.4s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 2.8it/s 1.1s all 42 11765 0.535 0.355 0.423 0.161 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 80/200 4.66G 2.949 1.542 0.9118 850 640: 100% ━━━━━━━━━━━━ 6/6 4.0it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.527 0.356 0.415 0.153 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 81/200 4.67G 2.983 1.63 0.9153 651 640: 100% ━━━━━━━━━━━━ 6/6 3.4it/s 1.8s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.533 0.352 0.412 0.153 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 82/200 3.86G 3 1.576 0.9092 1078 640: 100% ━━━━━━━━━━━━ 6/6 4.1it/s 1.5s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.5it/s 0.9s all 42 11765 0.555 0.351 0.418 0.159 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 83/200 4.35G 2.919 1.55 0.9184 891 640: 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.3it/s 0.9s all 42 11765 0.555 0.351 0.418 0.159 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 84/200 5.06G 3.125 1.633 0.9047 1273 640: 100% ━━━━━━━━━━━━ 6/6 3.6it/s 1.7s Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 3/3 3.4it/s 0.9s all 42 11765 0.56 0.354 0.422 0.16 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 85/200 4.68G 3.032 1.623 0.9231 2638 640: 17% ━━────────── 1/6 1.2it/s 0.5s<4.0sWARNING CUDA OutOfMemoryError in TaskAlignedAssigner, using CPU 85/200 4.68G 3.032 1.623 0.9231 2638 640: 33% ━━━━──────── 2/6 0.9it/s 7.8s<4.4s Traceback (most recent call last): File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\tal.py", line 84, in forward return self._forward(pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt) File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\tal.py", line 111, in _forward mask_pos, align_metric, overlaps = self.get_pos_mask( File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\tal.py", line 146, in get_pos_mask mask_in_gts = self.select_candidates_in_gts(anc_points, gt_bboxes) File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\tal.py", line 298, in select_candidates_in_gts bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 1.45 GiB. GPU 0 has a total capacity of 6.00 GiB of which 0 bytes is free. Of the allocated memory 3.02 GiB is allocated by PyTorch, and 1. 10 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "D:\Anaconda3\envs\yolov8\Scripts\yolo-script.py", line 9, in <module> sys.exit(entrypoint()) File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\cfg\__init__.py", line 990, in entrypoint getattr(model, mode)(**overrides) # default args from model File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\engine\model.py", line 800, in train self.trainer.train() File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\engine\trainer.py", line 235, in train self._do_train() File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\engine\trainer.py", line 420, in _do_train loss, self.loss_items = unwrap_model(self.model).loss(batch, preds) File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\nn\tasks.py", line 339, in loss return self.criterion(preds, batch) File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\loss.py", line 272, in __call__ _, target_bboxes, target_scores, fg_mask, _ = self.assigner( File "D:\Anaconda3\envs\yolov8\lib\site-packages\torch\nn\modules\module.py", line 1739, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "D:\Anaconda3\envs\yolov8\lib\site-packages\torch\nn\modules\module.py", line 1750, in _call_impl return forward_call(*args, **kwargs) File "D:\Anaconda3\envs\yolov8\lib\site-packages\torch\utils\_contextlib.py", line 116, in decorate_context return func(*args, **kwargs) File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\tal.py", line 89, in forward result = self._forward(*cpu_tensors) File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\tal.py", line 111, in _forward mask_pos, align_metric, overlaps = self.get_pos_mask( File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\tal.py", line 146, in get_pos_mask mask_in_gts = self.select_candidates_in_gts(anc_points, gt_bboxes) File "D:\Anaconda3\envs\yolov8\lib\site-packages\ultralytics\utils\tal.py", line 298, in select_candidates_in_gts bbox_deltas = torch.cat((xy_centers[None] - lt, rb - xy_centers[None]), dim=2).view(bs, n_boxes, n_anchors, -1) RuntimeError: [enforce fail at alloc_cpu.cpp:115] data. DefaultCPUAllocator: not enough memory: you tried to allocate 1555814400 bytes.
最新发布
10-23
首先，用户的问题是：“我想解决使用YOLOv8进行目标检测训练时出现的CUDA out of memory错误 请问YOLOv8训练时出现CUDA out of memory错误的解决办法”。参考了一些站内引用，这些引用讨论了类似的内存不足问题。 ...
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0； 6.00 GiB total
得塔云的博客
05-24 5380
PyTorch提供了自动混合精度（Automatic Mixed Precision, AMP）支持，这可以通过使用较小的数据类型（如半精度浮点数）来减少内存使用，同时保持模型训练的准确性。：如果你有多个GPU，你可以使用PyTorch的DataParallel或DistributedDataParallel来在多个GPU上并行训练你的模型。但是，请注意这通常是PyTorch内部用于管理内存分配的参数，并且可能需要深入了解PyTorch的内存管理机制才能正确使用。：这是最常见的解决方法。
（超全方法）尝试解决问题：torch.cuda.OutOfMemoryError: CUDA out of memory.
qq_56438555的博客
11-28 3万+
提供多种方法解决CUDA内存不足（Out of Memory）的问题。
【已解决】torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 250.00 MiB.
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dont worry about it的博客
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报错：torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 250.00 MiB.
报错记录torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 90.00 MiB (GPU 0； 7.93 GiB to
gongkeguo的博客
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power的专栏
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遇到这个错误，按网上改小batch_size改的很小了，依然报错。错误原因是网络的输入输出shape不一致。后改小了网络结构，搞定。
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate XXX
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问题描述：torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 2.39 GiB. GPU 0 has a total capacty of 8.00 GiB of which 0 bytes is free. Of the allocated memory 9.50 GiB is allocated by PyTorch, and 2.24 GiB is reserved by PyTorch but unallocated.
Pycharm报错torch.cuda.OutOfMemoryError: CUDA out of memory.
Lu0v0Lu的博客
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做深度学习相关的实验，可以看到我的显卡内存很小（哭了，不过我有时候是在别的电脑上做的，那个电脑比这个好用），网上搜到的说的 max_split_size_mb:128 这个方法我贴到我代码上之后没有效果。因为我在这个电脑上做的是主实验后面的一些对比实验，也就是代码中很多张量很多数据是不需要的。所以抖了个机灵，把不需要的数据全都注释掉，代码正常运行，对比实验数据跑出来了耶！
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 128.00 MiB. GPU
06-07
这个错误提示表明在使用CUDA运算时，GPU的显存不足，无法分配128MB的显存。通常情况下，我们可以通过以下几种方式来解决这个问题： 1. 减少批次大小：可以减少每次训练的批次大小来降低显存使用量。 2. 减少模型...
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