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npu-smi info 显示 910ProB
如果是软件比如cann torch torchnpu配套问题,那么哪里有文档可以查看。感谢!
测试数据集:ultralytics野生动物数据集
脚本:参数设置得比较保守,只要硬件能调用,这些可以慢慢调(跟着这里做:https://gitcode.com/Ascend/modelzoo-GPL/tree/master/built-in/PyTorch/Official/cv/object_detection/Yolov8_for_PyTorch)
from ultralytics import YOLO import argparse import os import sys from pathlib import Path import torch import torch_npu from torch_npu.contrib import transfer_to_npu torch.npu.config.allow_internal_format = False torch.npu.set_compile_mode(jit_compile=False) FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv8 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative if __name__ == "__main__": # Load a model model = YOLO(ROOT / 'ultralytics/cfg/models/v8/yolov8.yaml').load('yolov8n.pt') # Train the model model.train( data='./wildlife/data.yaml', epochs=100, imgsz=640, batch=4, device=0, workers=2, )
# 容器启动命令: docker run -itd \ --name ultralytics-server \ --net=host \ --shm-size=10g \ --privileged=true \ --device /dev/davinci0 \ --device /dev/davinci1 \ --device /dev/davinci_manager \ --device /dev/devmm_svm \ --device /dev/hisi_hdc \ -v /usr/local/dcmi:/usr/local/dcmi \ -v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \ -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \ -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \ -v /etc/ascend_install.info:/etc/ascend_install.info \ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver \ -v /usr/local/Ascend/add-ons:/usr/local/Ascend/add-ons \ -v /data:/data \ swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.3.rc1-910-ubuntu22.04-py3.11 \ bash # 替换容器内ubuntu的apt源 cp /etc/apt/sources.list /etc/apt/sources.list.bak sed -i 's#http://ports.ubuntu.com/#http://mirrors.aliyun.com/#g' /etc/apt/sources.list cat /etc/apt/sources.list apt-get update #安装系统依赖 apt-get install cmake gcc g++ libgtk2.0-dev libgtk-3-dev libpng-dev libjpeg-dev libopenexr-dev libtiff-dev libwebp-dev unzip vim tree 按照这个装ultralytics可以调用NPU跑起来,速度很快:https://gitcode.com/Ascend/modelzoo-GPL/tree/master/built-in/PyTorch/Official/cv/object_detection/Yolov8_for_PyTorch
本帖最后由 匿名用户 于 2026/08/30 16:01:20 编辑
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卡:Atlas 300T Pro
npu-smi info 显示 910ProB
1.目前装上ultralytics之后总是报错,开始训练就会一直卡住。虽然加载了npu,但是训练没有用到npu。
2.使用mindyolo能训练,但是因为不熟悉,导致使用不是很方便。而且预测推理还是有问题,比较麻烦。
如果是软件比如cann torch torchnpu配套问题,那么哪里有文档可以查看。感谢!
已经解决了,感谢各位。
测试数据集:ultralytics野生动物数据集
脚本:参数设置得比较保守,只要硬件能调用,这些可以慢慢调(跟着这里做:https://gitcode.com/Ascend/modelzoo-GPL/tree/master/built-in/PyTorch/Official/cv/object_detection/Yolov8_for_PyTorch)
from ultralytics import YOLO import argparse import os import sys from pathlib import Path import torch import torch_npu from torch_npu.contrib import transfer_to_npu torch.npu.config.allow_internal_format = False torch.npu.set_compile_mode(jit_compile=False) FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv8 root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative if __name__ == "__main__": # Load a model model = YOLO(ROOT / 'ultralytics/cfg/models/v8/yolov8.yaml').load('yolov8n.pt') # Train the model model.train( data='./wildlife/data.yaml', epochs=100, imgsz=640, batch=4, device=0, workers=2, )镜像:swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.3.rc1-910-ubuntu22.04-py3.11 主机驱动已装好