| NVIDIA rtx3070显卡驱动、算力和cuda版本匹配关系 | 您所在的位置：网站首页 › 3070ti的算力 › NVIDIA rtx3070显卡驱动、算力和cuda版本匹配关系 |
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问题描述：3070装好显卡驱动，cuda之后无法使用，抛出异常：RuntimeError: CUDA error: no kernel image is available for execution on the device 原因：版本之间不匹配 我的显卡驱动： +-----------------------------------------------------------------------------+ | NVIDIA-SMI 470.103.01 Driver Version: 470.103.01 CUDA Version: 11.4 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 NVIDIA GeForce ... Off | 00000000:1A:00.0 Off | N/A | | 30% 35C P8 15W / 220W | 497MiB / 7982MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 1 NVIDIA GeForce ... Off | 00000000:3D:00.0 Off | N/A | | 30% 30C P8 18W / 220W | 8MiB / 7982MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 2 NVIDIA GeForce ... Off | 00000000:89:00.0 Off | N/A | | 30% 30C P8 20W / 220W | 8MiB / 7982MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 3 NVIDIA GeForce ... Off | 00000000:B2:00.0 Off | N/A | | 30% 31C P8 23W / 220W | 8MiB / 7982MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ 显卡算力查看：链接3070 算力：8.6 torch版本所需算力查看方法： import torch torch.cuda.get_arch_list()torch所需算力（用前面的方法试出来的） package pyton cuda cudnn architectures pytorch-1.0.0 py3.7 cuda10.0.130 cudnn7.4.1_1 sm_30, sm_35, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.0.0 py3.7 cuda8.0.61 cudnn7.1.2_1 sm_20, sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61 pytorch-1.0.0 py3.7 cuda9.0.176 cudnn7.4.1_1 sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61, sm_70 pytorch-1.0.1 py3.7 cuda10.0.130 cudnn7.4.2_0 sm_35, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.0.1 py3.7 cuda10.0.130 cudnn7.4.2_2 sm_35, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.0.1 py3.7 cuda8.0.61 cudnn7.1.2_0 sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61 pytorch-1.0.1 py3.7 cuda8.0.61 cudnn7.1.2_2 sm_35, sm_37, sm_50, sm_52, sm_53, sm_60, sm_61 pytorch-1.0.1 py3.7 cuda9.0.176 cudnn7.4.2_0 sm_35, sm_50, sm_60, sm_61, sm_70 pytorch-1.0.1 py3.7 cuda9.0.176 cudnn7.4.2_2 sm_35, sm_50, sm_60, sm_70 pytorch-1.1.0 py3.7 cuda10.0.130 cudnn7.5.1_0 sm_35, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.1.0 py3.7 cuda9.0.176 cudnn7.5.1_0 sm_35, sm_50, sm_60, sm_61, sm_70 pytorch-1.2.0 py3.7 cuda9.2.148 cudnn7.6.2_0 sm_35, sm_50, sm_60, sm_61, sm_70 pytorch-1.2.0 py3.7 cuda10.0.130 cudnn7.6.2_0 sm_35, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.2.0 py3.7 cuda9.2.148 cudnn7.6.2_0 sm_35, sm_50, sm_60, sm_61, sm_70 pytorch-1.3.0 py3.7 cuda10.0.130 cudnn7.6.3_0 sm_30, sm_35, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.3.0 py3.7 cuda10.1.243 cudnn7.6.3_0 sm_30, sm_35, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.3.0 py3.7 cuda9.2.148 cudnn7.6.3_0 sm_35, sm_50, sm_60, sm_61, sm_70 pytorch-1.3.1 py3.7 cuda10.0.130 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.3.1 py3.7 cuda10.1.243 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.3.1 py3.7 cuda9.2.148 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70 pytorch-1.4.0 py3.7 cuda10.0.130 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.4.0 py3.7 cuda10.1.243 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.4.0 py3.7 cuda9.2.148 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70 pytorch-1.5.0 py3.7 cuda10.1.243 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.5.0 py3.7 cuda10.2.89 cudnn7.6.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.5.0 py3.7 cuda9.2.148 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70 pytorch-1.5.1 py3.7 cuda10.1.243 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.5.1 py3.7 cuda10.2.89 cudnn7.6.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.5.1 py3.7 cuda9.2.148 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70 pytorch-1.6.0 py3.7 cuda10.1.243 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.6.0 py3.7 cuda10.2.89 cudnn7.6.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.6.0 py3.7 cuda9.2.148 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70 pytorch-1.7.0 py3.7 cuda10.1.243 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.7.0 py3.7 cuda10.2.89 cudnn7.6.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.7.0 py3.7 cuda11.0.221 cudnn8.0.3_0 sm_37, sm_50, sm_60, sm_61, sm_70, sm_75, sm_80 pytorch-1.7.0 py3.7 cuda9.2.148 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70 pytorch-1.7.1 py3.7 cuda10.1.243 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.7.1 py3.7 cuda10.2.89 cudnn7.6.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.7.1 py3.7 cuda11.0.221 cudnn8.0.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75, sm_80 pytorch-1.7.1 py3.7 cuda9.2.148 cudnn7.6.3_0 sm_37, sm_50, sm_60, sm_61, sm_70 pytorch-1.8.0 py3.7 cuda10.1 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.8.0 py3.7 cuda10.2 cudnn7.6.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.8.0 py3.7 cuda11.1 cudnn8.0.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75, sm_80, sm_86 pytorch-1.8.1 py3.7 cuda10.1 cudnn7.6.3_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.8.1 py3.7 cuda10.2 cudnn7.6.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75 pytorch-1.8.1 py3.7 cuda11.1 cudnn8.0.5_0 sm_35, sm_37, sm_50, sm_60, sm_61, sm_70, sm_75, sm_80, sm_86 结论：rtx 3070 需要安装cuda11以上（也许并不绝对，我猜的）references： https://blog.csdn.net/weixin_42642296/article/details/115598760https://developer.nvidia.com/zh-cn/cuda-gpus#compute±----------------------------------------------------------------------------+ | NVIDIA-SMI 470.103.01 Driver Version: 470.103.01 CUDA Version: 11.4 | |-------------------------------±---------------------±---------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=++==============| | 0 NVIDIA GeForce … Off | 00000000:1A:00.0 Off | N/A | | 30% 35C P8 15W / 220W | 497MiB / 7982MiB | 0% Default | | | | N/A | ±------------------------------±---------------------±---------------------+ | 1 NVIDIA GeForce … Off | 00000000:3D:00.0 Off | N/A | | 30% 30C P8 18W / 220W | 8MiB / 7982MiB | 0% Default | | | | N/A | ±------------------------------±---------------------±---------------------+ | 2 NVIDIA GeForce … Off | 00000000:89:00.0 Off | N/A | | 30% 30C P8 20W / 220W | 8MiB / 7982MiB | 0% Default | | | | N/A | ±------------------------------±---------------------±---------------------+ | 3 NVIDIA GeForce … Off | 00000000:B2:00.0 Off | N/A | | 30% 31C P8 23W / 220W | 8MiB / 7982MiB | 0% Default | | | | N/A | ±------------------------------±---------------------±---------------------+ |
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