Publications - Computer Vision Laboratory
: Hur får jag en nvidia-drivrutin i min docker ubuntu-bild? - Androidnetc
问题. 在服务器上安装mxne的GPU版本 sudo pip install mxnet-cu80==1.2. Jul 23, 2019 Cuda runtime error 59. If you happen to run into this error, you know how frustrating it can be.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link: mxnet.base.MXNetError: [14:40:28] src/storage/storage.cc:119: Check failed: e == cudaSuccess || e == cudaErrorCudartUnloading CUDA: CUDA driver version is insufficient for CUDA runtime version. 原因. CUDA版本对显卡驱动版本有要求，见如下链接。 https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html Re: FATAL ERROR: CUDA error in cudaGetDeviceCount on Pe 0 (thomasASUS): CUDA driver version is insufficient for CUDA runtime version. From: Thomas Evangelidis CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: “GeForce GTX 980M” CUDA Driver Version / Runtime Version 10.0 / 10.0 CUDA Capability Major/Minor version number: 5.2 Total amount of global memory: 8127 MBytes (8521711616 bytes) 1 tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version CUDA driver version is insufficient for CUDA runtime version - code 35: Failed getting devices info MikeHersee April 2019 edited April 2019 in DualSPHysics v4.4 配置ubuntu17.1+CUDA9.2的caffe环境，CUDA sample编译完成，执行到./deviceQuery时报错:CUDA driver version is insufficient CUDA Device Query (Runtime API) version (CUDART static linking) cudaGetDeviceCount returned 35 -> CUDA driver version is insufficient for CUDA runtime version Result = FAIL とエラーを吐いてしまいました． I have a GeForce GTX950M and I want to use cuda. I tried installing cuda 8 and with drives 375, but it does not work. I think the problem is in compatibility.
I think there might be a mismatch between the some of the card drivers and cuda libraries between the host and container. CUDA driver version is insufficient for CUDA runtime version 简单的讲, cuda驱动版本和cuda库的版本不一致. 常见错误场景: cuda 驱动最高支持 cuda 90的库, 如果用 cuda 91的库, 会出现这种情况 两种 解决 思路: 升级 cuda 驱动 降低 cuda 91库为 cuda 90 建议选择第二种 解决 方案.
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I'd really appreciate the help. CUDA driver version is insufficient for CUDA runtime version，翻译过来就是CUDA的驱动程序版本跟CUDA的运行时版本不匹配！ 1.CUDA driver version（驱动版本）：就是NVIDIA GPU的驱动程序版本； 查看命令：nvidia-smi RuntimeError: cuda runtime error (35) : CUDA driver version is insufficient for CUDA runtime version at /opt/conda/conda-bld/pytorch_1524584710464/work/aten/src/THC/THCGeneral.cpp:70.
Unable to start trainig - darknet - gitMemory :
Yes, you'd better use CUDA 6.5 instead of 7.0, because currennt don't support the higher version . Hopes it helps! If you would like to refer to this comment somewhere else in this project, copy and paste the following link: mxnet.base.MXNetError: [14:40:28] src/storage/storage.cc:119: Check failed: e == cudaSuccess || e == cudaErrorCudartUnloading CUDA: CUDA driver version is insufficient for CUDA runtime version. 原因. CUDA版本对显卡驱动版本有要求，见如下链接。 https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html Re: FATAL ERROR: CUDA error in cudaGetDeviceCount on Pe 0 (thomasASUS): CUDA driver version is insufficient for CUDA runtime version. From: Thomas Evangelidis CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: “GeForce GTX 980M” CUDA Driver Version / Runtime Version 10.0 / 10.0 CUDA Capability Major/Minor version number: 5.2 Total amount of global memory: 8127 MBytes (8521711616 bytes) 1 tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed.
2018-09-15 18:56:51.011724: E tensorflow/core/common_runtime/direct_session.cc:158] Internal: cudaGetDevice () failed. Status: CUDA driver version is insufficient for CUDA runtime version Traceback (most recent call last): File "evaluate_sample.py", line 160, in
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The other half is the Compute Capability. A GPU will support a specific compute version, and if your CUDA version minimum compute capability is higher than that supported by your GPU, your code will not compile or run.
Which cuda version should I use?
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gdev/cuda_runtime.h at master · shinpei0208/gdev · GitHub
This quick tip by alexjoaofl may help!