Linux x86_64 Driver Version Windows x86_64 Driver Version CUDA 11.4 Update 1 >=470.57.02 >=471.41 CUDA 11.4.0 GA >=470.42.01 >=471.11 CUDA 11.3.1 Update 1 >=465.19.01 >=465.89 CUDA 11.3.0 GA >=465.19.01 >=465.89 CUDA 11.2.2 Update 2 >=460.32.03 >=461.33 CUDA 11.2.1 Update ...
$ cat /proc/driver/nvidia/versionnvcc -V$nvidia-smi有输出则成功。 3. 安装cudnn6.0 tar zxvf cudnn-8.0-linux-x64-v6.0.tgz解压后有个cuda文件,内有include和lib64两个文件夹,进入include文件夹,执行如下命令复制头文件: sudo cp cuda/include/cudnn.h /usr/local/cuda/include/ sudo cp cuda/lib64/...
创建虚拟环境:输入 conda create -n 环境名字(我这里输入的是cuda11.1) python=3.7 出现提示后按y确认,安装完成后输入activate cuda11.1(你的环境名字)进入虚拟环境,可以看到左边括号里从base变成了环境名,说明已经成功激活,其他相关命令请自行百度。 安装tensorflow,由于正式版tensorflow不支持cuda11.1,所以要安装tf-nig...
Table 1. CUDA Toolkit and Compatible Driver Versions Google一下发现是tensorflow1.5.0版本只支持cuda9.0 I downgrade to tensorflow version 1.4.0 and keras version 2.0.8. 否则版本运行有错: https:///keras-team/keras/issues/9621 如何查看CUDA版本和CUDNN版本 cuda一般安装在 /usr/local/cuda/ 路径下,...
安装tensorflow -> CUDA 安装cuda 安装前准备 确认有一个支持CUDA的GPU $ lspci|grep -i nvidia# output# 01:00.0 VGA compatible controller: NVIDIA Corporation Device 1b81 (rev a1)# 01:00.1 Audio device: NVIDIA Corporation Device 10f0 (rev a1)...
推荐的cuda版本是10.0,cudnn的版本是 7.4.1.5 cuda10.0官网的地址是:https://developer.nvidia.com/cuda-10.0-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal cudnn官网的地址是:(需要注册登录 ):https://developer.nvidia.com/cudnn ...
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || (CUDART_VERSION >= 8000) 安装完tensorflow的时候出现AttributeError: type object 'NewBase' has no attribute 'is_abstract',原因是six没有更新 I solved this problem on mac osx doing this : ...
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device(/gpu:0) -> (device:0, name: Tesla K20m, pci bus id:0000:02:00.0) E tensorflow/stream_executor/cuda/cuda_dnn.cc:347] Loaded runtime CuDNN library: 6022(compatibility version5000) but source was compiled ...
Installing an earlier version of gcc is a possible solution. But since you haven’t said what the problem is, it’s difficult to say. Maybe all you need to do is make sure your version of gcc-6 is compatible with CUDA 9.0, and make sure it is selected. It may not even matter if...
@attalurisTensorFlow[and-cuda] 2.15.0/2.15.1 is likely not compatible with jax[cuda12]. There's a version mismatch with respect to the NVIDIA NCCL library, a component needed for GPU support in both TensorFlow and JAX. TensorFlow 2.15.0/2.15.1 might depend on an older NCCL version (e...