在使用conda tensorflow-gpu软件包之前是否仍然需要安装CUDA？

Question

当我通过Conda安装tensorflow-gpu时; 它给了我以下输出：

conda install tensorflow-gpu
Collecting package metadata (current_repodata.json): done
Solving environment: done


## Package Plan ##

  environment location: /home/psychotechnopath/anaconda3/envs/DeepLearning3.6

  added / updated specs:
    - tensorflow-gpu


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    _tflow_select-2.1.0        |              gpu           2 KB
    cudatoolkit-10.1.243       |       h6bb024c_0       347.4 MB
    cudnn-7.6.5                |       cuda10.1_0       179.9 MB
    cupti-10.1.168             |                0         1.4 MB
    tensorflow-2.1.0           |gpu_py36h2e5cdaa_0           4 KB
    tensorflow-base-2.1.0      |gpu_py36h6c5654b_0       155.9 MB
    tensorflow-gpu-2.1.0       |       h0d30ee6_0           3 KB
    ------------------------------------------------------------
                                           Total:       684.7 MB

The following NEW packages will be INSTALLED:

  cudatoolkit        pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0
  cudnn              pkgs/main/linux-64::cudnn-7.6.5-cuda10.1_0
  cupti              pkgs/main/linux-64::cupti-10.1.168-0
  tensorflow-gpu     pkgs/main/linux-64::tensorflow-gpu-2.1.0-h0d30ee6_0

我看到安装tensorflow-
gpu会自动触发cudatoolkit和cudnn的安装。这是否意味着我不再需要手动安装CUDA和CUDNN即可使用tensorflow-
gpu？此CUDA的conda安装在哪里？

我首先以旧方式安装了CUDA和CuDNN（例如，按照以下安装说明进行操作：https ://docs.nvidia.com/cuda/cuda-
installation-guide-linux/index.html ）

然后我注意到tensorflow-gpu也在安装cuda和cudnn

我现在是否已安装两个版本的CUDA / CuDNN，该如何检查？

Answer 1

我现在是否已安装两个版本的CUDA，该如何检查？

没有。

conda将安装支持其提供的CUDA加速软件包所需的最少可再发行库组件。软件包名称cudatoolkit是完整的误称。这不是什么。即使现在它的范围已从以前的范围（从原来的5个文件大大扩展了，我认为在某些时候他们也一定已经从NVIDIA获得了许可协议，因为其中一些不在/不在官方的“自由重新分发”列表AFAIK），它基本上仍然只是少数几个库。

您可以自己检查：

cat /opt/miniconda3/conda-meta/cudatoolkit-10.1.168-0.json 
{
  "build": "0",
  "build_number": 0,
  "channel": "https://repo.anaconda.com/pkgs/main/linux-64",
  "constrains": [],
  "depends": [],
  "extracted_package_dir": "/opt/miniconda3/pkgs/cudatoolkit-10.1.168-0",
  "features": "",
  "files": [
    "lib/cudatoolkit_config.yaml",
    "lib/libcublas.so",
    "lib/libcublas.so.10",
    "lib/libcublas.so.10.2.0.168",
    "lib/libcublasLt.so",
    "lib/libcublasLt.so.10",
    "lib/libcublasLt.so.10.2.0.168",
    "lib/libcudart.so",
    "lib/libcudart.so.10.1",
    "lib/libcudart.so.10.1.168",
    "lib/libcufft.so",
    "lib/libcufft.so.10",
    "lib/libcufft.so.10.1.168",
    "lib/libcufftw.so",
    "lib/libcufftw.so.10",
    "lib/libcufftw.so.10.1.168",
    "lib/libcurand.so",
    "lib/libcurand.so.10",
    "lib/libcurand.so.10.1.168",
    "lib/libcusolver.so",
    "lib/libcusolver.so.10",
    "lib/libcusolver.so.10.1.168",
    "lib/libcusparse.so",
    "lib/libcusparse.so.10",
    "lib/libcusparse.so.10.1.168",
    "lib/libdevice.10.bc",
    "lib/libnppc.so",
    "lib/libnppc.so.10",
    "lib/libnppc.so.10.1.168",
    "lib/libnppial.so",
    "lib/libnppial.so.10",
    "lib/libnppial.so.10.1.168",
    "lib/libnppicc.so",
    "lib/libnppicc.so.10",
    "lib/libnppicc.so.10.1.168",
    "lib/libnppicom.so",
    "lib/libnppicom.so.10",
    "lib/libnppicom.so.10.1.168",
    "lib/libnppidei.so",
    "lib/libnppidei.so.10",
    "lib/libnppidei.so.10.1.168",
    "lib/libnppif.so",
    "lib/libnppif.so.10",
    "lib/libnppif.so.10.1.168",
    "lib/libnppig.so",
    "lib/libnppig.so.10",
    "lib/libnppig.so.10.1.168",
    "lib/libnppim.so",
    "lib/libnppim.so.10",
    "lib/libnppim.so.10.1.168",
    "lib/libnppist.so",
    "lib/libnppist.so.10",
    "lib/libnppist.so.10.1.168",
    "lib/libnppisu.so",
    "lib/libnppisu.so.10",
    "lib/libnppisu.so.10.1.168",
    "lib/libnppitc.so",
    "lib/libnppitc.so.10",
    "lib/libnppitc.so.10.1.168",
    "lib/libnpps.so",
    "lib/libnpps.so.10",
    "lib/libnpps.so.10.1.168",
    "lib/libnvToolsExt.so",
    "lib/libnvToolsExt.so.1",
    "lib/libnvToolsExt.so.1.0.0",
    "lib/libnvblas.so",
    "lib/libnvblas.so.10",
    "lib/libnvblas.so.10.2.0.168",
    "lib/libnvgraph.so",
    "lib/libnvgraph.so.10",
    "lib/libnvgraph.so.10.1.168",
    "lib/libnvjpeg.so",
    "lib/libnvjpeg.so.10",
    "lib/libnvjpeg.so.10.1.168",
    "lib/libnvrtc-builtins.so",
    "lib/libnvrtc-builtins.so.10.1",
    "lib/libnvrtc-builtins.so.10.1.168",
    "lib/libnvrtc.so",
    "lib/libnvrtc.so.10.1",
    "lib/libnvrtc.so.10.1.168",
    "lib/libnvvm.so",
    "lib/libnvvm.so.3",
    "lib/libnvvm.so.3.3.0"
  ]

  .....

即您得到的是（记住上面的那些“文件”大部分只是符号链接）

• CUBLAS运行时
• CUDA运行时库
• CUFFT运行时
• CUrand运行时
• 稀疏的rutime
• CUsolver运行时
• NPP运行时
• nvblas运行时
• NVTX运行时
• NVgraph运行时
• NVjpeg运行时
• NVRTC / NVVM运行时

conda安装的CUDNN软件包是可再发行的二进制发行版，与NVIDIA发行的二进制发行版完全相同-正好是两个文件，一个头文件和一个库。

您仍然需要安装受支持的NVIDIA驱动程序，才能使conda安装的tensorflow正常工作。