使用conda tensorflow-gpu包之前是否还需要安装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 才能使用 tensorflow-gpu？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

我现在是否安装了两个版本的 CUDA/CuDNN，我该如何检查？

Answer 1

我现在是否安装了两个版本的 CUDA，我该如何检查？

不。

conda 安装了支持它们提供的 CUDA 加速包所需的最低限度的可再发行库组件。包名称cudatoolkit完全是用词不当。它不是那种东西。尽管现在它的范围比以前有了很大的扩展（字面意思是 5 个文件——我认为在某些时候他们一定已经从 NVIDIA 获得了许可协议，因为其中一些不是/不在官方文件中）可自由再发行”列表 AFAIK），它基本上仍然只是少数几个库。

你可以自己检查一下：

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"
  ]

  .....

即你得到的是（记住上面的大多数“文件”只是符号链接）

• CUBLAS 运行时
• CUDA 运行时库
• CUFFT 运行时
• CUrand运行时
• CUsparse rutime
• CUsolver 运行时
• 核电厂运行时
• 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 包是可再发行的二进制发行版，它与 NVIDIA 发行的发行版完全相同——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。