問題描述
當我通過 Conda 安裝 tensorflow-gpu 時；它給了我以下輸出:
When I install tensorflow-gpu through Conda; it gives me the following output:
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
我看到安裝 tensorflow-gpu 會自動觸發 cudatoolkit 和 cudnn 的安裝.這是否意味著我不再需要手動安裝 CUDA 和 CUDNN 才能使用 tensorflow-gpu?這個 CUDA 的 conda 安裝在哪里?
I see that installing tensorflow-gpu automatically triggers the installation of the cudatoolkit and cudnn. Does this mean that I no longer need to install CUDA and CUDNN manually anymore to be able to use tensorflow-gpu? Where does this conda installation of CUDA reside?
我首先以舊方式安裝了 CUDA 和 CuDNN(例如，按照以下安裝說明進行操作:https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
I first installed CUDA and CuDNN the old way (e.g. by following these installation instructions: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html )
然后我注意到 tensorflow-gpu 也在安裝 cuda 和 cudnn
And then I noticed that tensorflow-gpu was also installing cuda and cudnn
我現在是否安裝了兩個版本的 CUDA/CuDNN?如何檢查?
推薦答案
我現在是否安裝了兩個版本的 CUDA?如何檢查?
Do i now have two versions of CUDA installed and how do I check this?
沒有.
conda 安裝支持它們提供的 CUDA 加速包所需的最少的可再發行庫組件.包名 cudatoolkit 完全用詞不當.這不是那種事.盡管現在它的范圍比以前大大擴展了(字面意思是 5 個文件——我認為在某些時候他們一定已經從 NVIDIA 獲得了許可協議，因為其中一些不是/不在官方的"自由再分發"列表 AFAIK)，它基本上仍然只是少數幾個庫.
conda installs the bare minimum redistributable library components required to support the CUDA accelerated packages they offer. The package name cudatoolkit is a complete misnomer. It is nothing of the sort. Even though it is now greatly expanded in scope from what it used to be (literally 5 files -- I think at some point they must have gotten a licensing deal from NVIDIA because some of this wasn't/isn't on the official "freely redistributable" list AFAIK), it still is basically just a handful of libraries.
你可以自己檢查一下:
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(記住上面的大多數文件"只是符號鏈接)
i.e. what you get is (keeping in mind most of those "files" above are just symlinks)
- CUBLAS 運行時
- CUDA 運行時庫
- CUFFT 運行時
- CU 和運行時
- CUsparse rutime
- CUsolver 運行時
- NPP 運行時
- nvblas 運行時
- NVTX 運行時
- NVgraph 運行時
- NVjpeg 運行時
- NVRTC/NVVM 運行時
conda 安裝的 CUDNN 包是可再分發的二進制分發包，它與 NVIDIA 分發的包完全相同，即兩個文件，一個頭文件和一個庫.
The CUDNN package that conda installs is the redistributable binary distribution which is identical to what NVIDIA distribute -- which is exactly two files, a header file and a library.
您仍然需要安裝受支持的 NVIDIA 驅動程序才能使 conda 安裝的 tensorflow 工作.
You would still require a supported NVIDIA driver installation to make the tensorflow which conda installs work.
如果您想真正編譯和構建 CUDA 代碼，您需要安裝一個單獨的 CUDA 工具包，其中包含 conda 故意從其發行版中省略的所有開發組件.
If you want to actually compile and build CUDA code, you need to install a separate CUDA toolkit which contains all the the development components which conda deliberately omits from their distribution.
這篇關于在使用 conda tensorflow-gpu 包之前是否還需要安裝 CUDA?的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！