背景：

在实验室的集群上，有个人账号，却没有sudo权限。（也不好意思要，怕以后出问题）

安装了conda来管理虚拟环境（本来用的virtualenv，但是后来发现还是conda好，因为conda进入虚拟环境之后仍然可以使用global的一些包和命令，但是virtualenv不行，是不能见到外部的，又没有sudo权限，不能随便安装包）。

问题来源：

一些经典老代码是tensoflow1.15 这个版本只能单独安装tensoflow-gpu 而且还要搭配cuda-10.0使用，然后集群机器上的版本是10.2，所以需要自己在虚拟环境下手动安装。

首先在stackoverflow上找到了一个方法：

https://stackoverflow.com/questions/39379792/install-cuda-without-root

Update The installation UI for 10.1 changed. The following works:

Deselect driver installation (pressing ENTERon it)
Change options -> root install path to a non-sudo directory.
Press A on the line marked with a + to access advanced options. Deselect create symbolic link, and change the toolkit install path.
Now installation should work without root permissions

Thank you very much for the hints in the question! I just want to complete it with an approach that worked for me, also inspired in this gist and that hopefully helps in situations where a valid driver is installed, and installing a more recent CUDA on Linux without root permissions is still needed.

TL;DR: Here are the steps to install CUDA9+CUDNN7 on Debian, and installing a pre-compiled version of TensorFlow1.4 on Python2.7 to test that everything works. Everything without root privileges and via terminal. Should also work for other CUDA, CUDNN, TensorFlow and Python versions on other Linux systems too.

INSTALLATION

Go to NVIDIA's official release web for CUDA (as for Nov. 2017, CUDA9 is out): https://developer.nvidia.com/cuda-downloads.
Under your Linux distro, select the runfile (local)option. Note that the sudo indication present in the installation instructions is deceiving, since it is possible to run this installer without root permissions. On a server, one easy way is to copy the <LINK> of the Download button and, in any location of your home directory, run wget <LINK>. It will download the <INSTALLER> file.
Run chmod +x <INSTALLER> to make it executable, and execute it ./<INSTALLER>.
accept the EULA, say no to driver installation, and enter a <CUDA> location under your home directory to install the toolkit and a <CUDASAMPLES> for the samples.
Not asked here but recommended: Download a compatible CUDNN file from the official web (you need to sign in). In my case, I downloaded the cudnn-9.0-linux-x64-v7.tgz, compatible with CUDA9 into the <CUDNN> folder. Uncompress it: tar -xzvf ....
Optional: compile the samples. cd <CUDASAMPLES> && make. There are some very nice examples there and a very good starting point to write some CUDA scripts of yourself.
(If you did 5.): Copy the required files from <CUDNN> into <CUDA>, and grant reading permission to user (not sure if needed):

cp -P <CUDNN>/cuda/include/cudnn.h <CUDA>/include/
cp -P <CUDNN>/cuda/lib64/libcudnn* <CUDA>/lib64
chmod a+r <CUDA>/include/cudnn.h <CUDA>/lib64/libcudnn*

Add the library to your environment. This is typically done adding this following two lines to your ~/.bashrc file (in this example, the <CUDA> directory was ~/cuda9/:

export PATH=<CUDA>/bin:$PATH
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<CUDA>/lib64/

然后按照这个一顿操作后在命令行python 进如，输入print(tf.test.is_gpu_available()) 结果是True. 但是运行我自己py文件的时候仍然找不到lib.so文件

很奇怪，懒得去找原因了

突然发现可以直接试试conda来安装

在虚拟环境下输入

conda install cudatoolkit==10.0

瞬间搞定。

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INSTALLATION

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