How install tensorflow with GPU

Requirements to run TensorFlow with GPU support
If you are installing TensorFlow with GPU support using one of the mechanisms described in this guide, then the following NVIDIA software must be installed on your system:

CUDA® Toolkit 9.0. For details, see NVIDIA’s documentation Ensure that you append the relevant Cuda pathnames to the %PATH% environment variable as described in the NVIDIA documentation.
The NVIDIA drivers associated with CUDA Toolkit 9.0.
cuDNN v7.0. For details, see NVIDIA’s documentation. Note that cuDNN is typically installed in a different location from the other CUDA DLLs. Ensure that you add the directory where you installed the cuDNN DLL to your %PATH% environment variable.
GPU card with CUDA Compute Capability 3.0 or higher for building from source and 3.5 or higher for our binaries. See NVIDIA documentation for a list of supported GPU cards.
If you have a different version of one of the preceding packages, please change to the specified versions. In particular, the cuDNN version must match exactly: TensorFlow will not load if it cannot find cuDNN64_7.dll. To use a different version of cuDNN, you must build from source.

My GPU is Quadro 1000M, i do not know Compute Capability. Would everybody check Compute Capability for me.

Well, for me the ngc.nvidia.com method appears the most straightforward for Tensorflow with GPU support use;
However , if your GPU doesn’t support the containers you just install tensorflow-gpu wheel or follow the guides.
The capability ,as far as I know are somewhere stated in a specification table, otherwise could be googled.

According to the page https://en.wikipedia.org/wiki/Nvidia_Quadro, it appears to have 3.0 compute coefficient for K version and 2.1 without K in the name of the gpu.

Quadro K1000M[207] 2012-06-01 GK107GL 28 PCI-E 3.0 ×16 850 850 1800 192:16:16:1 3.4 13.6 2048 28.8 DDR3 128 326 1/24 of SP 11.2 4.6 3.0 1.2 45