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CentOS 7 steps to build a Linux GPU server for your reference. The details are as follows

1. Installation of CUDA Toolkit

Go to https://developer.nvidia.com/cuda-gpus to query the CUDA versions supported by GPU:

Go to https://developer.nvidia.com/cuda-downloads, download the corresponding CUDA Toolkit version according to the operating system, download a .run file, and run the file installation directly with the root user after the download is completed.

After the installation is complete. Run:

The copy code is as follows: nvidia-smi

If the GPU status information is listed, the installation is successful:

2. Installation of cuDNN

TensorFlow accelerates the neural network through the cuDNN library, so first go to https://developer.nvidia.com/rdp/cudnn-archive, download the corresponding version of cuDNN according to the version of CUDA, which is also a .run file, and run it directly after download.

3. Installation of TensorFlow

In order to avoid problems such as version conflicts during installation, it is recommended that you install Anoconda first. After downloading to https://www.anaconda.com/download/#linux, run the .sh file to install.

Then install TensorFlow using the following command:

Conda create-n tensorflow python=2.7source activate tensorflowexport TF_BINARY_URL= https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.11.0rc1-cp27-none-linux_x86_64.whlpip install-- ignore-installed-- upgrade $TF_BINARY_URL

Enter in turn:

Source activate tensorflowpythonimport tensorflow as tfimport pandas as pdtf.__version__

If no error is reported, the installation is successful:

The above is the whole content of this article, I hope it will be helpful to your study, and I also hope that you will support it.

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