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In the serie, “How to use GPU with Tensorflow 1.8 and CUDA 9.2”, we are now in the third phase. This step is related to the compilation of Tensorflow 1.8 by including the library CUDA. This compilation will enable tensorflow to use the GPU core of the graphical card (384 cores). TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA GPU and you need to run performance-critical applications, you should ultimately build and install this version. In the previous sections, we already installed the last driver of the graphical card (MX150), the  library CUDNN, and CUPTI to prepare the laptop for the compilation of Tensorflow 1.8. We also check if the library is correctly installed by using the samples available in the package of CUDA.

The compilation of Tensorflow is not simple. So, I recommend to reserve around 2 hours to make this task. The compilation of the software is structured into 4 steps:

• Step 1: Download the required packages in the Ubuntu repository
• Step 2: Installation of Blazel. Blazel is an open-source build and test tool similar to Make, Maven, and Gradle.
• Step 3: Compilation of Tensorflow from the source.
• Step 4: Creation of the package whl. This package will be deployed in the phase 4.

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