Getting ready

The prerequisite for TensorFlow installation is that the system has Python 2.5 or higher installed. The recipes in this book have been designed for Python 3.5 (the Anaconda 3 distribution). To get ready for the installation of TensorFlow, first ensure that you have Anaconda installed. You can download and install Anaconda for Windows/macOS or Linux from https://www.continuum.io/downloads.

After installation, you can verify the installation using the following command in your terminal window:

conda --version

Once Anaconda is installed, we move to the next step, deciding whether to install TensorFlow CPU or GPU. While almost all computer machines support TensorFlow CPU, TensorFlow GPU can be installed only if the machine has an NVDIA® GPU card with CUDA compute capability 3.0 or higher (minimum NVDIA® GTX 650 for desktop PCs).

CPU versus GPU: Central Processing Unit (CPU) consists of a few cores (4-8) optimized for sequential serial processing. A Graphical Processing Unit (GPU) on the other hand has a massively parallel architecture consisting of thousands of smaller, more efficient cores (roughly in 1,000s) designed to handle multiple tasks simultaneously.

For TensorFlow GPU, it is imperative that CUDA toolkit 7.0 or greater is installed, proper NVDIA® drivers are installed, and cuDNN v3 or greater is installed. On Windows, additionally, certain DLL files are needed; one can either download the required DLL files or install Visual Studio C++. One more thing to remember is that cuDNN files are installed in a different directory. One needs to ensure that directory is in the system path. One can also alternatively copy the relevant files in CUDA library in the respective folders.