The shell scripts and the source code methods do not provide you with good dependency checking and can make your life difficult when TensorFlow refuses to work due to incompatible software and libraries. You can, however, still use the vendor provided shell scripts or compile the modules from the source code. Yum will handle all the dependency checks and make your life much easier to manage the package installation and upgrades. Whenever possible, use rpm and yum to perform the installation of packages and software modules. You can skip this step if you already have Python 3.6 and PIP 3.6 installed on your workstation. You do not want to use this version if you are going to use GPU. There is a CPU version of TensorFlow, named tensorflow. Version of TensorFlow that is going to be installed is tensorflow-gpu – the GPU enabled version of TensorFlow. conda, is not going to be used for setting up the TensorFlow programming environment you are welcome to use use Anaconda, however, please make sure that your installation of Anaconda does not interfere with the master installation of Python, PIP and TensorFlow.Ħ. Python is your programming language for TensorFlowĥ.Ěnaconda installer, i.e. Whenever possible, RPM or yum and PIP are the primary means used to install the required software and modulesĤ. For the list of CUDA compatible GPU devices, see ģ. The workstation has one or more of Nvidia CUDA compatible GPUs already installed in appropriate PCI-e slots in the chassis. The workstation you are going to use TensorFlow has CentOS 7 or Red Hat Enterprise Linux Workstation 7 Ubuntu is often the most often used examples on the Internet, at Brown, we tend to use Red Hat and CentOS for most work, especially if you require support from the CIS or the division Systems Manager.Ģ. In order to use TensorFlow on your workstation, there are a few assumptions and requirements.ġ.
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