Once activated, the virtual environment’s bin directory will be added at the beginning of the $PATH variable To start using this virtual environment, you need to activate it by running the activate script: source venv/bin/activate You can use any name you want for the virtual environment. , the standard Python library and other supporting files. The command above creates a directory named venv, which contains a copy of the Python binary, the Pip package manager

Within the directory, run the following command to create the virtual environment: python3 -m venv venv Into it: mkdir tensorflow_project cd tensorflow_project It can be your home directory or any other directory where the user has read and write permissions. Navigate to the directory where you would like to store your TensorFlow project. To access Python 3.6 you need to launch a new shell instance using the scl tool: scl enable rh-python36 bash Starting from Python 3.6, the recommended way to create a virtual environment is to use the venv module.

We are now ready to create a virtual environment for our TensorFlow project. Once done install Python 3.6 by running the following command: sudo yum install rh-python36 To enable the repository, install the SCL release file: sudo yum install centos-release-scl SCL will allow you to install newer versions of python 3.x alongside the default python v2.7.5 so that system tools such as yum will continue to work properly. Installing Python 3 #ģ.6 from the Software Collections (SCL) repositories.ĬentOS 7 ships with Python 2.7.5 which is a critical part of the CentOS base system. This way you can have multiple different isolated Python environments on a single computer and install a specific version of a module on a per project basis without worrying that it will affect your other Projects. We will be using Python 3 and install TensorFlow inside a virtual environment. TensorFlow can be installed system-wide, in a Python virtual environment, as a Docker This tutorial will walk you through how to install TensorFlow on CentOS 7. It is used by a number of organizations including Twitter, PayPal, Intel, Lenovo, and Airbus. Is a free and open-source platform for building machine learning models developed by Google.

How to install glibc on centos