![]() Once activated, your terminal prompt will reflect that you are in the virtual environment: ( $Īt this point you can install TensorFlow in your virtual environment. It also includes pip and a standalone version of Python. This creates a new tensorflow-dev directory which will contain all of the packages that you install while this environment is activated. Run the following command to create the environment: Then create a new virtual environment called tensorflow-dev, for instance. Navigate to your newly created tf-demo directory: We’ll call it tf-demo for demonstration purposes, but choose a directory name that is meaningful to you: ![]() If you already have a clean programming environment set up, feel free to skip this step.įirst, create a project directory. In this step, we’ll create a virtual environment in order to install TensorFlow into it without compromising our other programming projects. ![]() Step 1 - Creating a Programming Environment Follow How To Install Python 3 on Ubuntu 20.04 to configure Python and virtualenv. Python 3.8 or higher and virtualenv installed. One Ubuntu 20.04 server with at least 4GB of RAM set up by following the Ubuntu 20.04 initial server setup guide, including a sudo non-root user and a firewall. Prerequisitesīefore you begin this tutorial, you’ll need the following: Once you complete the installation, you’ll validate your installation by importing Tensorflow to ensure you have no errors. This approach isolates the TensorFlow installation and gets things up and running quickly. In this tutorial, you’ll install TensorFlow in a Python virtual environment with virtualenv. These multi-dimensional arrays are commonly known as “tensors,” hence the name TensorFlow. Expressed in the form of stateful dataflow graphs, each node in the graph represents the operations performed by neural networks on multi-dimensional arrays. An open-source machine learning software library, TensorFlow is used to train neural networks.
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