TensorFlow is one among the leading frameworks for deep learning and machine learning, and as of 2017. It is now integrating for Intel® Xeon Phi™ processors.
Here are the steps provided to provide a quick overview of the installation process in distinct operating systems using Python dependencies.
Step1: A 64-bit Python 3.5.x window is the only version that TensorFlow supports.
Head over to Python 3.5.x from python.org
Step 2: Select the 3.5.2 download from downloads
Step 3: Select either the x86-64 or amd64 installer like Windows x86-64 executable installer
Step 4: Choose Add Python 3.5 to PATH
Step 5: Now a message appears as “Setup was successful.”
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Open Command Prompt and check the version to confirm that the installation is successful.
Start with installing Homebrew (brew), which makes it easy to install a large number of different packages. Installing brew is simple:
Now, install Python 2
brew install python
Now install pip using the following command:
sudo apt-get install python-setuptools
sudo easy_install pip
Go to finder and create a folder to install everything. As soon as you create your virtual environment, a few folders should appear inside your folder.
virtualenv –system-site-packages SOME_PATH/SOME_FOLDER
Instead of entering the whole folder path every time, macOS provides a current directory with the “cd” command. Simply type “cd” with space at the end and hit enter.
After that go to finder, and then drag the folder to your terminal window that enables you to paste the absolute folder path to the end of your input.
Now activate the virtual environment that you have installed on the folder
Now download the binary files from Google to install TensorFlow
sudo pip install –upgrade $TF_BINARY_URL
With this, installing Tensorflow on macOs is successful.
Before installing TensorFlow on Ubuntu, some prerequisites are required as follows:
An Ubuntu 16.04 server with 1GB of RAM, following Ubuntu 16.04 initial server setup guide, a sudo non-root user and at the firewall.
Step 1: Installing TensorFlow,
Create a project-directory tf-demo
$ mkdir ~/tf-demo
Navigate to the newly created directory
$ cd ~/tf-demo
With this, a new tensorflow-dev directory that contains the packages you install while the environment is activated, including a standalone version of Python and pip.
Now, install TensorFlow in your virtual environment.
To install and upgrade TensorFlow newest version in PyPi, run the following command
(tensorflow-dev) $pip3 install --upgrade tensorflow
After successful installation, the output will be:
With this, the TensorFlow installation is completed. Make sure that it is working.
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Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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