How to Install Tensorflow 1.4.0

How to Install Tensorflow 1.4.0

This tutorial explains how to install Tensorflow 1.4.0 for CPU and GPU. Tensorflow is an open source software library for machine learning, developed by Google Brain Team.

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TensorFlow is an open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

Why TensorFlow?

TensorFlow is an open-source software library for machine learning. It was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

TensorFlow 1.4.0 was released on February 11, 2017. Notable changes in this version include:

– GPU support for Windows
– Support for Java 8
– Improved model performance with faster than ever XLA compilation
– New TensorBoard features including a profile plugin and visual debugger


Before starting the installation process, it is important to note that TensorFlow 1.4.0 is only compatible with Python 3.5 and 3.6. If you are using a different version of Python, you will need to install TensorFlow 1.3.0 instead.

With that said, let’s get started. The first thing you’ll need to do is install the following dependencies:

-Python 3.5 or 3.6
-pip 19 or higher
-virtualenv 15 or higher

Once you have all of the dependencies installed, you can create a virtual environment for TensorFlow 1.4.0 by running the following command:

virtualenv – system-site-packages -p python3 tensorflow1.4

This will create a folder called “tensorflow1.4” in your current working directory that contains a copy of Python 3 and all of the necessary libraries for TensorFlow 1.4 (including pip).

Next, you’ll need to activate the virtual environment by running the following command:

source tensorflow1/bin/activate # If using bash
source tensorflow1/bin/activate # If using fish shell


Before you begin, ensure you have met the following requirements:
You have installed the latest version of pip.
You are using Python 2.7 or 3.4+.
You installed CUDA 8.0 and CUDNN 6.0 (see TensorFlow GPU Support).If you are not sure whether you have these prerequisites installed, TensorFlow provides a script to check if you have the right versions installed.
To run the script, use the following command:
$ python tensorflow/tensorflow/python/tools/

Creating a TensorFlow environment

TensorFlow 1.4.0 has recently been released, and there are a few ways to install it. The easiest way is to use an existing TensorFlow environment, such as Anaconda, which already has TensorFlow 1.4.0 installed. However, you can also create your own TensorFlow environment. This tutorial will show you how to install TensorFlow 1.4 in a new environment.

First, you will need to download and install Anaconda 3.6 (or another Python 3 distribution). Once Anaconda is installed, open a new terminal window and create a new environment with the following command:

$ conda create -n tensorflow1.4 python=3.6

This will create a new environment called tensorflow1.4 with Python 3.6 installed. You can now activate this environment with the following command:

$ source activate tensorflow1.4 # If using Mac/Linux
$ activate tensorflow1.4 # If using Windows

Once the environment is activated, you can install TensorFlow 1.4 with the following command:
$ pip install – ignore-installed – upgrade https://storage_url/tensorflow-1_dot_4_dot_0-cp36-cp36m-linux_x86_64 dot whl

Installing TensorFlow

TensorFlow is an open source software library for machine learning, developed by Google Brain Team. The 1.4.0 version was released on February 11, 2017.

In this guide, we will show you how to install TensorFlow 1.4.0 on Ubuntu 16.04 LTS.

### Prerequisites

Before continuing with this guide, make sure you are logged in as a user with sudo privileges. All the commands in this tutorial should be run as a non-root user.

To check if you are a sudo user, run this command:
$ sudo -l
You should see this output:

Matching Defaults entries for sammy on this host:
env_reset, mail_badpass, secure_path=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/snap/bin

User sammy may run the following commands on this host:
(root) NOPASSWD: /usr/local/bin/docker-compose up -d – build

Testing your installation

Now that you have TensorFlow installed, you can verify it works by opening the Python interpreter in your terminal and typing import tensorflow as tf. If you see an error message, it likely means that you do not have the proper version of Python installed, or that TensorFlow is not in your Python path. You can check your Python version by typing python – version at the command line. If you see something likePython 2.7.6, then you have version 2.7 installed; if you seePython 3.4.3, then you have 3.4 installed. If neither of those is true, follow the instructions for installing Python from

If your error message says something like No module named ‘tensorflow’, it means that TensorFlow is not in your Python path. To fix this error, first find out where your default Python path is by typing python -c “import sys; print(sys.path)” at the command line. This will print a list of directories; one of those directories should contain the file and another should contain the file tensorflow/ . Add both of those directories to your PYTHONPATH environment variable, using ; as a separator on Windows and : on macOS and Linux:

For example, if the output of that command is[”, ‘/usr/lib/python2.7’, ‘/usr/lib/python2.7/plat-linux2’, ‘/usr/lib/python2.7/lib-tk’, ‘/usr/lib/python2.7/lib-old’, ‘/usr/lib/python2.7/lib-dynload’, ‘/usr/local/lib/python2

Upgrading TensorFlow

If you’re using an older version of TensorFlow, we recommend upgrading to TensorFlow 1.4.0. TensorFlow 1.4.0 offers significant improvements, including:

– Support for Python 3.5
– Better performance on 64-bit architectures
– Experiments show that TensorFlow 1.4.0 is up to 2x faster than previous versions

To upgrade TensorFlow, simply follow the instructions below:

1. uninstall the previous version of TensorFlow:

pip uninstall tensorflow

2. install TensorFlow 1.4.0:

pip install tensorflow==1.4

Uninstalling TensorFlow

If you installed TensorFlow with pip, you can uninstall it by running
pip uninstall tensorflow
If you installed TensorFlow using the tar file, you can uninstall it by doing the following:
1. Destroy the TensorFlow installation (by deleting the folder).
2. Uninstall Python 3.5 (if necessary).


In this tutorial, we have installed TensorFlow 1.4.0 on Windows 10. We have also discussed some of the new features in TensorFlow 1.4.0, including the new high-level API, Eager execution, and support for the Keras sequential model.

Keyword: How to Install Tensorflow 1.4.0

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