How to Check TensorFlow Version?

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When given a large quantity of data, deep learning surpassed all other machine learning algorithms a few years ago. Therefore, to improve its services by utilizing deep neural networks, Google created the TensorFlow framework to allow academicians and developers to collaborate on AI models. A large number of individuals can use it after it has been created and scaled.

TensorFlow was originally released in late 2015, with the first stable version following in 2017. It’s free and open-source, thanks to the Apache Open Source license. So without spending anything on Google, you may use it, alter it, and redistribute the updated version for a price.

TensorFlow is an open-source machine learning platform with end-to-end encryption. It is one of the famous machine learning packages. And it’s critical to know which version is installed on the system because various versions offer different choices. Based on the installation method, there are several ways to verify the TensorFlow version. This article demonstrates four alternative techniques to verify the TensorFlow version.

What is TensorFlow?

TensorFlow is a machine learning software library that is free and open-source. It may be used for a variety of applications, but it focuses on deep neural network training and inference. TensorFlow is a dataflow and discrete programming-based symbolic math toolkit. At Google, it is utilized for both research and manufacturing purposes. It enables programmers to construct machine learning applications utilizing a variety of tools, frameworks, and open-source resources.

The Google Brain team created TensorFlow for internal Google use. In 2015, it was published under the Apache License 2.0. Google’s TensorFlow is now the most well-known deep learning package on the planet. Google uses machine learning in all of its services to enhance search, translation, picture captions, and recommendations.

For instance, Google users may benefit from AI-assisted search that is both quicker and much more refined. For example, when a user puts a keyword into Google’s search field, the search engine suggests the following word.

Google aims to employ machine learning to make the most of its huge database to provide the best possible experience to its consumers. Three different groups use machine learning:

  • Data Scientists
  • Programmers
  • Researchers

They may all cooperate and increase their efficiency by using the same toolbox.

TensorFlow was developed to scale since Google has more than just data; they also have the world’s most powerful computer. TensorFlow is a machine learning and deep neural network research library created by the Google Brain Team.

It was designed to work on various CPUs, GPUs, and even mobile operating systems, including wrappers in Python, C++, and Java.

Why is TensorFlow a favorite?

TensorFlow is the finest library of them all since it is designed to be user-friendly. The TensorFlow library includes a variety of APIs for creating large-scale deep learning architectures such as CNNs and RNNs.

TensorFlow is a graph-based programming language that allows developers to view the neural network’s creation using TensorBoard. In addition, this software debugging tool is really useful. Finally, TensorFlow is designed to be used in large-scale deployments. It works on both the CPU and the GPU.

In comparison to the other deep learning frameworks, TensorFlow is the most popular one on GitHub.

How TensorFlow Works?

By accepting inputs as a multi-dimensional array named Tensor, TensorFlow allows you to create dataflow graphs and architectures to specify how data travels through a graph. In addition, it lets you create a flowchart of operations that may be done on these inputs, which travel in one direction and out the other.

How to Check TensorFlow Version?

Now that we’ve discussed the fundamentals of TensorFlow, we’ll be going ahead to understand how we can check the TensorFlow version. We’ll learn about the process in six different ways.


  1. Python 2 or Python 3 is installed
  2. Installed TensorFlow
  3. Accessibility to a command-line interface (CLI) or an integrated development environment (IDE)


A Python IDE or code editor is the simplest way to verify the TensorFlow version. Fundamental data may be shown using the library’s built-in functions. Run the following command in Python to output the TensorFlow version:

import TensorFlow as tf


Newer TensorFlow Versions

A mechanism for printing the TensorFlow version is available in the TensorFlow 2.x versions.

Use the following command to see which one is installed on your desktop:

import TensorFlow as tf


Older TensorFlow Versions

TensorFlow 1.x uses a somewhat different approach for determining the library’s version. Run the following command in Python to print the version for earlier TensorFlow builds:

import TensorFlow as tf



The python command in the CLI may be used to display the TensorFlow version by invoking Python. The -c option is used to run code.

Use the pythonversion> command if your workstation has several Python instances installed.

Finding TensorFlow Version in Linux Terminal

Run the following command in the terminal to print the TensorFlow version:

python -c ‘import TensorFlow as tf; print(tf.__version__)’

Use the following command if there are many Python instances on the system:

python<version> -c ‘import TensorFlow as tf; print(tf.__version__)’

Finding TensorFlow Version in Windows Command Prompt

In Windows Command Prompt, find the TensorFlow version using the following command:

python -c “import TensorFlow as tf; print(tf.__version__)”

By adding the version number to the python command, you may check with a specific version of Python:

python<version> -c “import TensorFlow as tf; print(tf.__version__)”


The pip package manager is the most frequent way to install Python libraries. The version with pip can be printed in two ways.

Method 1: Using pip Show

Any installed package’s data is printed using the pip display command.

Run the following command to see the TensorFlow data:

pip show tensorflow

Method 2: Using pip List

The pip list command displays all of the packages that have been installed with pip install. To filter the results in Linux, use the grep command:

pip list | grep tensorflow

To filter the pip list results on Windows, use findstr:

pip list | findstr "tensorflow"

Virtual Environment

The manual for TensorFlow suggests deploying the platform in a virtual environment. Before verifying the version, turn on the virtual environment.

Step 1: Activate

Use the relevant command for your operating system to activate the virtual environment:

Run the following command on Linux:

virtualenv <environment name>

Run the following command on Windows:

<environment name>\Scripts\activate

Step 2: Check Version

Check the version inside the environment using the python -c or pip show command.


pip show tensorflow


In recent years, TensorFlow has become the most well-known deep learning library. Any deep learning structure, such as a CNN, RNN, or basic artificial neural network, may be built using TensorFlow.

Academics, startups, and major corporations are the most common users of TensorFlow. TensorFlow is a session-based system. TensorFlow is used in virtually all Google products, including Gmail, Photos, and the Google Search Engine. TensorFlow is popular among practitioners because it is simple to set up and scale. It’s designed to function in the cloud as well as on mobile platforms like iOS and Android.

This article will explain how to verify the TensorFlow version in various situations and contexts, along with the basics of TensorFlow. We hope this was helpful. Share it with your friends and spread the knowledge!

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