How to Check the Version of TensorFlow in Conda Environment?

Are you looking to find out which version of TensorFlow is installed in your Conda environment? This is a common question among data scientists and developers using TensorFlow. This article provides methods to check the TensorFlow version in your Conda environment.

Using Python Code

One straightforward way to check the TensorFlow version is by running Python code. Open a Python interpreter in your terminal or IDE and execute the following lines of code:

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Running this script will display the installed TensorFlow version on your screen. This method is quick and effective for Python users.

Checking Conda Package List

Another way to determine the TensorFlow version is by examining the list of installed packages. Run the following command in your terminal:

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This command will show all installed packages, including TensorFlow. Look for tensorflow or tensorflow-gpu in the list along with its version number. This allows you to verify TensorFlow alongside other installed packages.

Utilizing Pip List

If you installed TensorFlow using pip within your Conda environment, you can check the version with the pip list command. Run the following command in your terminal:

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This filters the list of installed packages for TensorFlow, helping you find its version number easily.

Looking at the Conda Environment Details

For a more detailed overview of your Conda environment, use the following command:

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This generates a YAML file with details about your environment, including the TensorFlow version. You can find the version information in this structured view.

Accessing TensorFlow Documentation

You can refer to the official TensorFlow documentation for guidance on checking the version of TensorFlow. The documentation provides detailed information and best practices for working with TensorFlow.

Checking the version of TensorFlow in your Conda environment is easy and can be done in several ways. Whether you choose to run Python code, check the package list, use pip commands, inspect environment details, or consult the documentation, you can quickly find the TensorFlow version you need for your projects.