Fix: Could not Find a Version that Satisfies the Requirement for Tensorflow

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Tensor Flow is an open source artificial intelligence platform that includes vast libraries and community resources. A geek can easily install Python and PyCharm on their systems. However, when trying to install Tensorflow, an error occurs: “No version found that meets the requirements for TensorFlow. No suitable distribution found for TensorFlow. “

This error can have several reasons. We will take it a step further and fix it until we can get rid of this error on Windows.

Check Python version on your system

If you are on Windows 10 with Python 3.6X, you are probably using 32-bit Python. TensorFlow is only compatible with the 64-bit version. Install 64-bit versions and set your PATH environment variable. By adding the path to the Python executables, you can access python.exe by entering the python keyword. If the PATH variable is not set, an error occurs. To solve this problem, you need to provide the full path.

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Downgrade your Python version to Anaconda

TensorFlow only supports Python 3.6.x and 64-bit. If you are using a GUI like Anaconda and Python 3.7, you will need to upgrade to Pythin3.6 to install Tensorflow.

Update package installer for Python

Pip is a package installer for Python and we can use pip to install packages from the Python Package Index and other indices. Although updates are released regularly every three months and these packages need to be manually updated on your system by running certain commands. If they are not out of date, they might cause this Tensorflow installation error. So let’s update the pip package by running the following commands so that all packages are updated automatically:

pip install --upgrade pip
pip install --upgrade

Hopefully, after updating the Pip packages, your installation will go smoothly and you can now develop interesting software projects with Tensorflow.

The TensorFlow version may not be compatible with your Python version. This is especially true if you are using a newer version of Python. For example, there might be a delay between the release of a new version of Python and the release of TensorFlow for that version of Python. In this case, I think the above options, such as downgrading Python, compiling TensorFlow from source, and waiting for the correct version of TensorFlow to be released, are all workarounds for Possible workarounds that you can use to install Tensorflow on your system without interference.

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