pip install tensorflow not working with code examples

When it comes to machine learning and deep learning, one of the most popular and widely-used libraries is TensorFlow. Developed by Google, TensorFlow is an open-source software library that allows users to build and deploy deep learning models with ease. However, one of the common issues that users face when working with TensorFlow is that the installation process can sometimes be quite tricky, with the pip install tensorflow not working at times. In this article, we will delve into this issue, discussing the possible causes, and providing solutions to troubleshoot common installation errors.

Causes of the Problem

One of the causes of the "pip install tensorflow not working" problem is the lack of pre-installed dependencies. There are several dependencies such as CUDA or cuDNN, that are needed by TensorFlow to function properly, however, these dependencies need to be pre-installed before installing TensorFlow. Furthermore, incorrect versions of these dependencies can also cause installation errors.

Another possible cause is an outdated pip version. As TensorFlow is a rapidly evolving library, newer versions of pip may be required to install and use the newest version of TensorFlow. If this is the case, upgrading to the latest version of pip may help resolve the installation issues.

Lastly, we cannot forget the possibility of human error, which can sometimes result in typos or incorrect commands being used during the installation process. It is essential to ensure that the installation commands are accurate and error-free, to avoid unnecessary headaches.


If you are experiencing problems installing TensorFlow with the pip command, here are a few solutions to help you troubleshoot the problem:

  1. Check that pre-installed dependencies are available

As previously mentioned, there are several dependencies that need to be pre-installed before installing TensorFlow, such as CUDA or cuDNN. To check whether these dependencies are available, run the following command in your terminal:


If the above command returns an error, it is likely that you do not have the necessary dependencies installed. Make sure to install the required dependencies for your operating system, and then try the pip install tensorflow command again.

  1. Check pip version

If you are using an outdated version of pip, there is a high chance that it may not support the latest version of TensorFlow. To upgrade pip to the latest version, run the following command:

pip install –upgrade pip

Ensure that you run this command before installing TensorFlow, to avoid any installation errors related to outdated pip.

  1. Try different installation methods

If none of the above solutions work, consider trying an alternative installation method such as Anaconda or virtual environments. Both these methods offer an isolated environment to install TensorFlow and other dependencies, making it easier to manage and maintain your machine learning environment.

  1. Check for typos and syntax errors

It is not uncommon to make errors when running commands. Ensure that the commands you are using are correct and free of syntax errors. You might also consider copying and pasting the command from the TensorFlow website or official documentation to eliminate typing errors.

Code Examples

Here's an example of a working installation command:

pip install tensorflow

If you encounter an error, an example error message might look like this:

ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)

Solution: This error message usually occurs if you have an outdated version of pip, thus try upgrading pip to the latest version using the "pip install –upgrade pip" command.

In addition, if you encounter errors relating to pre-installed dependencies, they may look like this:

cuda_not_found error message or ImportError: libcublas.so.10.0.130: cannot open shared object file: No such file or directory

Both errors indicate that the CUDA libraries or other pre-installed dependencies have not been found or installed. Ensure that you have installed the correct dependencies for your system, and that they are in the correct directory.


Installing TensorFlow can sometimes be a tricky process, with the "pip install tensorflow not working" error being a common obstacle. However, by following the solutions presented, you should be able to successfully install TensorFlow with ease. Additionally, be aware that TensorFlow is frequently updated, so make sure to keep an eye on any changes or updates in the installation process to avoid having to troubleshoot installation errors in the future.

Sure. In the previous topic, we discussed how to troubleshoot the "pip install tensorflow not working" error. In this section, we will dive deeper into some common causes of this error and some steps that can help to resolve it.

One of the most common causes of the "pip install tensorflow not working" error is the lack of pre-installed dependencies. These dependencies include CUDA, cuDNN, and other libraries that are required for TensorFlow to work properly. When installing TensorFlow, it is important to ensure that all these dependencies are installed beforehand, otherwise, the tensorflow installation may fail. Installing the correct version of these dependencies is also critical. For instance, a user may install the wrong version of CUDA and encounter an error when installing TensorFlow.

Another potential cause of the "pip install tensorflow not working" error is an outdated version of pip. TensorFlow is frequently updated, so it is important to ensure that pip is also updated to the latest version to avoid any compatibility issues.

Additionally, another cause of this error could be due to network issues. If the internet connection is not stable, the installation process may be interrupted, or the pip installer may not be able to connect to the package index. In such cases, it is advisable to check your internet connection and try again once the network issue is resolved.

Regarding the steps to resolve the error, we have discussed some solutions such as checking the pre-installed dependencies, upgrading pip, trying a different installation method, and checking for typos or syntax errors. However, it is also important to note that there are different ways to install TensorFlow, including Anaconda, Docker, and virtual environments. Each of these methods has its respective advantages, so it is advisable to choose the one that works best for your machine learning project and environment.

In conclusion, the "pip install tensorflow not working" error can be frustrating, but it is important to understand the potential causes and solutions to resolve it. By following the steps outlined above, you should be able to install TensorFlow successfully and develop deep learning models with ease.

Popular questions

  1. What are some common causes of the "pip install tensorflow not working" error?
    Answer: Some common causes include lack of pre-installed dependencies, outdated pip version, network issues, and typos/syntax errors during installation.

  2. What are some pre-installed dependencies that may cause the error?
    Answer: CUDA, cuDNN, and other libraries are required for TensorFlow to work properly and should be installed beforehand.

  3. What should you do if you encounter an error relating to pre-installed dependencies?
    Answer: Ensure that you have installed the correct versions of the dependencies for your system and that they are located in the correct directory.

  4. Are there different installation methods for TensorFlow?
    Answer: Yes, you can choose to install TensorFlow using Anaconda, Docker, or virtual environments.

  5. Why is it important to keep pip updated?
    Answer: TensorFlow is frequently updated, and to ensure compatibility, pip needs to be updated to the latest version.



Cloud Computing and DevOps Engineering have always been my driving passions, energizing me with enthusiasm and a desire to stay at the forefront of technological innovation. I take great pleasure in innovating and devising workarounds for complex problems. Drawing on over 8 years of professional experience in the IT industry, with a focus on Cloud Computing and DevOps Engineering, I have a track record of success in designing and implementing complex infrastructure projects from diverse perspectives, and devising strategies that have significantly increased revenue. I am currently seeking a challenging position where I can leverage my competencies in a professional manner that maximizes productivity and exceeds expectations.
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