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How to pip install an old version of TensorFlow

In this blog, we will learn about the process of installing an older version of a library such as TensorFlow, a common requirement for data scientists. This necessity may arise when dealing with legacy codebases or when attempting to reproduce experiments conducted with an earlier TensorFlow release. The article will guide you through the steps to successfully install an older version of TensorFlow using pip.
By Saturn Cloud | | Miscellaneous | Updated:

As a data scientist, you may sometimes need to install an older version of a library like TensorFlow. This could be because you’re working on a legacy codebase or because you need to replicate an experiment that was done with an older version of TensorFlow. In this article, we’ll walk you through the steps to install an older version of TensorFlow using pip.

Table of Contents

  1. Step 1: Check your current TensorFlow version
  2. Step 2: Find the version of TensorFlow you need
  3. Step 3: Install the old version of TensorFlow
  4. Step 4: Verify the installation
  5. Common Errors and Solutions
  6. Conclusion

Step 1: Check your current TensorFlow version

Before you start, it’s a good idea to check which version of TensorFlow you have installed. You can do this by opening a Python interpreter and running the following code:

import tensorflow as tf
print(tf.__version__)

This will print the version of TensorFlow that you currently have installed.

Step 2: Find the version of TensorFlow you need

The next step is to find the version of TensorFlow that you need to install. You can do this by checking the release notes on the TensorFlow website or by looking at the documentation for the project you’re working on. Once you know which version you need, you can move on to the next step.

Step 3: Install the old version of TensorFlow

Using pip

To install an older version of TensorFlow using pip, you’ll need to specify the version number in the pip install command. For example, if you want to install TensorFlow version 1.15.0, you would run the following command:

pip install tensorflow==1.15.0

This will install TensorFlow version 1.15.0 and any dependencies that it requires.

From Source

For more granular control, consider installing TensorFlow from source. Clone the TensorFlow repository and switch to the desired branch or tag:

git clone https://github.com/tensorflow/tensorflow.git
cd tensorflow
git checkout <desired_version>
pip install .

Step 4: Verify the installation

Once the installation is complete, you can verify that you have the correct version of TensorFlow installed by running the same code as before:

import tensorflow as tf
print(tf.__version__)

This should now print the version of TensorFlow that you just installed.

Common Errors and Solutions

Error: “No matching distribution found”

This error may occur if the specified version is not available on the Python Package Index (PyPI). Try a different version or consider installing from source.

Error: “Could not find a version that satisfies the requirement”

Ensure that the specified version or version range is correct. Double-check the TensorFlow release notes for valid version numbers.

Conclusion

In this article, we’ve shown you how to install an older version of TensorFlow using pip. This can be a useful skill to have as a data scientist, especially if you’re working with legacy code or need to replicate an experiment that was done with an older version of TensorFlow. Remember to check your current version of TensorFlow, find the version you need, use pip to install the old version, and verify the installation to make sure everything is working correctly.

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