Adventures in Machine Learning

Resolving AttributeError in PyTorch: Upgrades and Solutions

How to Resolve “AttributeError” Issues with PyTorch and Distutils

Have you ever encountered an AttributeError while working with PyTorch? This issue commonly occurs due to problems with accessing the version attribute from the distutils module.

In this article, we will discuss the reason behind the AttributeError and some solutions to resolve it. We will also provide a guide on how to upgrade your PyTorch package to its latest version using pip or conda.

Error with accessing version attribute from distutils module

The distutils module is a standard python module that provides some necessary tools for building and distributing python modules. It is often a dependency of other modules, such as setuptools.

setuptools is a python module that enables developers to easily package and distribute python projects. However, sometimes there can be a conflict between the distutils and setuptools modules.

If you encounter an AttributeError while trying to build or install a python package, it may be due to a mismatch between your setuptools and distutils versions. Specifically, your setuptools version may be higher than your distutils version, which leads to an error while trying to access the version attribute from the distutils module.

Solution to resolve AttributeError

There are a few ways to resolve the AttributeError when you face it. Here are a few solutions:

1. Upgrade PyTorch

If you’re using an older version of PyTorch, it may be helpful to upgrade to the latest version. PyTorch maintains backward compatibility, which means your code should still work after upgrading.

However, it is recommended that you test your code thoroughly before upgrading in case there are any compatibility issues.

2. Pin setuptools version

Another way to resolve the AttributeError is to pin your setuptools version to match your distutils version. You can do this using the pip tool:

pip install --upgrade setuptools==

Replace with the version of distutils you have installed.

For example, if you have distutils version 3.6, you can run:

pip install --upgrade setuptools==50.3.2

3. Use LooseVersion

If you face this issue even after upgrading or pinning the setuptools version, you can try using LooseVersion from the distutils module.

from distutils.version import LooseVersion
version = LooseVersion(torch.__version__)

This code should work for most cases, even if you have conflicting versions of setuptools and distutils installed.

Upgrading PyTorch Package

Now that we’ve covered how to resolve AttributeError using setuptools and distutils let’s move on to upgrading your PyTorch package. There are two ways to upgrade your PyTorch package using pip or conda.

Updating torch package to resolve AttributeError

The easiest way to upgrade your PyTorch package is by using the pip tool:

pip install --upgrade torch

This command will upgrade your PyTorch package to the latest version, which includes bug fixes and new features. However, if you are working on a specific project and require a particular version of PyTorch, you may want to specify the version number using the following command:

pip install torch==1.9.1

Testing PyTorch version 1.11.0

If you are testing a codebase or working on a project that requires PyTorch version 1.11.0, you can install it using pip or conda.

Here’s how to do it with pip:

pip install torch==1.11.0

If you prefer using conda to install and manage your packages, here’s how to install PyTorch version 1.11.0 using conda:

conda install pytorch=1.11.0 torchvision -c pytorch


We hope this article has provided some useful insights into how to resolve the AttributeError when working with PyTorch and distutils. Whether you choose to upgrade your PyTorch package to the latest version or use pip or conda to install a specific version, make sure to test your code thoroughly to ensure compatibility.

Also, remember to pin your setuptools version or use LooseVersion from the distutils module if you encounter any issues. Happy coding!

Pinning setuptools Package Version

As we have discussed earlier, sometimes installing a new version of setuptools may cause issues with accessing the version attribute from the distutils module, resulting in an AttributeError. In this case, you can try a different approach, which is to pin your setuptools package version to a specific release number to ensure compatibility.

Reason for pinning setuptools version

The reason for pinning the setuptools version is to prevent incompatibility between the setuptools and distutils modules. Picking the wrong version of the setuptools may cause errors when attempting to access the version attribute from the distutils module, or it may not build or install at all.

Therefore, it is essential to ensure that the version of setuptools you have installed can work harmoniously with your distutils package.

Commands to pin setuptools package version

To pin your setuptools version, you can use either pip or conda package managers. The following commands will instruct pip or conda to install an explicit version of setuptools, 59.5.0, to work with distutils:

Using pip:

pip install setuptools==59.5.0

Using conda:

conda install setuptools=59.5.0

If you encounter issues while running these commands, you may receive an error message.

In most cases, the “PackagesNotFoundError” message usually occurs when the installation package is not found in your package manager’s repository.

To resolve this issue, run the following command in your terminal before executing the install command:

conda config --append channels conda-forge

This command adds the conda-forge channel to your configuration, expanding the package availability and allowing you to install the required version of setuptools.

Alternative solution – importing LooseVersion class

We have discussed the use of LooseVersion as an alternative solution to address AttributeError issues with PyTorch and distutils. The LooseVersion is a class from the distutils.version module that provides a relaxed way of comparing versions of Python packages.

Instead of comparing exact numbers, LooseVersion compares the version strings’ individual components. This method can solve issues with conflicting versions of setuptools and distutils, as it disregards the version number’s exact digits.

Adjusting import statement to access LooseVersion class

When using LooseVersion, the import statement is essential to note. The import statement for the LooseVersion class is as follows:

from distutils.version import LooseVersion

Ensure that your import statement does not clash with other installed packages by using the full path to the module class.

For example, if you were only to import “version” from distutils, you may potentially import a different version method than what you are expecting. Additionally, if you’re accessing a specific package version using the import statement, make sure that you are using the correct version.

If you are importing a package that relies on another package, it is possible that the incorrect version of the dependent package may cause issues. Always check your package dependencies carefully.


Pinning your setuptools package version and using the LooseVersion class from the distutils.version module are two viable solutions to resolve AttributeError issues when working with PyTorch and distutils. By doing so, you can avoid version incompatibilities that may cause errors that are difficult to resolve.

While these issues can be frustrating and time-consuming, understanding the root cause and following the correct processes to resolve them can help your projects progress smoothly. It is essential to keep up-to-date with the latest developments and updates in Python modules to avoid errors and incompatibilities within your codebase.

Latest version of distutils module no longer has the version class

In the latest version of Python, distutils has been updated to remove the version class, which may cause problems if you are using this version class in your codebase. This means that earlier solutions, such as importing the version class from this module, are no longer viable in the current version of distutils.

This update makes it important to understand alternative solutions to accessing version information from distutils without relying on the version class.

Solutions to resolve AttributeError when accessing version attribute

Upgrading pytorch, importing the LooseVersion class, and pinning the setuptools package version are effective solutions to resolve the AttributeError when accessing version attributes in PyTorch and distutils. These solutions offer different approaches to resolving the issue, depending on your preference and the requirements of your project.

Upgrade PyTorch

Upgrading PyTorch to the latest version is an excellent solution for resolving the AttributeError issue when version-agnostic functions are available in the update. This solution is beneficial if you’re working with recent releases of PyTorch and want to ensure the most efficient compatibility between PyTorch and your package dependencies.

Pin setuptools version

Pinning your setuptools version when using PyTorch is a dependable solution when using outdated versions of PyTorch, where testing and maintaining code functionality between PyTorch packages can be trickier. This is especially useful when upgrading to the latest version of PyTorch, where depending package versions may no longer be compatible.

Import LooseVersion class

Using LooseVersion is also another viable solution if you encounter issues when accessing version attributes, such as an ImportError or a version inconsistency. It involves incorporating the LooseVersion class from the distutils.version module to relax the constraints when comparing version strings, allowing for greater flexibility.

Consequently, LooseVersion helps resolve compatibility issues when using PyTorch and the distutils package module. The use of LooseVersion can be beneficial when incompatibilities between setuptools and distutils occur.


Resolving AttributeError when accessing the version attribute in PyTorch requires updating our approaches to account for the removal of the version class from recent versions of distutils. Pinning setuptools, using LooseVersion, and upgrading PyTorch to the latest version are effective approaches to account for this change while ensuring code functionality.

By staying up-to-date with module developments and understanding alternative solutions, developers can resolve issues around conflicts between package dependencies and continue building robust, accessible code. In conclusion, resolving AttributeError when accessing the version attribute in PyTorch requires upgrading your approach to account for the removal of the version class from recent versions of distutils.

Pinning setuptools, importing LooseVersion class, and upgrading PyTorch to the latest version are effective solutions to ensure code functionality while addressing potential issues of version incompatibility. A crucial takeaway from this article is to maintain familiarity with changes to Python modules and understand alternative solutions to common errors for efficient development.

By staying up-to-date and open to new solutions, developers can effectively and confidently navigate the nuances of developing with PyTorch.

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