# How to Package a TensorFlow Application as an AppImage?

**URL:** <https://discourse.appimage.org/t/how-to-package-a-tensorflow-application-as-an-appimage/3042>\
**Category:** Developers\
**Created:** [October 23, 2024, 9:48am UTC](https://discourse.appimage.org/t/how-to-package-a-tensorflow-application-as-an-appimage/3042 "2024-10-23T09:48:46Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![drektheler](https://yyz1.discourse-cdn.com/flex027/user_avatar/discourse.appimage.org/drektheler/32/875_2.png) [@drektheler](https://discourse.appimage.org/u/drektheler)\
**Post date:** [October 23, 2024, 9:48am UTC](https://discourse.appimage.org/t/how-to-package-a-tensorflow-application-as-an-appimage/3042/1 "2024-10-23T09:48:46Z")

</div>

Hi everyone,

I am working on a machine learning project using TensorFlow and I am interested in packaging my application as an AppImage to make it easier to distribute across different Linux distributions. My goal is to ensure that users don’t need to worry about installing dependencies like TensorFlow, Python or other libraries it should just work out of the box.

I have gone through the basics of creating AppImages but TensorFlow has a lot of dependencies and I am wondering what is the best way to package everything. Should I use something like **pyinstaller** or **AppImageKit** to bundle it? Also, is there a way to ensure that GPU support (CUDA/cuDNN) works correctly within the AppImage: ??

If anyone has experience packaging complex applications like [TensorFlow](https://www.igmguru.com/machine-learning-ai/deep-learning-tensorflow-training) into AppImages, I would love to hear about your workflow, any challenges you faced and tips for handling large dependencies.

Thanks in advance for your insights !!

Derek Theler