NetDebugger

Installation

This repository is currently set up to run on 1) Mac OSX and 2) Linux/Windows machines with CUDA 10.2. Please raise a GitHub issue if you want to use this repo with a different configuration. Otherwise, please follow these steps for installation:

1. Install poetry on your machine.
2. If Python3.7 is installed on your machine skip to step 3, if not you will need to install it. There are many ways to do this, one option is detailed below:
   • Install Homebrew on your machine.
   • Run brew install python@3.7. Take note of the path to the python executable.
3. Clone this repo on your machine.
4. Open a terminal at the root directory of this repository.
5. Run poetry env use /path/to/python3.7/executable. If you installed Python3.7 with Homebrew, the path may be something like /usr/local/Cellar/python\@3.7/3.7.13_1/bin/python3.7.
6. Run poetry install.
7. If your machine is a Mac, run poetry run poe torch-osx. If not, run poetry run poe torch-linux_win-cuda102.
8. If your machine is a Mac, run poetry run poe pyg-osx. If not, run poetry run poe pyg-linux_win-cuda102.

Example

Run tutorial.ipynb

Motivation

Modified from my talk on neural network debugging:

Imagine a program that did not pass along helpful descriptions of errors, let alone provide a notification that an error has occurred. How would one even begin to debug such a nightmare? Error-laden deep learning systems are susceptible to such “silent failures”. The reason for silent failure in deep learning is that a neural network is made up of many adaptive components; if one component is broken, the others may adapt such that the model's overall performance is reasonable. If we only look at performance, one would not be aware of errors in the broken module. To mitigate chances of falling into this trap, we have collected and encoded a list of common-sense checks that deep learning systems should pass. Several checks are borrowed from a well-written blog post¹ by Andrej Karpathy, Tesla’s Director of Artificial Intelligence, while some are original. The implementation of these checks is original work and is known as NetDebugger.

[1] Karpathy A., A Recipe for Training Neural Networks, http://karpathy.github.io/2019/04/25/recipe/

Citation

If you use this repository in your work please consider citing us.

@misc{https://doi.org/10.48550/arxiv.2209.13557,
  doi = {10.48550/ARXIV.2209.13557},
  
  url = {https://arxiv.org/abs/2209.13557},
  
  author = {Gurnani, Rishi and Kuenneth, Christopher and Toland, Aubrey and Ramprasad, Rampi},
  
  keywords = {Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, FOS: Physical sciences},
  
  title = {Polymer informatics at-scale with multitask graph neural networks},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

License

This repository is protected under a General Public Use License Agreement, the details of which can be found in GT Open Source General Use License.pdf.