Practical overview
When it fits
Use it when you want a code-based learning example or a starting implementation for experimenting with graph attention on Cora or PPI.
Who it is for
- Students and researchers learning graph neural networks and graph attention.
- Developers who want to inspect or adapt a PyTorch GAT implementation.
- Practitioners experimenting with transductive Cora or inductive PPI examples.
Documented uses
- Train the included GAT example on Cora or PPI.
- Inspect graph structure and learned attention patterns through visualizations.
- Study the implementation in an annotated Jupyter notebook.
Requirements & limits
The public repository is open source under the MIT license.
- The repository documents Conda-based setup through environment.yml.
- The README recommends a GPU with at least 2 GB of memory for Cora and 8 GB or more for PPI; PPI also has a documented CPU fallback and resource-reduction suggestions.
- PPI attention visualizations are limited to the first GAT layer because later-layer coefficients are described as nearly zero.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the repository and open the annotated notebook
Create the Conda environment and open the project's learning notebook.
Before you start
- Git
- Anaconda or Miniconda with Conda
- Python environment dependencies specified in environment.yml
Steps
Clone the repository and navigate to its project directory.
Run conda env create from that directory.
Activate the pytorch-gat environment.
Run jupyter notebook and open The Annotated GAT.ipynb.
Expected outcome
The annotated notebook opens in Jupyter for exploring the implementation.
Guide limitations
- The README recommends installing system-wide CUDA beforehand for GPU drivers.
- A Windows win32api DLL error is noted, with pywin32 reinstall options suggested.
Train the included Cora or PPI example
Run one of the repository's documented dataset training scripts.
Before you start
- Repository environment created from environment.yml and activated
- A GPU suited to the selected dataset, or the documented CPU option for PPI
Steps
For Cora, run python training_script_cora.py from the project environment.
For PPI, run python training_script_ppi.py.
For PPI on a machine without a strong GPU, add --force_cpu; the README also suggests reducing batch size or model size.
Expected outcome
The scripts train the corresponding GAT example and save checkpoints, a final model, and training metrics in the repository's output directories.
Guide limitations
- The README describes Cora as suitable for a GPU with 2 GB or more of memory.
- PPI is more GPU-intensive; the README recommends 8 GB or more of VRAM unless using CPU or reducing resource demands.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 16 October 2026
- Original website title
- gordicaleksa/pytorch-GAT · Official homepage title · retained in its source language