TristanBilot/mlx-GCN
MLX implementation of GCN, with benchmark on MPS, CUDA and CPU (M1 Pro, M2 Ultra, M3 Max).
This project provides an example implementation of a Graph Convolutional Network (GCN) using the MLX framework, primarily for those working with Apple Silicon. It allows you to train and test a GCN model on a standard dataset, with inputs being graph-structured data and outputs being classified nodes or predictions based on the graph's connections. Researchers and machine learning engineers developing or experimenting with graph neural networks on Apple hardware would find this useful.
No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher developing graph neural networks and want to see an efficient GCN implementation on Apple Silicon devices (M1 Pro, M2 Ultra, M3 Max) using the MLX framework.
Not ideal if you are looking for a production-ready GCN library for general use cases or if you are not interested in benchmarking performance across different hardware backends.
Stars
25
Forks
3
Language
Python
License
MIT
Category
Last pushed
Dec 16, 2023
Commits (30d)
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