https://github.com/TristanBilot/mlx-GCN

MLX implementation of GCN, with benchmark on MPS, CUDA and CPU (M1 Pro, M2 Ultra, M3 Max).
https://github.com/TristanBilot/mlx-GCN

apple cuda deep-learning gnn mlx pytorch

Last synced: about 2 months ago
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MLX implementation of GCN, with benchmark on MPS, CUDA and CPU (M1 Pro, M2 Ultra, M3 Max).

• Host: GitHub
• URL: https://github.com/TristanBilot/mlx-GCN
• Owner: TristanBilot
• License: mit
• Created: 2023-12-11T09:40:09.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2023-12-16T22:19:53.000Z (9 months ago)
• Last Synced: 2024-06-16T12:04:15.952Z (3 months ago)
• Topics: apple, cuda, deep-learning, gnn, mlx, pytorch
• Language: Python
• Homepage:
• Size: 188 KB
• Stars: 17
• Watchers: 4
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Graph Convolutional Network in MLX

An example of [GCN](https://arxiv.org/pdf/1609.02907.pdf%EF%BC%89) implementation with MLX. Other examples are available here.

The actual benchmark on **M1 Pro**, **M2 Ultra**, **M3 Max** and **Tesla V100**s is explained in this Medium article.

### Install env and requirements

```

CONDA_SUBDIR=osx-arm64 conda create -n mlx python=3.10 numpy pytorch scipy requests -c conda-forge

conda activate mlx

pip install mlx

```

### Run

To try the model, just run the `main.py` file. This will download the Cora dataset, run the training and testing. The actual MLX code is located in `main.py`, whereas the PyTorch equivalent is in `main_torch.py`.

```

python main.py

```

### Run benchmark

To run the benchmark on CUDA device, a new env needs to be set up without the `CONDA_SUBDIR=osx-arm64` prefix, to be in i386 mode and not arm. For all other experiments on arm and Apple Silicon, just use the env created previously.

```

python benchmark.py --experiment=[ mlx | torch_mps | torch_cpu | torch_cuda ]

```

### Process benchmark figure

This needs to install additional packages: `matplotlib` and `scikit-learn`.

```

python viz.py

```