Row 91864

Row ID: 91864 | Dataset Entry | Axioma AXP Content Repository

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What do people use for GNN research libraries now?

I am looking to work with pytorch or jax, consumer grade GPUs (RTX 4090). I don't need fancy SOTA GNN layers, just a robust well-optimzied message passing framework that accepts heterogeneous graphs (multiple nodes, edges and levels).

I am aware of [DGL](https://www.dgl.ai/), [PyG](https://www.pyg.org/) and [Jraph](https://github.com/google-deepmind/jraph). A plus would be to serialize the models.

Any positive/negative experiences?

FieldValue
text What do people use for GNN research libraries now? I am looking to work with pytorch or jax, consumer grade GPUs (RTX 4090). I don't need fancy SOTA GNN layers, just a robust well-optimzied message passing framework that accepts heterogeneous graphs (multiple nodes, edges and levels). I am aware of [DGL](https://www.dgl.ai/), [PyG](https://www.pyg.org/) and [Jraph](https://github.com/google-deepmind/jraph). A plus would be to serialize the models. Any positive/negative experiences?
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-25
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Raw Record

{
  "text": "What do people use for GNN research libraries now?\n\nI am looking to work with pytorch or jax, consumer grade GPUs (RTX 4090). I don't need fancy SOTA GNN layers, just a robust well-optimzied message passing framework that accepts heterogeneous graphs (multiple nodes, edges and levels).\n\nI am aware of [DGL](https://www.dgl.ai/), [PyG](https://www.pyg.org/) and [Jraph](https://github.com/google-deepmind/jraph). A plus would be to serialize the models.\n\nAny positive/negative experiences?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-25",
  "username_encoded": "Z0FBQUFBQm5Lak1zRDcwR2pmeG55bWh3VlRrbkJwTjJaNy1Ia1E5enpTMFBrVFQzVVNRaWV4OERkYTI3ZHlZN1V5TVo3TldNWE9uRjJQeWtKWGRVZ0IwR28yelQ2VlBWRXc9PQ==",
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}
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