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* Add pure_gpu example of graphsage * move to advanced directory Co-authored-by: Quan Gan <coin2028@hotmail.com>
40 行
1.3 KiB
Markdown
40 行
1.3 KiB
Markdown
More Examples for Training GraphSAGE
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============================
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### Pure GPU sampling
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```bash
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python3 pure_gpu_node_classification.py
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```
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### Unsupervised training
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Train w/ mini-batch sampling in an unsupervised fashion (on the Reddit dataset)
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```bash
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python3 train_sampling_unsupervised.py
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```
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Notably,
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* The loss function is defined by predicting whether an edge exists between two nodes or not. This matches the official
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implementation, and is equivalent to the loss defined in the paper with 1-hop random walks.
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* When computing the score of `(u, v)`, the connections between node `u` and `v` are removed from neighbor sampling.
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This trick increases the F1-micro score on test set by 0.02.
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* The performance of the learned embeddings are measured by training a softmax regression with scikit-learn, as described
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in the paper.
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Micro F1 score reaches 0.9212 on test set.
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This example also demonstrates the advanced usages of multi-GPU `DDP` training, UVA-base sampling, full GPU sampling, and fine control of storing the graph structure and features individually.
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### Training with PyTorch Lightning
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We also provide minibatch training scripts with PyTorch Lightning in `train_lightning.py` and `train_lightning_unsupervised.py`.
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Requires `pytorch_lightning` and `torchmetrics`.
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```bash
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python3 train_lightning.py
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python3 train_lightning_unsupervised.py
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```
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