71 lines
1.4 KiB
YAML
71 lines
1.4 KiB
YAML
# Whether to use GPU
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use_gpu: True
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# Deciding which GPU to use
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device: 0
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# Federate learning related options
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federate:
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# `standalone` or `distributed`
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mode: standalone
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# Evaluate in Server or Client test set
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make_global_eval: True
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# Number of dataset being split
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client_num: 5
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# Number of communication round
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total_round_num: 400
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# Dataset related options
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data:
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# Root directory where the data stored
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root: data/
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# Dataset name
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type: cora
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# Use Louvain algorithm to split `Cora`
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splitter: 'louvain'
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dataloader:
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# Type of sampler
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type: pyg
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# Use fullbatch training, batch_size should be `1`
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batch_size: 1
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# Model related options
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model:
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# Model type
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type: gcn
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# Hidden dim
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hidden: 64
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# Dropout rate
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dropout: 0.5
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# Number of Class of `Cora`
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out_channels: 7
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# Criterion related options
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criterion:
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# Criterion type
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type: CrossEntropyLoss
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# Trainer related options
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trainer:
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# Trainer type
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type: nodefullbatch_trainer
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# Train related options
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train:
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# Number of local update steps
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local_update_steps: 4
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# Optimizer related options
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optimizer:
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# Learning rate
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lr: 0.25
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# Weight decay
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weight_decay: 0.0005
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# Optimizer type
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type: SGD
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# Evaluation related options
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eval:
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# Frequency of evaluation
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freq: 1
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# Evaluation metrics, accuracy and number of correct items
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metrics: ['acc', 'correct'] |