FS-TFP/federatedscope/cv/baseline/fedavg_convnet2_on_cifar10....

46 lines
990 B
YAML

use_gpu: True
device: 0
early_stop:
patience: 5
seed: 12345
federate:
mode: standalone
client_num: 5
total_round_num: 300
sample_client_rate: 0.2
make_global_eval: True
merge_test_data: True
data:
root: data/
type: 'CIFAR10@torchvision'
splits: [1.0, 0.0, 0.0]
num_workers: 0
transform: [['ToTensor'], ['Normalize', {'mean': [0.4914, 0.4822, 0.4465], 'std': [0.2470, 0.2435, 0.2616]}]]
test_transform: [['ToTensor'], ['Normalize', {'mean': [0.4914, 0.4822, 0.4465], 'std': [0.2470, 0.2435, 0.2616]}]]
args: [{'download': True}]
splitter: 'lda'
splitter_args: [{'alpha': 0.05}]
dataloader:
batch_size: 64
model:
type: convnet2
hidden: 2048
out_channels: 10
dropout: 0.0
train:
local_update_steps: 1
batch_or_epoch: epoch
optimizer:
lr: 0.01
weight_decay: 0.0
grad:
grad_clip: 5.0
criterion:
type: CrossEntropyLoss
trainer:
type: cvtrainer
eval:
freq: 1
metrics: ['acc', 'correct']
best_res_update_round_wise_key: test_loss