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  • sacs/decentralizepy
  • mvujas/decentralizepy
  • randl/decentralizepy
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with 632 additions and 16 deletions
eval/step_configs/config_cifar_topkacc.ini 0 → 100644
View file @ d140bf44
[DATASET]
dataset_package = decentralizepy.datasets.CIFAR10
dataset_class = CIFAR10
model_class = LeNet
train_dir = /mnt/nfs/shared/CIFAR
test_dir = /mnt/nfs/shared/CIFAR
; python list of fractions below
sizes =
random_seed = 99
partition_niid = True
shards = 4
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 65
full_epochs = False
batch_size = 8
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.5
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
eval/step_configs/config_cifar_wavelet.ini 0 → 100644
View file @ d140bf44
[DATASET]
dataset_package = decentralizepy.datasets.CIFAR10
dataset_class = CIFAR10
model_class = LeNet
train_dir = /mnt/nfs/shared/CIFAR
test_dir = /mnt/nfs/shared/CIFAR
; python list of fractions below
sizes =
random_seed = 99
partition_niid = True
shards = 4
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 65
full_epochs = False
batch_size = 8
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.Wavelet
sharing_class = Wavelet
change_based_selection = True
alpha = 0.5
wavelet=sym2
level= 4
accumulation = True
accumulate_averaging_changes = True
eval/step_configs/config_femnist_fft.ini 0 → 100644
View file @ d140bf44
[DATASET]
dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist
random_seed = 97
model_class = CNN
train_dir = /mnt/nfs/shared/leaf/data/femnist/per_user_data/train
test_dir = /mnt/nfs/shared/leaf/data/femnist/data/test
; python list of fractions below
sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = SGD
lr = 0.001
# There are 734463 femnist samples
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 47
full_epochs = False
batch_size = 16
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.FFT
sharing_class = FFT
alpha = 0.1
change_based_selection = True
accumulation = True
eval/step_configs/config_femnist_grow.ini
View file @ d140bf44
...@@ -9,12 +9,12 @@ sizes = ...@@ -9,12 +9,12 @@ sizes =
[OPTIMIZER_PARAMS] [OPTIMIZER_PARAMS]
optimizer_package = torch.optim optimizer_package = torch.optim
optimizer_class = Adam optimizer_class = SGD
lr = 0.001 lr = 0.001
[TRAIN_PARAMS] [TRAIN_PARAMS]
training_package = decentralizepy.training.GradientAccumulator training_package = decentralizepy.training.Training
training_class = GradientAccumulator training_class = Training
rounds = 20 rounds = 20
full_epochs = False full_epochs = False
batch_size = 64 batch_size = 64
......
eval/step_configs/config_femnist.ini → eval/step_configs/config_femnist_partialmodel.ini
View file @ d140bf44
[DATASET] [DATASET]
dataset_package = decentralizepy.datasets.Femnist dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist dataset_class = Femnist
random_seed = 97
model_class = CNN model_class = CNN
train_dir = /home/risharma/leaf/data/femnist/per_user_data/train train_dir = /mnt/nfs/shared/leaf/data/femnist/per_user_data/train
test_dir = /home/risharma/leaf/data/femnist/data/test test_dir = /mnt/nfs/shared/leaf/data/femnist/data/test
; python list of fractions below ; python list of fractions below
sizes = sizes =
[OPTIMIZER_PARAMS] [OPTIMIZER_PARAMS]
optimizer_package = torch.optim optimizer_package = torch.optim
optimizer_class = Adam optimizer_class = SGD
lr = 0.001 lr = 0.001
[TRAIN_PARAMS] [TRAIN_PARAMS]
training_package = decentralizepy.training.GradientAccumulator training_package = decentralizepy.training.Training
training_class = GradientAccumulator training_class = Training
rounds = 20 rounds = 47
full_epochs = False full_epochs = False
batch_size = 64 batch_size = 16
shuffle = True shuffle = True
loss_package = torch.nn loss_package = torch.nn
loss_class = CrossEntropyLoss loss_class = CrossEntropyLoss
...@@ -30,3 +31,4 @@ addresses_filepath = ip_addr_6Machines.json ...@@ -30,3 +31,4 @@ addresses_filepath = ip_addr_6Machines.json
[SHARING] [SHARING]
sharing_package = decentralizepy.sharing.PartialModel sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel sharing_class = PartialModel
alpha=0.1
eval/step_configs/config_femnist_100.ini → eval/step_configs/config_femnist_sharing.ini
View file @ d140bf44
[DATASET] [DATASET]
dataset_package = decentralizepy.datasets.Femnist dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist dataset_class = Femnist
random_seed = 97
model_class = CNN model_class = CNN
train_dir = /home/risharma/leaf/data/femnist/per_user_data/train train_dir = /mnt/nfs/shared/leaf/data/femnist/per_user_data/train
test_dir = /home/risharma/leaf/data/femnist/data/test test_dir = /mnt/nfs/shared/leaf/data/femnist/data/test
; python list of fractions below ; python list of fractions below
sizes = sizes =
[OPTIMIZER_PARAMS] [OPTIMIZER_PARAMS]
optimizer_package = torch.optim optimizer_package = torch.optim
optimizer_class = Adam optimizer_class = SGD
lr = 0.001 lr = 0.001
[TRAIN_PARAMS] [TRAIN_PARAMS]
training_package = decentralizepy.training.Training training_package = decentralizepy.training.Training
training_class = Training training_class = Training
rounds = 20 rounds = 47
full_epochs = False full_epochs = False
batch_size = 64 batch_size = 16
shuffle = True shuffle = True
loss_package = torch.nn loss_package = torch.nn
loss_class = CrossEntropyLoss loss_class = CrossEntropyLoss
......
eval/step_configs/config_femnist_subsampling.ini 0 → 100644
View file @ d140bf44
[DATASET]
dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist
random_seed = 97
model_class = CNN
train_dir = /mnt/nfs/shared/leaf/data/femnist/per_user_data/train
test_dir = /mnt/nfs/shared/leaf/data/femnist/data/test
; python list of fractions below
sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = SGD
lr = 0.001
# There are 734463 femnist samples
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 47
full_epochs = False
batch_size = 16
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.SubSampling
sharing_class = SubSampling
alpha = 0.1
eval/step_configs/config_femnist_topkacc.ini 0 → 100644
View file @ d140bf44
[DATASET]
dataset_package = decentralizepy.datasets.Femnist
dataset_class = Femnist
random_seed = 97
model_class = CNN
train_dir = /mnt/nfs/shared/leaf/data/femnist/per_user_data/train
test_dir = /mnt/nfs/shared/leaf/data/femnist/data/test
; python list of fractions below
sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = SGD
lr = 0.001
# There are 734463 femnist samples
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 47
full_epochs = False
batch_size = 16
shuffle = True
loss_package = torch.nn
loss_class = CrossEntropyLoss
[COMMUNICATION]
comm_package = decentralizepy.communication.TCP
comm_class = TCP
addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.1
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
eval/step_configs/config_femnist_topkaccbound.ini 0 → 100644
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eval/step_configs/config_femnist_topkparam.ini 0 → 100644
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eval/step_configs/config_femnist_wavelet.ini 0 → 100644
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eval/step_configs/config_reddit_partialmodel.ini 0 → 100644
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eval/step_configs/config_reddit_sharing.ini 0 → 100644
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eval/step_configs/config_reddit_subsampling.ini 0 → 100644
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eval/step_configs/config_reddit_topkacc.ini 0 → 100644
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eval/step_configs/config_reddit_wavelet.ini 0 → 100644
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eval/step_configs/config_shakespeare_partialmodel.ini 0 → 100644
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eval/step_configs/config_shakespeare_sharing.ini 0 → 100644
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eval/step_configs/config_shakespeare_topkacc.ini 0 → 100644
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