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Commit cf970ee4 authored by Rishi Sharma's avatar Rishi Sharma
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Update random alpha

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with 252 additions and 26 deletions
eval/step_configs/config_celeba_topkacc_norm.ini 0 → 100644
+ 36
− 0
View file @ cf970ee4
[DATASET]
dataset_package = decentralizepy.datasets.Celeba
dataset_class = Celeba
model_class = CNN
images_dir = /mnt/nfs/shared/leaf/data/celeba/data/raw/img_align_celeba
train_dir = /mnt/nfs/shared/leaf/data/celeba/per_user_data/train
test_dir = /mnt/nfs/shared/leaf/data/celeba/data/test
; python list of fractions below
sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 4
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.TopKNormalized
sharing_class = TopKNormalized
alpha = 0.1
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
eval/step_configs/config_celeba_topkaccbound.ini 0 → 100644
+ 38
− 0
View file @ cf970ee4
[DATASET]
dataset_package = decentralizepy.datasets.Celeba
dataset_class = Celeba
model_class = CNN
images_dir = /mnt/nfs/shared/leaf/data/celeba/data/raw/img_align_celeba
train_dir = /mnt/nfs/shared/leaf/data/celeba/per_user_data/train
test_dir = /mnt/nfs/shared/leaf/data/celeba/data/test
; python list of fractions below
sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 4
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.LowerBoundTopK
sharing_class = LowerBoundTopK
lower_bound = 0.1
alpha = 0.1
metro_hastings = False
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
eval/step_configs/config_celeba_topkparam.ini
+ 1
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
......
eval/step_configs/config_celeba_topkrandom.ini
+ 1
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
......
eval/step_configs/config_celeba_wavelet.ini
+ 2
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
......@@ -36,3 +36,4 @@ alpha = 0.1
wavelet=sym2
level= 4
accumulation = True
accumulate_averaging_changes = True
eval/step_configs/config_cifar_SharingWithRWAsyncDynamic.ini 0 → 100644
+ 36
− 0
View file @ cf970ee4
[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 = 1
[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.TCPRandomWalkRouting
comm_class = TCPRandomWalkRouting
addresses_filepath = ip_addr_6Machines.json
sampler = equi
[SHARING]
sharing_package = decentralizepy.sharing.SharingWithRWAsyncDynamic
sharing_class = SharingWithRWAsyncDynamic
\ No newline at end of file
eval/step_configs/config_CIFAR_topk.ini → eval/step_configs/config_cifar_partialmodel.ini
+ 6
− 6
View file @ cf970ee4
......@@ -5,19 +5,20 @@ model_class = LeNet
train_dir = /mnt/nfs/shared/CIFAR
test_dir = /mnt/nfs/shared/CIFAR
; python list of fractions below
sizes =
random_seed = 91
sizes =
random_seed = 99
partition_niid = True
shards = 4
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 5
rounds = 65
full_epochs = False
batch_size = 8
shuffle = True
......@@ -32,5 +33,4 @@ addresses_filepath = ip_addr_6Machines.json
[SHARING]
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.5
accumulation = True
alpha=0.5
eval/step_configs/config_CIFAR.ini → eval/step_configs/config_cifar_sharing.ini
+ 4
− 4
View file @ cf970ee4
......@@ -5,20 +5,20 @@ model_class = LeNet
train_dir = /mnt/nfs/shared/CIFAR
test_dir = /mnt/nfs/shared/CIFAR
; python list of fractions below
sizes =
random_seed = 91
sizes =
random_seed = 99
partition_niid = True
shards = 4
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 5
rounds = 65
full_epochs = False
batch_size = 8
shuffle = True
......
eval/step_configs/config_cifar_subsampling.ini 0 → 100644
+ 36
− 0
View file @ cf970ee4
[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.SubSampling
sharing_class = SubSampling
alpha = 0.5
eval/step_configs/config_cifar_topkacc.ini 0 → 100644
+ 38
− 0
View file @ cf970ee4
[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 → eval/step_configs/config_cifar_wavelet.ini
+ 5
− 4
View file @ cf970ee4
......@@ -5,20 +5,20 @@ model_class = LeNet
train_dir = /mnt/nfs/shared/CIFAR
test_dir = /mnt/nfs/shared/CIFAR
; python list of fractions below
sizes =
random_seed = 91
sizes =
random_seed = 99
partition_niid = True
shards = 4
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
training_package = decentralizepy.training.Training
training_class = Training
rounds = 5
rounds = 65
full_epochs = False
batch_size = 8
shuffle = True
......@@ -38,3 +38,4 @@ alpha = 0.5
wavelet=sym2
level= 4
accumulation = True
accumulate_averaging_changes = True
eval/step_configs/config_femnist_fft.ini
+ 1
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
# There are 734463 femnist samples
......
eval/step_configs/config_femnist_grow.ini
+ 1
− 1
View file @ cf970ee4
......@@ -9,7 +9,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
......
eval/step_configs/config_femnist_partialmodel.ini
+ 1
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
......
eval/step_configs/config_femnist_sharing.ini
+ 1
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
[TRAIN_PARAMS]
......
eval/step_configs/config_femnist_subsampling.ini
+ 1
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
# There are 734463 femnist samples
......
eval/step_configs/config_femnist_topkacc.ini
+ 3
− 2
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
# There are 734463 femnist samples
......@@ -33,4 +33,5 @@ addresses_filepath = ip_addr_6Machines.json
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.1
accumulation = True
\ No newline at end of file
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
eval/step_configs/config_femnist_topkaccbound.ini 0 → 100644
+ 38
− 0
View file @ cf970ee4
[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.LowerBoundTopK
sharing_class = LowerBoundTopK
lower_bound = 0.1
alpha = 0.1
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
eval/step_configs/config_femnist_topkparam.ini
+ 1
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
# There are 734463 femnist samples
......
eval/step_configs/config_femnist_wavelet.ini
+ 2
− 1
View file @ cf970ee4
......@@ -10,7 +10,7 @@ sizes =
[OPTIMIZER_PARAMS]
optimizer_package = torch.optim
optimizer_class = Adam
optimizer_class = SGD
lr = 0.001
# There are 734463 femnist samples
......@@ -37,3 +37,4 @@ alpha = 0.1
wavelet=sym2
level= 4
accumulation = True
accumulate_averaging_changes = True
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