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Commit fa934740 authored by Jeffrey Wigger's avatar Jeffrey Wigger
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more experiments wv

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......@@ -70,7 +70,7 @@ echo samples per user: $samples_per_user
#random_seeds=("90" "91" "92" "93" "94")
random_seeds=("97")
# "bior2.2" "rbio2.2"
alphas=("0.0091" "0.0455" "0.182" "0.273") #
alphas=("0.91" "0.182" "0.273") #"0.0091" "0.0455"
echo batchsize: $batchsize
echo communication rounds per global epoch: $comm_rounds_per_global_epoch
# calculating how many batches there are in a global epoch for each user/proc
......
[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
[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
compression_package = decentralizepy.compression.Elias16f
compression_class = Elias16f
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.Sharing
sharing_class = Sharing
[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
[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
compression_package = decentralizepy.compression.EliasFpzipLossy14
compression_class = EliasFpzipLossy14
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.Sharing
sharing_class = Sharing
[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
[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
compression_package = decentralizepy.compression.EliasFpzipLossy
compression_class = EliasFpzipLossy
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.Sharing
sharing_class = Sharing
[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
[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
compression_package = decentralizepy.compression.Elias8bitQuant
compression_class = Elias8bitQuant
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.Sharing
sharing_class = Sharing
[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
compression_package = decentralizepy.compression.Elias16f
compression_class = Elias16f
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.1
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
[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
compression_package = decentralizepy.compression.EliasFpzipLossy14
compression_class = EliasFpzipLossy14
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.1
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
[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
compression_package = decentralizepy.compression.EliasFpzipLossy
compression_class = EliasFpzipLossy
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.1
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
[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
compression_package = decentralizepy.compression.Elias8bitQuant
compression_class = Elias8bitQuant
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.PartialModel
sharing_class = PartialModel
alpha = 0.1
accumulation = True
accumulate_averaging_changes = True
\ No newline at end of file
[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
compression_package = decentralizepy.compression.Elias16f
compression_class = Elias16f
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.Wavelet
sharing_class = Wavelet
change_based_selection = True
alpha = 0.1
wavelet=rbio2.2
level= None
accumulation = True
accumulate_averaging_changes = True
[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
compression_package = decentralizepy.compression.EliasFpzipLossy14
compression_class = EliasFpzipLossy14
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.Wavelet
sharing_class = Wavelet
change_based_selection = True
alpha = 0.1
wavelet=rbio2.2
level= None
accumulation = True
accumulate_averaging_changes = True
[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
compression_package = decentralizepy.compression.EliasFpzipLossy
compression_class = EliasFpzipLossy
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.Wavelet
sharing_class = Wavelet
change_based_selection = True
alpha = 0.1
wavelet=rbio2.2
level= None
accumulation = True
accumulate_averaging_changes = True
[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
compression_package = decentralizepy.compression.Elias8bitQuant
compression_class = Elias8bitQuant
compress = True
[SHARING]
sharing_package = decentralizepy.sharing.Wavelet
sharing_class = Wavelet
change_based_selection = True
alpha = 0.1
wavelet=rbio2.2
level= None
accumulation = True
accumulate_averaging_changes = True
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