eval/step_configs/config_cifar_topkacc.ini

+[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

+[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

+[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

@@ -9,12 +9,12 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]
-training_package = decentralizepy.training.GradientAccumulator
-training_class = GradientAccumulator
+training_package = decentralizepy.training.Training
+training_class = Training
 rounds = 20
 full_epochs = False
 batch_size = 64

eval/step_configs/config_femnist.ini → eval/step_configs/config_femnist_partialmodel.ini

 [DATASET]
 dataset_package = decentralizepy.datasets.Femnist
 dataset_class = Femnist
+random_seed = 97
 model_class = CNN
-train_dir = /home/risharma/leaf/data/femnist/per_user_data/train
-test_dir = /home/risharma/leaf/data/femnist/data/test
+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 = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]
-training_package = decentralizepy.training.GradientAccumulator
-training_class = GradientAccumulator
-rounds = 20
+training_package = decentralizepy.training.Training
+training_class = Training
+rounds = 47
 full_epochs = False
-batch_size = 64
+batch_size = 16
 shuffle = True
 loss_package = torch.nn
 loss_class = CrossEntropyLoss
@@ -30,3 +31,4 @@ addresses_filepath = ip_addr_6Machines.json
 [SHARING]
 sharing_package = decentralizepy.sharing.PartialModel
 sharing_class = PartialModel
+alpha=0.1

eval/step_configs/config_femnist_100.ini → eval/step_configs/config_femnist_sharing.ini

 [DATASET]
 dataset_package = decentralizepy.datasets.Femnist
 dataset_class = Femnist
+random_seed = 97
 model_class = CNN
-train_dir = /home/risharma/leaf/data/femnist/per_user_data/train
-test_dir = /home/risharma/leaf/data/femnist/data/test
+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 = 
+sizes =
 
 [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 = 20
+rounds = 47
 full_epochs = False
-batch_size = 64
+batch_size = 16
 shuffle = True
 loss_package = torch.nn
 loss_class = CrossEntropyLoss

eval/step_configs/config_femnist_subsampling.ini

+[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

+[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