diff --git a/eval/config_celeba_grow.ini b/eval/config_celeba_grow.ini
new file mode 100644
index 0000000000000000000000000000000000000000..b824b194c868c4473520b16c655b5cb55721d383
--- /dev/null
+++ b/eval/config_celeba_grow.ini
@@ -0,0 +1,37 @@
+[DATASET]
+dataset_package = decentralizepy.datasets.Celeba
+dataset_class = Celeba
+model_class = CNN
+n_procs = 96
+images_dir = /home/risharma/leaf/data/celeba/data/raw/img_align_celeba
+train_dir = /home/risharma/leaf/data/celeba/per_user_data/train
+test_dir = /home/risharma/leaf/data/celeba/data/test
+; python list of fractions below
+sizes =
+
+[OPTIMIZER_PARAMS]
+optimizer_package = torch.optim
+optimizer_class = Adam
+lr = 0.001
+
+[TRAIN_PARAMS]
+training_package = decentralizepy.training.GradientAccumulator
+training_class = GradientAccumulator
+epochs_per_round = 5
+batch_size = 512
+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.GrowingAlpha
+sharing_class = GrowingAlpha
+init_alpha=0.0
+max_alpha=1.0
+k=8
+metadata_cap=0.6
diff --git a/eval/config_femnist_grow.ini b/eval/config_femnist_grow.ini
new file mode 100644
index 0000000000000000000000000000000000000000..c15d98a3081ffc26c1e9c27a88ffc0641c193afd
--- /dev/null
+++ b/eval/config_femnist_grow.ini
@@ -0,0 +1,36 @@
+[DATASET]
+dataset_package = decentralizepy.datasets.Femnist
+dataset_class = Femnist
+model_class = CNN
+n_procs = 16
+train_dir = /home/risharma/leaf/data/femnist/per_user_data/train
+test_dir = /home/risharma/leaf/data/femnist/data/test
+; python list of fractions below
+sizes =
+
+[OPTIMIZER_PARAMS]
+optimizer_package = torch.optim
+optimizer_class = Adam
+lr = 0.001
+
+[TRAIN_PARAMS]
+training_package = decentralizepy.training.GradientAccumulator
+training_class = GradientAccumulator
+epochs_per_round = 5
+batch_size = 1024
+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.GrowingAlpha
+sharing_class = GrowingAlpha
+init_alpha=0.0
+max_alpha=1.0
+k=10
+metadata_cap=0.6
\ No newline at end of file
diff --git a/src/decentralizepy/datasets/Celeba.py b/src/decentralizepy/datasets/Celeba.py
index 748f59623e47fa5ce82ceb7968eabccc2651ca03..f648a7fa6c40419b9cbfd3631fdad471bfe5baae 100644
--- a/src/decentralizepy/datasets/Celeba.py
+++ b/src/decentralizepy/datasets/Celeba.py
@@ -320,6 +320,7 @@ class Celeba(Dataset):
class CNN(Model):
def __init__(self):
super().__init__()
+ # 2.8k parameters
self.conv1 = nn.Conv2d(CHANNELS, 32, 3, padding="same")
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 32, 3, padding="same")
diff --git a/src/decentralizepy/datasets/Femnist.py b/src/decentralizepy/datasets/Femnist.py
index 7370a0d07ab77ce52e6db4dfa1cde8bd7255ba80..ab33880b5d7e2a1ff7e0588e2c27bd0481bf9340 100644
--- a/src/decentralizepy/datasets/Femnist.py
+++ b/src/decentralizepy/datasets/Femnist.py
@@ -337,6 +337,7 @@ class LogisticRegression(Model):
class CNN(Model):
def __init__(self):
super().__init__()
+ # 1.6 million params
self.conv1 = nn.Conv2d(1, 32, 5, padding=2)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, 5, padding=2)
diff --git a/src/decentralizepy/sharing/GrowingAlpha.py b/src/decentralizepy/sharing/GrowingAlpha.py
new file mode 100644
index 0000000000000000000000000000000000000000..afe86768ededc073de60518373192513184ec03d
--- /dev/null
+++ b/src/decentralizepy/sharing/GrowingAlpha.py
@@ -0,0 +1,68 @@
+import logging
+
+from decentralizepy.sharing.PartialModel import PartialModel
+from decentralizepy.sharing.Sharing import Sharing
+
+
+class GrowingAlpha(PartialModel):
+ def __init__(
+ self,
+ rank,
+ machine_id,
+ communication,
+ mapping,
+ graph,
+ model,
+ dataset,
+ log_dir,
+ init_alpha=0.0,
+ max_alpha=1.0,
+ k=10,
+ metadata_cap=0.6,
+ dict_ordered=True,
+ save_shared=False,
+ ):
+ super().__init__(
+ rank,
+ machine_id,
+ communication,
+ mapping,
+ graph,
+ model,
+ dataset,
+ log_dir,
+ init_alpha,
+ dict_ordered,
+ save_shared,
+ )
+ self.init_alpha = init_alpha
+ self.max_alpha = max_alpha
+ self.k = k
+ self.metadata_cap = metadata_cap
+ self.base = None
+
+ def step(self):
+ if (self.communication_round + 1) % self.k == 0:
+ self.alpha += (self.max_alpha - self.init_alpha) / self.k
+
+ if self.alpha == 0.0:
+ logging.info("Not sending/receiving data (alpha=0.0)")
+ self.communication_round += 1
+ return
+
+ if self.alpha > self.metadata_cap:
+ if self.base == None:
+ self.base = Sharing(
+ self.rank,
+ self.machine_id,
+ self.communication,
+ self.mapping,
+ self.graph,
+ self.model,
+ self.dataset,
+ self.log_dir,
+ )
+ self.base.communication_round = self.communication_round
+ self.base.step()
+ else:
+ super().step()
diff --git a/src/decentralizepy/sharing/PartialModel.py b/src/decentralizepy/sharing/PartialModel.py
index bd01be3e66e0925e86ce7bb2474f8b6e4b0843fe..19fdfd12d58baaf7ea064d308c1db9ac0bc9e22b 100644
--- a/src/decentralizepy/sharing/PartialModel.py
+++ b/src/decentralizepy/sharing/PartialModel.py
@@ -29,7 +29,6 @@ class PartialModel(Sharing):
)
self.alpha = alpha
self.dict_ordered = dict_ordered
- self.communication_round = 0
self.save_shared = save_shared
# Only save for 2 procs
@@ -106,8 +105,6 @@ class PartialModel(Sharing):
logging.info("Converted dictionary to json")
- self.communication_round += 1
-
return m
def deserialized_model(self, m):
diff --git a/src/decentralizepy/sharing/Sharing.py b/src/decentralizepy/sharing/Sharing.py
index 03e80fd52a8e4b14c381dd3b463a926e94741041..91d25d06badb20500e1dd8c797f4c1d84cca1b8b 100644
--- a/src/decentralizepy/sharing/Sharing.py
+++ b/src/decentralizepy/sharing/Sharing.py
@@ -23,6 +23,7 @@ class Sharing:
self.model = model
self.dataset = dataset
self.log_dir = log_dir
+ self.communication_round = 0
self.peer_deques = dict()
my_neighbors = self.graph.neighbors(self.uid)
@@ -89,3 +90,5 @@ class Sharing:
self.model.load_state_dict(total)
logging.info("Model averaging complete")
+
+ self.communication_round += 1