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Test #25

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Binary file added CIFAR10/output/confusion_matrix_0.png
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Binary file added CIFAR10/output/confusion_matrix_final.png
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130 changes: 130 additions & 0 deletions CIFAR10/output/metrics_0.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,130 @@
{
"epoch": 0,
"loss": 0.0213891339302063,
"accuracy": 0.5032,
"precision": 0.503870595423183,
"recall": 0.5046536204137805,
"f1": 0.499071014270288,
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129 changes: 129 additions & 0 deletions CIFAR10/output/metrics_final.json
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@@ -0,0 +1,129 @@
{
"loss": 0.021847880232334138,
"accuracy": 0.4852,
"precision": 0.4945647953890349,
"recall": 0.4852,
"f1": 0.4678093676224666,
"confusion_matrix": [
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3 changes: 3 additions & 0 deletions README.md
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Expand Up @@ -140,3 +140,6 @@ To include the dependency build files in VSCode, add the following to the includ
"${workspaceFolder}/bazel-PeerToPeer/external/{your_target_directory}"
]
```


protoc --python_out=ml proto/payload.proto
Empty file added ml/__init__.py
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24 changes: 16 additions & 8 deletions ml/aggregator.py
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Expand Up @@ -12,6 +12,8 @@
from dataloader import CIFAR10Dataset, get_data_loaders
from utils import train, val, test

from proto import payload_pb2, utility_pb2


def nn_aggregator(state_dicts):
"""
Expand All @@ -35,8 +37,9 @@ def nn_aggregator(state_dicts):

def main():
# Set up the context and responder socket
port_send = int(input("Enter the ZMQ sender port number: "))
port_rec = port_send + 1
port_rec = int(input("Enter the ZMQ sender port number: "))
port_send = int(input("Enter the ZMQ reciever port number: "))
num_peers = int(input("Enter the number of peers: "))

context = zmq.Context()
responder = context.socket(zmq.REP)
Expand All @@ -48,23 +51,28 @@ def main():
sender.connect("tcp://localhost:" + str(port_send))

# recieve the models from fake_peer.py
print("Waiting for models...")
state_dicts = []
for i in range(3):
for i in range(num_peers):
sd = responder.recv()
sd = pickle.loads(sd)
state_dicts.append(sd)
responder.send_string("ACK")
agg_inp = payload_pb2.AggregatorInputData()
agg_inp.ParseFromString(sd)
agg_inp = pickle.loads(agg_inp.modelStateDict)
state_dicts.append(agg_inp)

# average the models
print("Averaging models...")
avg_state_dict = nn_aggregator(state_dicts)

# send the averaged model back to fake_peer.py
avg_model = SimpleCNN()
avg_model.load_state_dict(avg_state_dict)
avg_model = pickle.dumps(avg_model)

sender.send(avg_model)
_ = sender.recv_string()
print("Sending averaged model...")
tr = payload_pb2.AggregatorInputData()
tr.modelStateDict = avg_model
sender.send(tr.SerializeToString())

print("Model averaging complete.")
return
Expand Down
56 changes: 56 additions & 0 deletions ml/proto/payload_pb2.py
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