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epochs = 50
We go introduce new tool if you wan dey use federated datasets. We don create FederatedDataset class wey we use as we dey use PyTorch Dataset class, we go give federated dataloader FederatedDataLoader wey go come repeat am in a federated fashion.
Person wey write am:
Person wey translate am:
We go use sandbox wey we discover last lesson
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import torch as th
import syft as sy
sy.create_sandbox(globals(), verbose=False)
Oya find dataset
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boston_data = grid.search("#boston", "#data")
boston_target = grid.search("#boston", "#target")
We go load a model and an optimizer
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n_features = boston_data['alice'][0].shape[1]
n_targets = 1
model = th.nn.Linear(n_features, n_targets)
We go cast the date wey we get for FederatedDataset. See the workers wey hold part of the data.
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# Cast the result in BaseDatasets
datasets = []
for worker in boston_data.keys():
dataset = sy.BaseDataset(boston_data[worker][0], boston_target[worker][0])
datasets.append(dataset)
# Make we build FederatedDataset object
dataset = sy.FederatedDataset(datasets)
print(dataset.workers)
optimizers = {}
for worker in dataset.workers:
optimizers[worker] = th.optim.Adam(params=model.parameters(),lr=1e-2)
We go put am for FederatedDataLoader and options go dey specify
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train_loader = sy.FederatedDataLoader(dataset, batch_size=32, shuffle=False, drop_last=False)
Last last we go iterate over epochs. You see sey he dey similar to how we dey tarin pure and local PyTorch training!
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for epoch in range(1, epochs + 1):
loss_accum = 0
for batch_idx, (data, target) in enumerate(train_loader):
model.send(data.location)
optimizer = optimizers[data.location.id]
optimizer.zero_grad()
pred = model(data)
loss = ((pred.view(-1) - target)**2).mean()
loss.backward()
optimizer.step()
model.get()
loss = loss.get()
loss_accum += float(loss)
if batch_idx % 8 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tBatch loss: {:.6f}'.format(
epoch, batch_idx, len(train_loader),
100. * batch_idx / len(train_loader), loss.item()))
print('Total loss', loss_accum)
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