async def fit_model_on_worker(
worker,
built_model: sy.Plan,
built_loss_fn: sy.Plan,
encrypters,
batch_size: int,
curr_round: int,
max_nr_batches: int,
lr: float,
):
"""Send the model to the worker and fit the model on the worker's training data.
Args:
worker: Remote location, where the model shall be trained.
traced_model: Model which shall be trained.
batch_size: Batch size of each training step.
curr_round: Index of the current training round (for logging purposes).
max_nr_batches: If > 0, training on worker will stop at min(max_nr_batches, nr_available_batches).
lr: Learning rate of each training step.
Returns:
A tuple containing:
* worker_id: Union[int, str], id of the worker.
* improved model: torch.jit.ScriptModule, model after training at the worker.
* loss: Loss on last training batch, torch.tensor.
"""
num_of_parameters = len(built_model.parameters())
built_model.id = "GlobalModel_MNIST"
built_loss_fn.id = "LossFunc"
model_config = sy.ModelConfig(model=built_model,
loss_fn=built_loss_fn,
optimizer="SGD",
batch_size=batch_size,
optimizer_args={"lr": lr},
epochs=1,
max_nr_batches=max_nr_batches)
# model_config_send_start = time.time()
# pdb.set_trace()
# model_config.send(worker)
# model_config_send_end = time.time()
# print("[trace] GlobalInformationSend duration", worker.id, model_config_send_end - model_config_send_start)
return_ids = [0, 1]
for i in range(num_of_parameters):
return_ids.append("p" + str(i))
fit_sagg_start = time.time()
result_list = await worker.async_fit2_sagg_mc(model_config, dataset_key="mnist", encrypters=encrypters, return_ids=return_ids)
fit_sagg_end = time.time()
print("[trace] FitSagg", "duration", worker.id, fit_sagg_end - fit_sagg_start)
loss = result_list[0]
num_of_training_data = result_list[1]
enc_params = result_list[2:]
print("Iteration %s: %s loss: %s" % (curr_round, worker.id, loss))
return worker.id, enc_params, loss, num_of_training_data
def send_model_to_worker(
worker,
built_model: sy.Plan,
):
"""Send the model to the worker and fit the model on the worker's training data.
Args:
worker: Remote location, where the model shall be trained.
traced_model: Model which shall be trained.
batch_size: Batch size of each training step.
curr_round: Index of the current training round (for logging purposes).
max_nr_batches: If > 0, training on worker will stop at min(max_nr_batches, nr_available_batches).
lr: Learning rate of each training step.
Returns:
A tuple containing:
* worker_id: Union[int, str], id of the worker.
* improved model: torch.jit.ScriptModule, model after training at the worker.
* loss: Loss on last training batch, torch.tensor.
"""
built_model.id = "GlobalModel"
model_send_start = time.time()
# pdb.set_trace()
built_model.send(worker)
print("[trace] GlobalModelSend duration", worker.id,
time.time() - model_send_start)
return None