diff --git a/config/scheduler/multistep.yaml b/config/scheduler/multistep.yaml
index 77af7c04aabbd32634c35cebc58bceb7426dc941..6caf71ace59d32e6ecb0eb61e2ad85ec03bbe965 100644
--- a/config/scheduler/multistep.yaml
+++ b/config/scheduler/multistep.yaml
@@ -1,3 +1,3 @@
_target_: torch.optim.lr_scheduler.MultiStepLR
-milestones: [ 100, 150 ]
+milestones: [ 101, 151 ]
gamma: 0.1
diff --git a/edml/config/battery/resnet110_cifar100_cost.yaml b/edml/config/battery/resnet110_cifar100_cost.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d1bcdb98d5d0d98d49eedff71d3cf12b9e1bf9d9
--- /dev/null
+++ b/edml/config/battery/resnet110_cifar100_cost.yaml
@@ -0,0 +1,4 @@
+deduction_per_second: 0.005
+deduction_per_mflop: 0.00000005
+deduction_per_mbyte_received: 0.0002
+deduction_per_mbyte_sent: 0.0002
diff --git a/edml/config/topology/equal_batteries_10_devices.yaml b/edml/config/topology/equal_batteries_10_devices.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ea81cd38a17f91a049a4bb82440a87239e00cbbf
--- /dev/null
+++ b/edml/config/topology/equal_batteries_10_devices.yaml
@@ -0,0 +1,62 @@
+devices: [
+ {
+ device_id: "d0",
+ address: "localhost:50051",
+ battery_capacity: 1000000,
+ torch_device: cuda:0
+ },
+ {
+ device_id: "d1",
+ address: "localhost:50052",
+ battery_capacity: 1000000,
+ torch_device: cuda:1
+ },
+ {
+ device_id: "d2",
+ address: "localhost:50053",
+ battery_capacity: 1000000,
+ torch_device: cuda:2
+ },
+ {
+ device_id: "d3",
+ address: "localhost:50054",
+ battery_capacity: 1000000,
+ torch_device: cuda:0
+ },
+ {
+ device_id: "d4",
+ address: "localhost:50055",
+ battery_capacity: 1000000,
+ torch_device: cuda:1
+ },
+ {
+ device_id: "d5",
+ address: "localhost:50056",
+ battery_capacity: 1000000,
+ torch_device: cuda:2
+ },
+ {
+ device_id: "d6",
+ address: "localhost:50057",
+ battery_capacity: 1000000,
+ torch_device: cuda:0
+ },
+ {
+ device_id: "d7",
+ address: "localhost:50058",
+ battery_capacity: 1000000,
+ torch_device: cuda:1
+ },
+ {
+ device_id: "d8",
+ address: "localhost:50059",
+ battery_capacity: 1000000,
+ torch_device: cuda:2
+ },
+ {
+ device_id: "d9",
+ address: "localhost:50060",
+ battery_capacity: 1000000,
+ torch_device: cuda:0
+ }
+]
diff --git a/edml/config/topology/resnet110_cifar100_batteries.yaml b/edml/config/topology/resnet110_cifar100_batteries.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3a81e0087447bd83066bf31840321d1cc2b4413d
--- /dev/null
+++ b/edml/config/topology/resnet110_cifar100_batteries.yaml
@@ -0,0 +1,27 @@
+devices: [
+ {
+ device_id: "d0",
+ address: "localhost:50051",
+ battery_capacity: 750,
+ },
+ {
+ device_id: "d1",
+ address: "localhost:50052",
+ battery_capacity: 750
+ },
+ {
+ device_id: "d2",
+ address: "localhost:50053",
+ battery_capacity: 600
+ },
+ {
+ device_id: "d3",
+ address: "localhost:50054",
+ battery_capacity: 600
+ },
+ {
+ device_id: "d4",
+ address: "localhost:50055",
+ battery_capacity: 600
+ }
+]
diff --git a/edml/controllers/strategy_optimization.py b/edml/controllers/strategy_optimization.py
index eddbf5742a526285cf6a3793b208900beda9f3ec..cd7bae8ac0db1c8eb4bcf818713ed8b2f1ed0e47 100644
--- a/edml/controllers/strategy_optimization.py
+++ b/edml/controllers/strategy_optimization.py
@@ -123,13 +123,15 @@ class ServerChoiceOptimizer:
self.global_params.train_global_time is not None
and self.global_params.last_server_device_id is not None
):
- return (
+ latency = (
self.global_params.train_global_time
- self._round_runtime_with_server_no_latency(
self.global_params.last_server_device_id
)
)
- return 0 # latency not known
+ if latency > 0:
+ return latency
+ return 0 # latency not known or runtime was overestimated previously
def _round_runtime_with_server_no_latency(self, server_device_id):
"""
@@ -356,20 +358,7 @@ class EnergySimulator:
device_params_list, global_params
)
- def simulate_greedy_selection(self):
- """
- Simulates the greedy server choice algorithm.
- Returns:
- num_rounds: number of rounds until the first device runs out of battery
- server_selection_schedule: list of server device ids for each round
- device_batteries: list of battery levels for each device after the last successful round
- """
-
- def __get_device_with_max_battery__(device_battery_list):
- return max(
- range(len(device_battery_list)), key=device_battery_list.__getitem__
- )
-
+ def _simulate_selection(self, selection_callback=None):
def __all_devices_alive__(device_battery_list):
return all(battery > 0 for battery in device_battery_list)
@@ -394,7 +383,9 @@ class EnergySimulator:
server_selection_schedule = []
num_rounds = 0
while all_devices_alive:
- server_device_idx = __get_device_with_max_battery__(device_batteries)
+ server_device_idx = selection_callback(
+ device_battery_list=device_batteries, num_rounds=num_rounds
+ )
new_batteries = device_batteries.copy()
for idx, device in enumerate(self.device_params_list):
new_batteries[idx] = new_batteries[idx] - energy[idx][server_device_idx]
@@ -409,6 +400,36 @@ class EnergySimulator:
break
return num_rounds, server_selection_schedule, device_batteries
+ def simulate_greedy_selection(self):
+ """
+ Simulates the greedy server choice algorithm.
+ Returns:
+ num_rounds: number of rounds until the first device runs out of battery
+ server_selection_schedule: list of server device ids for each round
+ device_batteries: list of battery levels for each device after the last successful round
+ """
+
+ def __get_device_with_max_battery__(device_battery_list, **kwargs):
+ return max(
+ range(len(device_battery_list)), key=device_battery_list.__getitem__
+ )
+
+ return self._simulate_selection(__get_device_with_max_battery__)
+
+ def simulate_sequential_selection(self):
+ """
+ Simulates the sequential server choice algorithm.
+ Returns:
+ num_rounds: number of rounds until the first device runs out of battery
+ server_selection_schedule: list of server device ids for each round
+ device_batteries: list of battery levels for each device after the last successful round
+ """
+
+ def __sequential_selection__(device_battery_list, num_rounds):
+ return num_rounds % len(device_battery_list)
+
+ return self._simulate_selection(__sequential_selection__)
+
def simulate_smart_selection(self):
"""
Simulates the smart server choice algorithm.
@@ -574,47 +595,52 @@ def run_grid_search(
for partition in partitions:
for cost_sec in cost_per_sec:
for cost_sent in cost_per_byte_sent:
- for cost_received in cost_per_byte_received:
- for cost_flop in cost_per_flop:
- global_params.cost_per_sec = cost_sec
- global_params.cost_per_byte_sent = cost_sent
- global_params.cost_per_byte_received = cost_received
- global_params.cost_per_flop = cost_flop
- for idx, device in enumerate(device_params_list):
- device.current_battery = battery[idx]
- device.comp_latency_factor = latency[idx]
- device.train_samples = (
- partition[idx] * total_train_samples
- )
- device.validation_samples = (
- partition[idx] * total_val_samples
- )
- energy_simulator = EnergySimulator(
- device_params_list, global_params
- )
- num_rounds_smart, _, _ = (
- energy_simulator.simulate_smart_selection()
- )
- num_rounds_greedy, _, _ = (
- energy_simulator.simulate_greedy_selection()
- )
- num_rounds_fl, _ = (
- energy_simulator.simulate_federated_learning()
+ # for cost_received in cost_per_byte_received:
+ cost_received = cost_sent
+ for cost_flop in cost_per_flop:
+ global_params.cost_per_sec = cost_sec
+ global_params.cost_per_byte_sent = cost_sent
+ global_params.cost_per_byte_received = cost_received
+ global_params.cost_per_flop = cost_flop
+ for idx, device in enumerate(device_params_list):
+ device.current_battery = battery[idx]
+ device.comp_latency_factor = latency[idx]
+ device.train_samples = (
+ partition[idx] * total_train_samples
)
- results.append(
- {
- "battery": battery,
- "latency": latency,
- "partition": partition,
- "cost_per_sec": cost_sec,
- "cost_per_byte_sent": cost_sent,
- "cost_per_byte_received": cost_received,
- "cost_per_flop": cost_flop,
- "num_rounds_smart": num_rounds_smart,
- "num_rounds_greedy": num_rounds_greedy,
- "num_rounds_fl": num_rounds_fl,
- }
+ device.validation_samples = (
+ partition[idx] * total_val_samples
)
+ energy_simulator = EnergySimulator(
+ device_params_list, global_params
+ )
+ num_rounds_smart, _, _ = (
+ energy_simulator.simulate_smart_selection()
+ )
+ num_rounds_greedy, _, _ = (
+ energy_simulator.simulate_greedy_selection()
+ )
+ num_rounds_seq, _, _ = (
+ energy_simulator.simulate_sequential_selection()
+ )
+ num_rounds_fl, _ = (
+ energy_simulator.simulate_federated_learning()
+ )
+ results.append(
+ {
+ "battery": battery,
+ "latency": latency,
+ "partition": partition,
+ "cost_per_sec": cost_sec,
+ "cost_per_byte_sent": cost_sent,
+ "cost_per_byte_received": cost_received,
+ "cost_per_flop": cost_flop,
+ "num_rounds_smart": num_rounds_smart,
+ "num_rounds_seq": num_rounds_seq,
+ "num_rounds_greedy": num_rounds_greedy,
+ "num_rounds_fl": num_rounds_fl,
+ }
+ )
return results
diff --git a/edml/controllers/test_controller.py b/edml/controllers/test_controller.py
index ba5e7c187a745d13f8dae9673d3518fc9015b9d2..a3f0ca972c4b98e44e0d05d228db5562239ceb57 100644
--- a/edml/controllers/test_controller.py
+++ b/edml/controllers/test_controller.py
@@ -43,7 +43,7 @@ class TestController(BaseController):
def _get_model_with_highest_postfix_number(self, model_save_path):
"""Returns the highest postfix number in the given directory for the configured model_prefix."""
- model_prefix = self.__model_prefix__()
+ model_prefix = f"{self.__model_prefix__()}_client_" # assume server and client weights were saved appropriately
highest_postfix_number = 0
for file in os.listdir(model_save_path):
if file.startswith(model_prefix):
diff --git a/edml/core/client.py b/edml/core/client.py
index b3c3e7491e718cf98c9ccde598e00fd4d2054996..d8096c7ca5bc479a2aec458f2daf1cc442db4986 100644
--- a/edml/core/client.py
+++ b/edml/core/client.py
@@ -18,7 +18,7 @@ from edml.helpers.decorators import (
from edml.helpers.flops import estimate_model_flops
from edml.helpers.load_optimizer import get_optimizer_and_scheduler
from edml.helpers.metrics import DiagnosticMetricResultContainer, DiagnosticMetricResult
-from edml.helpers.types import StateDict, SLTrainBatchResult
+from edml.helpers.types import StateDict
if TYPE_CHECKING:
from edml.core.device import Device
@@ -136,12 +136,18 @@ class DeviceClient:
@check_device_set()
def train_single_batch(
- self, batch_index: int
+ self, batch_index: int, round_no: int = -1
) -> Optional[torch.Tensor, torch.Tensor]:
torch.cuda.set_device(self._device)
# We have to re-initialize the data loader in the case that we do another epoch.
if batch_index == 0:
self._batchable_data_loader = iter(self._train_data)
+ # update lr scheduler in the beginning of each round
+ if self._lr_scheduler is not None:
+ if round_no != -1:
+ self._lr_scheduler.step(round_no)
+ else:
+ self._lr_scheduler.step()
# Used to measure training time. The problem we have with parallel split learning is that forward- and backward-
# passes are orchestrated by the current server.
@@ -258,6 +264,11 @@ class DeviceClient:
that, this approach does not require to deduce server batch processing time after a "traditional"
measurement.
"""
+ if self._lr_scheduler is not None:
+ if round_no != -1:
+ self._lr_scheduler.step(round_no)
+ else:
+ self._lr_scheduler.step()
client_train_start_time = time.time()
server_train_batch_times = (
[]
@@ -295,12 +306,6 @@ class DeviceClient:
smashed_data.backward(server_grad)
self._optimizer.step()
- if self._lr_scheduler is not None:
- if round_no != -1:
- self._lr_scheduler.step(round_no)
- else:
- self._lr_scheduler.step()
-
client_train_time = (
time.time() - client_train_start_time - sum(server_train_batch_times)
)
diff --git a/edml/core/device.py b/edml/core/device.py
index 1348bea7e4a4fadb44a6bd304bfc466f44bbba41..94bb18ac35241fc86eec778b8ef2d9145a8ed51f 100644
--- a/edml/core/device.py
+++ b/edml/core/device.py
@@ -207,7 +207,9 @@ class Device(ABC):
"""Evaluates a batch on the server of the device with the given id"""
@abstractmethod
- def train_batch_on_client_only_on(self, device_id: str, batch_index: int):
+ def train_batch_on_client_only_on(
+ self, device_id: str, batch_index: int, round_no: int
+ ):
""""""
@abstractmethod
@@ -265,17 +267,23 @@ class NetworkDevice(Device):
@update_battery
@log_execution_time("logger", "client_only_batch_train")
- def train_batch_on_client_only(self, batch_index: int):
- smashed_data, labels = self.client.train_single_batch(batch_index=batch_index)
+ def train_batch_on_client_only(self, batch_index: int, round_no: int):
+ smashed_data, labels = self.client.train_single_batch(
+ batch_index=batch_index, round_no=round_no
+ )
return smashed_data, labels
@update_battery
- def train_batch_on_client_only_on(self, device_id: str, batch_index: int):
+ def train_batch_on_client_only_on(
+ self, device_id: str, batch_index: int, round_no: int
+ ):
if self.device_id == device_id:
- return self.train_batch_on_client_only(batch_index=batch_index)
+ return self.train_batch_on_client_only(
+ batch_index=batch_index, round_no=round_no
+ )
else:
return self.request_dispatcher.train_batch_on_client_only(
- device_id=device_id, batch_index=batch_index
+ device_id=device_id, batch_index=batch_index, round_no=round_no
)
def __init__(
@@ -575,8 +583,11 @@ class RPCDeviceServicer(DeviceServicer):
def TrainSingleBatchOnClient(self, request, context):
batch_index = request.batch_index
+ round_no = request.round_no
- smashed_data, labels = self.device.client.train_single_batch(batch_index)
+ smashed_data, labels = self.device.client.train_single_batch(
+ batch_index, round_no=round_no
+ )
smashed_data = Activations(activations=tensor_to_proto(smashed_data))
labels = Labels(labels=tensor_to_proto(labels))
@@ -955,13 +966,15 @@ class DeviceRequestDispatcher:
return False
def train_batch_on_client_only(
- self, device_id: str, batch_index: int
+ self, device_id: str, batch_index: int, round_no: int
) -> Tuple[Tensor, Tensor] | None:
try:
response: SingleBatchTrainingResponse = self._get_connection(
device_id
).TrainSingleBatchOnClient(
- connection_pb2.SingleBatchTrainingRequest(batch_index=batch_index)
+ connection_pb2.SingleBatchTrainingRequest(
+ batch_index=batch_index, round_no=round_no
+ )
)
# The response can only be None if the last batch was smaller than the configured batch size.
diff --git a/edml/core/server.py b/edml/core/server.py
index 5e2f8235de476c5a998f9c8c040b462ac9d3b198..4b9ef80fd876d618075a98a14d61e03d83369b66 100644
--- a/edml/core/server.py
+++ b/edml/core/server.py
@@ -90,6 +90,11 @@ class DeviceServer:
if optimizer_state is not None:
self._optimizer.load_state_dict(optimizer_state)
for epoch in range(epochs):
+ if self._lr_scheduler is not None:
+ if round_no != -1:
+ self._lr_scheduler.step(round_no + epoch)
+ else:
+ self._lr_scheduler.step()
for device_id in devices:
print(
f"Train epoch {epoch} on client {device_id} with server {self.node_device.device_id}"
@@ -120,11 +125,6 @@ class DeviceServer:
metrics.add_results(train_metrics)
metrics.add_results(val_metrics)
- if self._lr_scheduler is not None:
- if round_no != -1:
- self._lr_scheduler.step(round_no + epoch)
- else:
- self._lr_scheduler.step()
return (
client_weights,
self.get_weights(),
@@ -220,7 +220,10 @@ class DeviceServer:
):
def client_training_job(client_id: str, batch_index: int):
result = self.node_device.train_batch_on_client_only_on(
- device_id=client_id, batch_index=batch_index
+ device_id=client_id,
+ batch_index=batch_index,
+ round_no=round_no,
+ # round_no is taken from outer method arg
)
return (client_id, result)
@@ -241,6 +244,12 @@ class DeviceServer:
if optimizer_state is not None:
self._optimizer.load_state_dict(optimizer_state)
+ if self._lr_scheduler is not None:
+ if round_no != -1:
+ self._lr_scheduler.step(round_no + 1) # epoch=1
+ else:
+ self._lr_scheduler.step()
+
num_threads = len(clients)
executor = create_executor_with_threads(num_threads)
@@ -289,7 +298,9 @@ class DeviceServer:
print(
f"\n{Fore.RED}ADAPTIVE TRESHOLD REACHED, NEXT BATCH\n{Fore.RESET}"
)
- self.node_device.log({"adaptive_learning_threshold_applied": True})
+ self.node_device.log(
+ {"adaptive_learning_threshold_applied": server_gradients.size(0)}
+ )
continue
num_client_gradients = len(client_forward_pass_responses)
@@ -346,11 +357,6 @@ class DeviceServer:
model_metrics.add_results(val_metrics)
optimizer_state = self._optimizer.state_dict()
- if self._lr_scheduler is not None:
- if round_no != -1:
- self._lr_scheduler.step(round_no + 1) # epoch=1
- else:
- self._lr_scheduler.step()
# delete references and free GPU memory manually
server_batch = None
server_labels = None
diff --git a/edml/generated/connection_pb2.py b/edml/generated/connection_pb2.py
index ce271bbab4fa5d30e0e8c5a0a8b309d96eaccf07..f3c72442f44b99d587925a030002e1af92924ac8 100644
--- a/edml/generated/connection_pb2.py
+++ b/edml/generated/connection_pb2.py
@@ -14,7 +14,7 @@ _sym_db = _symbol_database.Default()
import datastructures_pb2 as datastructures__pb2
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x10\x63onnection.proto\x1a\x14\x64\x61tastructures.proto\"4\n\x13SetGradientsRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\"5\n\x14UpdateWeightsRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\";\n\x1aSingleBatchBackwardRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\"j\n\x1bSingleBatchBackwardResponse\x12\x19\n\x07metrics\x18\x01 \x01(\x0b\x32\x08.Metrics\x12\"\n\tgradients\x18\x02 \x01(\x0b\x32\n.GradientsH\x00\x88\x01\x01\x42\x0c\n\n_gradients\"1\n\x1aSingleBatchTrainingRequest\x12\x13\n\x0b\x62\x61tch_index\x18\x01 \x01(\x05\"\x80\x01\n\x1bSingleBatchTrainingResponse\x12\'\n\x0csmashed_data\x18\x01 \x01(\x0b\x32\x0c.ActivationsH\x00\x88\x01\x01\x12\x1c\n\x06labels\x18\x02 \x01(\x0b\x32\x07.LabelsH\x01\x88\x01\x01\x42\x0f\n\r_smashed_dataB\t\n\x07_labels\"\xd5\x01\n\'TrainGlobalParallelSplitLearningRequest\x12\x15\n\x08round_no\x18\x01 \x01(\x05H\x00\x88\x01\x01\x12(\n\x1b\x61\x64\x61ptive_learning_threshold\x18\x02 \x01(\x01H\x01\x88\x01\x01\x12(\n\x0foptimizer_state\x18\x03 \x01(\x0b\x32\n.StateDictH\x02\x88\x01\x01\x42\x0b\n\t_round_noB\x1e\n\x1c_adaptive_learning_thresholdB\x12\n\x10_optimizer_state\"\x89\x02\n(TrainGlobalParallelSplitLearningResponse\x12 \n\x0e\x63lient_weights\x18\x01 \x01(\x0b\x32\x08.Weights\x12 \n\x0eserver_weights\x18\x02 \x01(\x0b\x32\x08.Weights\x12\x19\n\x07metrics\x18\x03 \x01(\x0b\x32\x08.Metrics\x12(\n\x0foptimizer_state\x18\x04 \x01(\x0b\x32\n.StateDictH\x00\x88\x01\x01\x12)\n\x12\x64iagnostic_metrics\x18\x05 \x01(\x0b\x32\x08.MetricsH\x01\x88\x01\x01\x42\x12\n\x10_optimizer_stateB\x15\n\x13_diagnostic_metrics\"\x86\x01\n\x12TrainGlobalRequest\x12\x0e\n\x06\x65pochs\x18\x01 \x01(\x05\x12\x15\n\x08round_no\x18\x02 \x01(\x05H\x00\x88\x01\x01\x12(\n\x0foptimizer_state\x18\x03 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+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x10\x63onnection.proto\x1a\x14\x64\x61tastructures.proto\"4\n\x13SetGradientsRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\"5\n\x14UpdateWeightsRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\";\n\x1aSingleBatchBackwardRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\"j\n\x1bSingleBatchBackwardResponse\x12\x19\n\x07metrics\x18\x01 \x01(\x0b\x32\x08.Metrics\x12\"\n\tgradients\x18\x02 \x01(\x0b\x32\n.GradientsH\x00\x88\x01\x01\x42\x0c\n\n_gradients\"C\n\x1aSingleBatchTrainingRequest\x12\x13\n\x0b\x62\x61tch_index\x18\x01 \x01(\x05\x12\x10\n\x08round_no\x18\x02 \x01(\x05\"\x80\x01\n\x1bSingleBatchTrainingResponse\x12\'\n\x0csmashed_data\x18\x01 \x01(\x0b\x32\x0c.ActivationsH\x00\x88\x01\x01\x12\x1c\n\x06labels\x18\x02 \x01(\x0b\x32\x07.LabelsH\x01\x88\x01\x01\x42\x0f\n\r_smashed_dataB\t\n\x07_labels\"\xd5\x01\n\'TrainGlobalParallelSplitLearningRequest\x12\x15\n\x08round_no\x18\x01 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_globals = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
@@ -31,61 +31,61 @@ if _descriptor._USE_C_DESCRIPTORS == False:
_globals['_SINGLEBATCHBACKWARDRESPONSE']._serialized_start=212
_globals['_SINGLEBATCHBACKWARDRESPONSE']._serialized_end=318
_globals['_SINGLEBATCHTRAININGREQUEST']._serialized_start=320
- _globals['_SINGLEBATCHTRAININGREQUEST']._serialized_end=369
- _globals['_SINGLEBATCHTRAININGRESPONSE']._serialized_start=372
- _globals['_SINGLEBATCHTRAININGRESPONSE']._serialized_end=500
- _globals['_TRAINGLOBALPARALLELSPLITLEARNINGREQUEST']._serialized_start=503
- _globals['_TRAINGLOBALPARALLELSPLITLEARNINGREQUEST']._serialized_end=716
- _globals['_TRAINGLOBALPARALLELSPLITLEARNINGRESPONSE']._serialized_start=719
- _globals['_TRAINGLOBALPARALLELSPLITLEARNINGRESPONSE']._serialized_end=984
- _globals['_TRAINGLOBALREQUEST']._serialized_start=987
- _globals['_TRAINGLOBALREQUEST']._serialized_end=1121
- _globals['_TRAINGLOBALRESPONSE']._serialized_start=1124
- _globals['_TRAINGLOBALRESPONSE']._serialized_end=1368
- _globals['_SETWEIGHTSREQUEST']._serialized_start=1370
- _globals['_SETWEIGHTSREQUEST']._serialized_end=1435
- _globals['_SETWEIGHTSRESPONSE']._serialized_start=1437
- _globals['_SETWEIGHTSRESPONSE']._serialized_end=1523
- _globals['_TRAINEPOCHREQUEST']._serialized_start=1525
- _globals['_TRAINEPOCHREQUEST']._serialized_end=1609
- _globals['_TRAINEPOCHRESPONSE']._serialized_start=1611
- _globals['_TRAINEPOCHRESPONSE']._serialized_end=1724
- _globals['_TRAINBATCHREQUEST']._serialized_start=1726
- _globals['_TRAINBATCHREQUEST']._serialized_end=1806
- _globals['_TRAINBATCHRESPONSE']._serialized_start=1809
- _globals['_TRAINBATCHRESPONSE']._serialized_end=1954
- _globals['_EVALGLOBALREQUEST']._serialized_start=1956
- _globals['_EVALGLOBALREQUEST']._serialized_end=2014
- _globals['_EVALGLOBALRESPONSE']._serialized_start=2016
- _globals['_EVALGLOBALRESPONSE']._serialized_end=2129
- _globals['_EVALREQUEST']._serialized_start=2131
- _globals['_EVALREQUEST']._serialized_end=2193
- _globals['_EVALRESPONSE']._serialized_start=2195
- _globals['_EVALRESPONSE']._serialized_end=2275
- _globals['_EVALBATCHREQUEST']._serialized_start=2277
- _globals['_EVALBATCHREQUEST']._serialized_end=2356
- _globals['_EVALBATCHRESPONSE']._serialized_start=2358
- _globals['_EVALBATCHRESPONSE']._serialized_end=2470
- _globals['_FULLMODELTRAINREQUEST']._serialized_start=2472
- _globals['_FULLMODELTRAINREQUEST']._serialized_end=2531
- _globals['_FULLMODELTRAINRESPONSE']._serialized_start=2534
- _globals['_FULLMODELTRAINRESPONSE']._serialized_end=2740
- _globals['_STARTEXPERIMENTREQUEST']._serialized_start=2742
- _globals['_STARTEXPERIMENTREQUEST']._serialized_end=2766
- _globals['_STARTEXPERIMENTRESPONSE']._serialized_start=2768
- _globals['_STARTEXPERIMENTRESPONSE']._serialized_end=2859
- _globals['_ENDEXPERIMENTREQUEST']._serialized_start=2861
- _globals['_ENDEXPERIMENTREQUEST']._serialized_end=2883
- _globals['_ENDEXPERIMENTRESPONSE']._serialized_start=2885
- _globals['_ENDEXPERIMENTRESPONSE']._serialized_end=2974
- _globals['_BATTERYSTATUSREQUEST']._serialized_start=2976
- _globals['_BATTERYSTATUSREQUEST']._serialized_end=2998
- _globals['_BATTERYSTATUSRESPONSE']._serialized_start=3000
- _globals['_BATTERYSTATUSRESPONSE']._serialized_end=3121
- _globals['_DATASETMODELINFOREQUEST']._serialized_start=3123
- _globals['_DATASETMODELINFOREQUEST']._serialized_end=3148
- _globals['_DATASETMODELINFORESPONSE']._serialized_start=3151
- _globals['_DATASETMODELINFORESPONSE']._serialized_end=3350
- _globals['_DEVICE']._serialized_start=3353
- _globals['_DEVICE']._serialized_end=4497
+ _globals['_SINGLEBATCHTRAININGREQUEST']._serialized_end=387
+ _globals['_SINGLEBATCHTRAININGRESPONSE']._serialized_start=390
+ _globals['_SINGLEBATCHTRAININGRESPONSE']._serialized_end=518
+ _globals['_TRAINGLOBALPARALLELSPLITLEARNINGREQUEST']._serialized_start=521
+ _globals['_TRAINGLOBALPARALLELSPLITLEARNINGREQUEST']._serialized_end=734
+ _globals['_TRAINGLOBALPARALLELSPLITLEARNINGRESPONSE']._serialized_start=737
+ _globals['_TRAINGLOBALPARALLELSPLITLEARNINGRESPONSE']._serialized_end=1002
+ _globals['_TRAINGLOBALREQUEST']._serialized_start=1005
+ _globals['_TRAINGLOBALREQUEST']._serialized_end=1139
+ _globals['_TRAINGLOBALRESPONSE']._serialized_start=1142
+ _globals['_TRAINGLOBALRESPONSE']._serialized_end=1386
+ _globals['_SETWEIGHTSREQUEST']._serialized_start=1388
+ _globals['_SETWEIGHTSREQUEST']._serialized_end=1453
+ _globals['_SETWEIGHTSRESPONSE']._serialized_start=1455
+ _globals['_SETWEIGHTSRESPONSE']._serialized_end=1541
+ _globals['_TRAINEPOCHREQUEST']._serialized_start=1543
+ _globals['_TRAINEPOCHREQUEST']._serialized_end=1627
+ _globals['_TRAINEPOCHRESPONSE']._serialized_start=1629
+ _globals['_TRAINEPOCHRESPONSE']._serialized_end=1742
+ _globals['_TRAINBATCHREQUEST']._serialized_start=1744
+ _globals['_TRAINBATCHREQUEST']._serialized_end=1824
+ _globals['_TRAINBATCHRESPONSE']._serialized_start=1827
+ _globals['_TRAINBATCHRESPONSE']._serialized_end=1972
+ _globals['_EVALGLOBALREQUEST']._serialized_start=1974
+ _globals['_EVALGLOBALREQUEST']._serialized_end=2032
+ _globals['_EVALGLOBALRESPONSE']._serialized_start=2034
+ _globals['_EVALGLOBALRESPONSE']._serialized_end=2147
+ _globals['_EVALREQUEST']._serialized_start=2149
+ _globals['_EVALREQUEST']._serialized_end=2211
+ _globals['_EVALRESPONSE']._serialized_start=2213
+ _globals['_EVALRESPONSE']._serialized_end=2293
+ _globals['_EVALBATCHREQUEST']._serialized_start=2295
+ _globals['_EVALBATCHREQUEST']._serialized_end=2374
+ _globals['_EVALBATCHRESPONSE']._serialized_start=2376
+ _globals['_EVALBATCHRESPONSE']._serialized_end=2488
+ _globals['_FULLMODELTRAINREQUEST']._serialized_start=2490
+ _globals['_FULLMODELTRAINREQUEST']._serialized_end=2549
+ _globals['_FULLMODELTRAINRESPONSE']._serialized_start=2552
+ _globals['_FULLMODELTRAINRESPONSE']._serialized_end=2758
+ _globals['_STARTEXPERIMENTREQUEST']._serialized_start=2760
+ _globals['_STARTEXPERIMENTREQUEST']._serialized_end=2784
+ _globals['_STARTEXPERIMENTRESPONSE']._serialized_start=2786
+ _globals['_STARTEXPERIMENTRESPONSE']._serialized_end=2877
+ _globals['_ENDEXPERIMENTREQUEST']._serialized_start=2879
+ _globals['_ENDEXPERIMENTREQUEST']._serialized_end=2901
+ _globals['_ENDEXPERIMENTRESPONSE']._serialized_start=2903
+ _globals['_ENDEXPERIMENTRESPONSE']._serialized_end=2992
+ _globals['_BATTERYSTATUSREQUEST']._serialized_start=2994
+ _globals['_BATTERYSTATUSREQUEST']._serialized_end=3016
+ _globals['_BATTERYSTATUSRESPONSE']._serialized_start=3018
+ _globals['_BATTERYSTATUSRESPONSE']._serialized_end=3139
+ _globals['_DATASETMODELINFOREQUEST']._serialized_start=3141
+ _globals['_DATASETMODELINFOREQUEST']._serialized_end=3166
+ _globals['_DATASETMODELINFORESPONSE']._serialized_start=3169
+ _globals['_DATASETMODELINFORESPONSE']._serialized_end=3368
+ _globals['_DEVICE']._serialized_start=3371
+ _globals['_DEVICE']._serialized_end=4515
# @@protoc_insertion_point(module_scope)
diff --git a/edml/generated/connection_pb2.pyi b/edml/generated/connection_pb2.pyi
index a9735505e808ee35b156158e972ab6b206e90b0a..89353343aa1c7e39072bd0ea03c891bd2df7b4df 100644
--- a/edml/generated/connection_pb2.pyi
+++ b/edml/generated/connection_pb2.pyi
@@ -32,10 +32,12 @@ class SingleBatchBackwardResponse(_message.Message):
def __init__(self, metrics: _Optional[_Union[_datastructures_pb2.Metrics, _Mapping]] = ..., gradients: _Optional[_Union[_datastructures_pb2.Gradients, _Mapping]] = ...) -> None: ...
class SingleBatchTrainingRequest(_message.Message):
- __slots__ = ["batch_index"]
+ __slots__ = ["batch_index", "round_no"]
BATCH_INDEX_FIELD_NUMBER: _ClassVar[int]
+ ROUND_NO_FIELD_NUMBER: _ClassVar[int]
batch_index: int
- def __init__(self, batch_index: _Optional[int] = ...) -> None: ...
+ round_no: int
+ def __init__(self, batch_index: _Optional[int] = ..., round_no: _Optional[int] = ...) -> None: ...
class SingleBatchTrainingResponse(_message.Message):
__slots__ = ["smashed_data", "labels"]
diff --git a/edml/proto/connection.proto b/edml/proto/connection.proto
index 2f12881a1cf7e3ee6ae2e076b2f9005141cfece2..5755518d01796bdff68957655d4d339dcf02ab1d 100644
--- a/edml/proto/connection.proto
+++ b/edml/proto/connection.proto
@@ -41,6 +41,7 @@ message SingleBatchBackwardResponse {
message SingleBatchTrainingRequest {
int32 batch_index = 1;
+ int32 round_no = 2;
}
message SingleBatchTrainingResponse {