HistoryMonitor

This file is originally part of Fed-BioMed

SPDX-License-Identifier: Apache-2.0

Send information from node to researcher during the training

Classes

HistoryMonitor 

HistoryMonitor(node_id, node_name, experiment_id, researcher_id, send)

Send information from node to researcher during the training

Parameters:

Name Type Description Default
nod_id

Unique ID of this node

 required
experiment_id str

TODO

 required
researcher_id str

TODO

 required
client

TODO

 required
Source code in fedbiomed/node/history_monitor.py 
def __init__(
    self,
    node_id: str,
    node_name: str,
    experiment_id: str,
    researcher_id: str,
    send: Callable,
):
    """Simple constructor for the class.

    Args:
        nod_id: Unique ID of this node
        experiment_id: TODO
        researcher_id: TODO
        client: TODO
    """
    self._node_id = node_id
    self._node_name = node_name
    self.experiment_id = experiment_id
    self.researcher_id = researcher_id
    self.send = send

Attributes

experiment_id instance-attribute 
experiment_id = experiment_id
researcher_id instance-attribute 
researcher_id = researcher_id
send instance-attribute 
send = send

Functions

add_scalar 
add_scalar(metric, iteration, epoch, total_samples, batch_samples, num_batches, num_samples_trained=None, train=False, test=False, test_on_global_updates=False, test_on_local_updates=False)

Adds a scalar value to the monitor, and sends an 'AddScalarReply' response to researcher.

Parameters:

Name Type Description Default
metric Dict[str, Union[int, float]]

recorded value

 required
iteration int

current epoch iteration.

 required
epoch int

current epoch

 required
total_samples int

TODO

 required
batch_samples int

TODO

 required
num_batches int

TODO

 required
num_samples_trained int

TODO

 None
train bool

TODO

 False
test bool

TODO

 False
test_on_global_updates bool

TODO

 False
test_on_local_updates bool

TODO

 False
Source code in fedbiomed/node/history_monitor.py 
def add_scalar(
    self,
    metric: Dict[str, Union[int, float]],
    iteration: int,
    epoch: int,
    total_samples: int,
    batch_samples: int,
    num_batches: int,
    num_samples_trained: int = None,
    train: bool = False,
    test: bool = False,
    test_on_global_updates: bool = False,
    test_on_local_updates: bool = False,
) -> None:
    """Adds a scalar value to the monitor, and sends an 'AddScalarReply'
        response to researcher.

    Args:
        metric:  recorded value
        iteration: current epoch iteration.
        epoch: current epoch
        total_samples: TODO
        batch_samples: TODO
        num_batches: TODO
        num_samples_trained: TODO
        train: TODO
        test: TODO
        test_on_global_updates: TODO
        test_on_local_updates: TODO

    """
    self.send(
        FeedbackMessage(
            researcher_id=self.researcher_id,
            scalar=Scalar(
                **{
                    "node_id": self._node_id,
                    "node_name": self._node_name,
                    "experiment_id": self.experiment_id,
                    "train": train,
                    "test": test,
                    "test_on_global_updates": test_on_global_updates,
                    "test_on_local_updates": test_on_local_updates,
                    "metric": metric,
                    "iteration": iteration,
                    "epoch": epoch,
                    "num_samples_trained": num_samples_trained,
                    "total_samples": total_samples,
                    "batch_samples": batch_samples,
                    "num_batches": num_batches,
                }
            ),
        )
    )