Metric Logging Callback

By default, both the director based flow and the taskrunner API support Tensorboard to log metrics. Once the experiment is over, the logs can be invoked from the workspace with tensorboard --logdir logs. The metrics that are logged by default are:

• Aggregated model validation accuracy (Aggregator/aggregated_model_validate/acc, validate_agg/aggregated_model_validate/acc)

• Locally tuned model validation accuracy (Aggregator/locally_tuned_model_validate/acc, validate_local/locally_tuned_model_validate/acc)

• Train loss (Aggregator/train/train_loss, trained/train/train_loss)

You can also use custom metric logging function for each task via Python* API or command line interface. This function calls on the aggregator node.

Python API

For logging metrics through Tensorboard, once fl_experiment.stream_metrics() is called from the frontend API, it saves logs in the tensorboard format. After the experiment has finished, the logs can be invoked from the workspace with tensorboard --logdir logs.

You could also add your custom metric logging function by defining the function with the follow signature:

def callback_name(node_name, task_name, metric_name, metric, round_number):
    """
    Write metric callback

    Args:
        node_name (str): Name of node, which generate metric
        task_name (str): Name of task
        metric_name (str): Name of metric
        metric (np.ndarray): Metric value
        round_number (int): Round number
    """
    your code

Example of a Metric Callback

This example shows how to use MLFlow logger to log metrics:

import mlflow

def callback_name(node_name, task_name, metric_name, metric, round_number):
    """
    Write metric callback

    Args:
        node_name (str): Name of node, which generate metric
        task_name (str): Name of task
        metric_name (str): Name of metric
        metric (np.ndarray): Metric value
        round_number (int): Round number
    """
    mlflow.log_metrics({f'{node_name}/{task_name}/{metric_name}': float(metric), 'round_number': round_number})

You could view the log results either through UI interactively by typing mlflow ui or through the use of MLflowClient. By default, only the last logged value of the metric is returned. If you want to retrieve all the values of a given metric, uses mlflow.get_metric_history method.

import mlflow
client = mlflow.tracking.MlflowClient()
print(client.get_metric_history("<RUN ID>", "validate_local/locally_tuned_model_validation/accuracy"))

Command Line Interface

For logging through Tensorboard, enable the parameter write_logs : true in aggregator’s plan settings :

aggregator :
  template : openfl.component.Aggregator
  settings :
      write_logs : true

Follow the steps below to write your custom callback function instead. As an example, a full implementation can be found at Federated_Pytorch_MNIST_Tutorial.ipynb and in the torch_cnn_mnist workspace.

1. Define the callback function, like how you defined in Python API, in the src directory in your workspace.

2. Provide a way to your function with the log_metric_callback key in the aggregator section of the plan.yaml file in your workspace.

aggregator :
  defaults : plan/defaults/aggregator.yaml
  template : openfl.component.Aggregator
  settings :
    init_state_path     : save/torch_cnn_mnist_init.pbuf
    best_state_path     : save/torch_cnn_mnist_best.pbuf
    last_state_path     : save/torch_cnn_mnist_last.pbuf
    rounds_to_train     : 10
    write_logs          : true
    log_metric_callback :
      template : src.mnist_utils.callback_name

Example of a Metric Callback

The following is an example of a log metric callback, which writes metric values to the TensorBoard.

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter('./logs/cnn_mnist', flush_secs=5)


def write_metric(node_name, task_name, metric_name, metric, round_number):
    writer.add_scalar("{}/{}/{}".format(node_name, task_name, metric_name),
                    metric, round_number)