配置日志记录

Flower 日志记录器会跟踪联邦学习工作负载中发生的所有核心事件。它默认按照标准信息格式提供信息：

DEFAULT_FORMATTER = logging.Formatter(
    "%(levelname)s %(name)s %(asctime)s | %(filename)s:%(lineno)d | %(message)s"
)

containing relevant information including: log message level (e.g. INFO, DEBUG), a timestamp, the line where the logging took place from, as well as the log message itself. In this way, the logger would typically display information on your terminal as follows:

...
INFO flwr 2023-07-15 15:32:30,935 | server.py:125 | fit progress: (3, 392.5575705766678, {'accuracy': 0.2898}, 13.781953627998519)
DEBUG flwr 2023-07-15 15:32:30,935 | server.py:173 | evaluate_round 3: strategy sampled 25 clients (out of 100)
DEBUG flwr 2023-07-15 15:32:31,388 | server.py:187 | evaluate_round 3 received 25 results and 0 failures
DEBUG flwr 2023-07-15 15:32:31,388 | server.py:222 | fit_round 4: strategy sampled 10 clients (out of 100)
DEBUG flwr 2023-07-15 15:32:32,429 | server.py:236 | fit_round 4 received 10 results and 0 failures
INFO flwr 2023-07-15 15:32:33,516 | server.py:125 | fit progress: (4, 370.3378576040268, {'accuracy': 0.3294}, 16.36216809399957)
DEBUG flwr 2023-07-15 15:32:33,516 | server.py:173 | evaluate_round 4: strategy sampled 25 clients (out of 100)
DEBUG flwr 2023-07-15 15:32:33,966 | server.py:187 | evaluate_round 4 received 25 results and 0 failures
DEBUG flwr 2023-07-15 15:32:33,966 | server.py:222 | fit_round 5: strategy sampled 10 clients (out of 100)
DEBUG flwr 2023-07-15 15:32:34,997 | server.py:236 | fit_round 5 received 10 results and 0 failures
INFO flwr 2023-07-15 15:32:36,118 | server.py:125 | fit progress: (5, 358.6936808824539, {'accuracy': 0.3467}, 18.964264554999318)
...

将日志保存到文件

By default, the Flower log is outputted to the terminal where you launch your Federated Learning workload from. This applies for both gRPC-based federation (i.e. when you do fl.server.start_server) and when using the VirtualClientEngine (i.e. when you do fl.simulation.start_simulation). In some situations you might want to save this log to disk. You can do so by calling the fl.common.logger.configure() function. For example:

import flwr as fl

...

# in your main file and before launching your experiment
# add an identifier to your logger
# then specify the name of the file where the log should be outputted to
fl.common.logger.configure(identifier="myFlowerExperiment", filename="log.txt")

# then start your workload
fl.simulation.start_simulation(...)  # or fl.server.start_server(...)

With the above, Flower will record the log you see on your terminal to log.txt. This file will be created in the same directory as were you are running the code from. If we inspect we see the log above is also recorded but prefixing with identifier each line:

...
myFlowerExperiment | INFO flwr 2023-07-15 15:32:30,935 | server.py:125 | fit progress: (3, 392.5575705766678, {'accuracy': 0.2898}, 13.781953627998519)
myFlowerExperiment | DEBUG flwr 2023-07-15 15:32:30,935 | server.py:173 | evaluate_round 3: strategy sampled 25 clients (out of 100)
myFlowerExperiment | DEBUG flwr 2023-07-15 15:32:31,388 | server.py:187 | evaluate_round 3 received 25 results and 0 failures
myFlowerExperiment | DEBUG flwr 2023-07-15 15:32:31,388 | server.py:222 | fit_round 4: strategy sampled 10 clients (out of 100)
myFlowerExperiment | DEBUG flwr 2023-07-15 15:32:32,429 | server.py:236 | fit_round 4 received 10 results and 0 failures
myFlowerExperiment | INFO flwr 2023-07-15 15:32:33,516 | server.py:125 | fit progress: (4, 370.3378576040268, {'accuracy': 0.3294}, 16.36216809399957)
myFlowerExperiment | DEBUG flwr 2023-07-15 15:32:33,516 | server.py:173 | evaluate_round 4: strategy sampled 25 clients (out of 100)
myFlowerExperiment | DEBUG flwr 2023-07-15 15:32:33,966 | server.py:187 | evaluate_round 4 received 25 results and 0 failures
myFlowerExperiment | DEBUG flwr 2023-07-15 15:32:33,966 | server.py:222 | fit_round 5: strategy sampled 10 clients (out of 100)
myFlowerExperiment | DEBUG flwr 2023-07-15 15:32:34,997 | server.py:236 | fit_round 5 received 10 results and 0 failures
myFlowerExperiment | INFO flwr 2023-07-15 15:32:36,118 | server.py:125 | fit progress: (5, 358.6936808824539, {'accuracy': 0.3467}, 18.964264554999318)
...

记录自己的信息

您可以通过添加更多与应用程序相关的信息来扩展 Flower 日志记录器默认显示的信息。您可以通过以下方法轻松实现这一目标。

# in the python file you want to add custom messages to the Flower log
from logging import INFO, DEBUG
from flwr.common.logger import log

# For example, let's say you want to add to the log some info about the training on your client for debugging purposes


class FlowerClient(fl.client.NumPyClient):
    def __init__(
        self,
        cid: int,
        # ...
    ):
        self.cid = cid
        self.net = net
        # ...

    def fit(self, parameters, config):
        log(INFO, f"Printing a custom INFO message at the start of fit() :)")

        set_params(self.net, parameters)

        log(DEBUG, f"Client {self.cid} is doing fit() with config: {config}")

        # ...

这样，除默认信息外，您的日志记录器还将显示由客户引入的信息，如上文所述。

...
INFO flwr 2023-07-15 16:18:21,726 | server.py:89 | Initializing global parameters
INFO flwr 2023-07-15 16:18:21,726 | server.py:276 | Requesting initial parameters from one random client
INFO flwr 2023-07-15 16:18:22,511 | server.py:280 | Received initial parameters from one random client
INFO flwr 2023-07-15 16:18:22,511 | server.py:91 | Evaluating initial parameters
INFO flwr 2023-07-15 16:18:25,200 | server.py:94 | initial parameters (loss, other metrics): 461.2934241294861, {'accuracy': 0.0998}
INFO flwr 2023-07-15 16:18:25,200 | server.py:104 | FL starting
DEBUG flwr 2023-07-15 16:18:25,200 | server.py:222 | fit_round 1: strategy sampled 10 clients (out of 100)
INFO flwr 2023-07-15 16:18:26,391 | main.py:64 | Printing a custom INFO message :)
DEBUG flwr 2023-07-15 16:18:26,391 | main.py:63 | Client 44 is doing fit() with config: {'epochs': 5, 'batch_size': 64}
INFO flwr 2023-07-15 16:18:26,391 | main.py:64 | Printing a custom INFO message :)
DEBUG flwr 2023-07-15 16:18:28,464 | main.py:63 | Client 99 is doing fit() with config: {'epochs': 5, 'batch_size': 64}
INFO flwr 2023-07-15 16:18:28,465 | main.py:64 | Printing a custom INFO message :)
DEBUG flwr 2023-07-15 16:18:28,519 | main.py:63 | Client 67 is doing fit() with config: {'epochs': 5, 'batch_size': 64}
INFO flwr 2023-07-15 16:18:28,519 | main.py:64 | Printing a custom INFO message :)
DEBUG flwr 2023-07-15 16:18:28,615 | main.py:63 | Client 11 is doing fit() with config: {'epochs': 5, 'batch_size': 64}
INFO flwr 2023-07-15 16:18:28,615 | main.py:64 | Printing a custom INFO message :)
DEBUG flwr 2023-07-15 16:18:28,617 | main.py:63 | Client 13 is doing fit() with config: {'epochs': 5, 'batch_size': 64}
...

登录远程服务

The fl.common.logger.configure function, also allows specifying a host to which logs can be pushed (via POST) through a native Python logging.handler.HTTPHandler. This is a particularly useful feature in gRPC-based Federated Learning workloads where otherwise gathering logs from all entities (i.e. the server and the clients) might be cumbersome. Note that in Flower simulation, the server automatically displays all logs. You can still specify a HTTPHandler should you wish to backup or analyze the logs somewhere else.