From 3214589cb61d57a7cc372ba44605cd31f9edd7ed Mon Sep 17 00:00:00 2001
From: HuongDM1896 <domaihuong1451997@gmail.com>
Date: Mon, 3 Feb 2025 08:13:26 +0100
Subject: [PATCH] add get_freq
---
Flower_v1/server.py | 30 +++++--
Flower_v1/server_1.py | 175 ++++++++++++++++++++++++++++++++++++++
Run/config_instance1.json | 44 +++++++---
Run/config_instance2.json | 48 +++++++----
Run/get_freq.py | 10 +++
Run/measure.py | 27 ++++--
Run/measure_1.py | 91 ++++++++++++++++++++
Run/run_flwr.py | 85 +-----------------
8 files changed, 384 insertions(+), 126 deletions(-)
create mode 100644 Flower_v1/server_1.py
create mode 100644 Run/get_freq.py
create mode 100644 Run/measure_1.py
diff --git a/Flower_v1/server.py b/Flower_v1/server.py
index 0c40e10..d736363 100644
--- a/Flower_v1/server.py
+++ b/Flower_v1/server.py
@@ -1,9 +1,10 @@
from typing import List, Tuple
-
from flwr.server import ServerApp, ServerConfig
-from flwr.server.strategy import FedAvg, FedMedian
-from flwr.common import Metrics
+from flwr.server.strategy import FedAvg, FedMedian, FedOpt, FedAdam
+from flwr.common import Metrics, Parameters
import argparse
+import tensorflow as tf
+import flwr
# Create the argument parser
parser = argparse.ArgumentParser(description="Server setting for FLWR")
@@ -17,8 +18,8 @@ parser.add_argument(
"--strategy",
type=str,
default="fedAvg",
- choices=["fedAvg", "fedMedian"], # Add options strategies
- help="Select strategy: fedAvg or fedMedian"
+ choices=["fedAvg", "fedMedian", "fedOpt", "fedAdam"], # Add options strategies
+ help="Select strategy: fedAvg, fedMedian, fedOpt or fedAdam"
)
# Parse the arguments from the command line
@@ -33,12 +34,27 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
# Aggregate and return custom metric (weighted average)
return {"accuracy": sum(accuracies) / sum(examples)}
+# Load model for server-side parameter initialization
+model = tf.keras.applications.MobileNetV2(
+ input_shape=(32, 32, 3), weights=None, classes=10
+)
+model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
+
+# Get model weights as a list of NumPy ndarray's
+weights = model.get_weights()
+
+# Serialize ndarrays to `Parameters`
+parameters = flwr.common.ndarrays_to_parameters(weights)
+
# Select the strategy based on the input argument
if args.strategy == "fedAvg":
strategy = FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
elif args.strategy == "fedMedian":
strategy = FedMedian(evaluate_metrics_aggregation_fn=weighted_average)
-
+elif args.strategy == "fedOpt":
+ strategy = FedOpt(evaluate_metrics_aggregation_fn=weighted_average, initial_parameters=parameters)
+elif args.strategy == "fedAdam":
+ strategy = FedAdam(evaluate_metrics_aggregation_fn=weighted_average, initial_parameters=parameters)
# Define config
config = ServerConfig(num_rounds=args.rounds)
@@ -59,4 +75,4 @@ if __name__ == "__main__":
server_address="0.0.0.0:8080",
config=config,
strategy=strategy,
- )
\ No newline at end of file
+ )
diff --git a/Flower_v1/server_1.py b/Flower_v1/server_1.py
new file mode 100644
index 0000000..8bf37a0
--- /dev/null
+++ b/Flower_v1/server_1.py
@@ -0,0 +1,175 @@
+import csv
+import time
+import os
+from datetime import datetime
+from typing import List, Tuple, Dict, Optional
+from flwr.common import Parameters, Metrics
+from flwr.server.strategy import Strategy
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server import ServerApp, ServerConfig
+from flwr.server.strategy import FedAvg, FedMedian, FedOpt, FedAdam
+import argparse
+import tensorflow as tf
+import flwr
+
+# Create the folder and generate the CSV filename dynamically
+def create_csv_filepath(strategy_name: str, num_rounds: int) -> str:
+ # Determine the parent directory of the current script
+ parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+ # Create the "Log_autres" directory if it doesn't exist
+ log_dir = os.path.join(parent_dir, "Log_autres")
+ os.makedirs(log_dir, exist_ok=True)
+
+ # Generate a timestamp for the file
+ timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+ # Generate the CSV file path
+ csv_filename = f"training_results_{strategy_name}_{num_rounds}_{timestamp}.csv"
+ csv_filepath = os.path.join(log_dir, csv_filename)
+
+ return csv_filepath
+
+class CustomStrategyWrapper(Strategy):
+ def __init__(self, strategy: Strategy, csv_filename: str):
+ self.strategy = strategy
+ self.csv_filename = csv_filename
+
+ # Initialize CSV file and write header
+ with open(self.csv_filename, mode="w", newline="") as f:
+ writer = csv.writer(f)
+ writer.writerow(["round", "time", "accuracy"]) # Header
+
+ def initialize_parameters(self, client_manager: ClientManager) -> Optional[Parameters]:
+ return self.strategy.initialize_parameters(client_manager)
+
+ def configure_fit(
+ self, server_round: int, parameters: Parameters, client_manager: ClientManager
+ ):
+ # Log the round start time
+ self.round_start_time = time.time()
+ return self.strategy.configure_fit(server_round, parameters, client_manager)
+
+ def aggregate_fit(
+ self,
+ server_round: int,
+ results: List[Tuple[ClientProxy, Metrics]],
+ failures: List[BaseException],
+ ):
+ # Call the actual strategy's aggregate_fit
+ return self.strategy.aggregate_fit(server_round, results, failures)
+
+ def configure_evaluate(
+ self, server_round: int, parameters: Parameters, client_manager: ClientManager
+ ):
+ return self.strategy.configure_evaluate(server_round, parameters, client_manager)
+
+ def aggregate_evaluate(
+ self,
+ server_round: int,
+ results: List[Tuple[ClientProxy, Metrics]],
+ failures: List[BaseException],
+ ):
+ # Call the actual strategy's aggregate_evaluate
+ aggregated_metrics = self.strategy.aggregate_evaluate(server_round, results, failures)
+
+ # Calculate round duration
+ round_time = time.time() - self.round_start_time
+
+ # Extract accuracy from aggregated_metrics
+ accuracy = aggregated_metrics[1].get("accuracy", 0) if aggregated_metrics else 0
+
+ # Append data to CSV
+ with open(self.csv_filename, mode="a", newline="") as f:
+ writer = csv.writer(f)
+ writer.writerow([server_round, round_time, accuracy])
+
+ return aggregated_metrics
+
+ def evaluate(
+ self, server_round: int, parameters: Parameters
+ ) -> Optional[Tuple[float, Metrics]]:
+ return self.strategy.evaluate(server_round, parameters)
+
+
+# Create the argument parser
+parser = argparse.ArgumentParser(description="Server setting for FLWR")
+
+# Add the argument for num_rounds (-r or --rounds)
+parser.add_argument("-r", "--rounds", type=int, default=10, help="Number of rounds")
+
+# Add the argument for strategy (-s or --strategy)
+parser.add_argument(
+ "-s",
+ "--strategy",
+ type=str,
+ default="fedAvg",
+ choices=["fedAvg", "fedMedian", "fedOpt", "fedAdam", "fedAvg2Clients"], # Add options strategies
+ help="Select strategy: fedAvg, fedMedian, fedOpt or fedAdam or fedAvg2Clients",
+)
+
+# Parse the arguments from the command line
+args = parser.parse_args()
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+ # Multiply accuracy of each client by number of examples used
+ accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+ examples = [num_examples for num_examples, _ in metrics]
+
+ # Aggregate and return custom metric (weighted average)
+ return {"accuracy": sum(accuracies) / sum(examples)}
+
+# Load model for server-side parameter initialization
+model = tf.keras.applications.MobileNetV2(
+ input_shape=(32, 32, 3), weights=None, classes=10
+)
+model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
+
+# Get model weights as a list of NumPy ndarray's
+weights = model.get_weights()
+
+# Serialize ndarrays to `Parameters`
+parameters = flwr.common.ndarrays_to_parameters(weights)
+
+# Select the base strategy
+if args.strategy == "fedAvg":
+ base_strategy = FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
+elif args.strategy == "fedAvg2Clients":
+ base_strategy = FedAvg(
+ fraction_fit=0.67,
+ min_fit_clients=2,
+ min_available_clients=2,
+ evaluate_metrics_aggregation_fn=weighted_average,
+ )
+elif args.strategy == "fedMedian":
+ base_strategy = FedMedian(evaluate_metrics_aggregation_fn=weighted_average)
+elif args.strategy == "fedOpt":
+ base_strategy = FedOpt(
+ evaluate_metrics_aggregation_fn=weighted_average, initial_parameters=parameters
+ )
+elif args.strategy == "fedAdam":
+ base_strategy = FedAdam(
+ evaluate_metrics_aggregation_fn=weighted_average, initial_parameters=parameters
+ )
+
+
+# Generate the CSV file path based on strategy and number of rounds
+csv_filepath = create_csv_filepath(strategy_name=args.strategy, num_rounds=args.rounds)
+
+# Wrap the selected strategy with the logging wrapper
+strategy = CustomStrategyWrapper(strategy=base_strategy, csv_filename=csv_filepath)
+
+# Define config
+config = ServerConfig(num_rounds=args.rounds)
+
+# Legacy mode
+if __name__ == "__main__":
+ from flwr.server import start_server
+
+ start_server(
+ server_address="0.0.0.0:8080",
+ config=config,
+ strategy=strategy,
+ )
diff --git a/Run/config_instance1.json b/Run/config_instance1.json
index 2b4f97c..d69ccd7 100644
--- a/Run/config_instance1.json
+++ b/Run/config_instance1.json
@@ -1,17 +1,27 @@
{
"instance": "fedAvg_cifar10",
- "output_dir": "/home/mdo/Test/eflwr/Log",
+ "output_dir": "/home/mdo/Framework/eflwr/Log",
+ "dvfs": {
+ "dummy": false,
+ "baseline": false,
+ "frequencies": [
+ 1000000,
+ 1400000,
+ 1800000,
+ 2200000
+ ]
+ },
"server": {
"command": "python3",
"args": [
- "/home/mdo/Test/eflwr/Flower_v1/server.py",
- "-r 1",
+ "/home/mdo/Framework/eflwr/Flower_v1/server_1.py",
+ "-r 25",
"-s fedAvg"
],
"additional_env_var": [
""
],
- "ip": "172.16.66.26",
+ "ip": "172.16.66.76",
"port": 8080
},
"clients": [
@@ -19,37 +29,43 @@
"name": "client1",
"command": "python3",
"args": [
- "/home/mdo/Test/eflwr/Flower_v1/client.py",
- "cifar10"
+ "/home/mdo/Framework/eflwr/Flower_v1/client_1.py",
+ "cifar10",
+ "1",
+ "3"
],
"additional_env_var": [
""
],
- "ip": "172.16.66.38"
+ "ip": "172.16.66.77"
},
{
"name": "client2",
"command": "python3",
"args": [
- "/home/mdo/Test/eflwr/Flower_v1/client.py",
- "cifar10"
+ "/home/mdo/Framework/eflwr/Flower_v1/client_1.py",
+ "cifar10",
+ "2",
+ "3"
],
"additional_env_var": [
""
],
- "ip": "172.16.66.4"
+ "ip": "172.16.66.78"
},
{
"name": "client3",
"command": "python3",
"args": [
- "/home/mdo/Test/eflwr/Flower_v1/client.py",
- "cifar10"
+ "/home/mdo/Framework/eflwr/Flower_v1/client_1.py",
+ "cifar10",
+ "3",
+ "3"
],
"additional_env_var": [
""
],
- "ip": "172.16.66.65"
+ "ip": "172.16.66.79"
}
]
-}
+}
\ No newline at end of file
diff --git a/Run/config_instance2.json b/Run/config_instance2.json
index 72d7c8b..fdc92f6 100644
--- a/Run/config_instance2.json
+++ b/Run/config_instance2.json
@@ -1,17 +1,27 @@
{
- "instance": "fedMedian_cifar10",
- "output_dir": "/home/mdo/Test/eflwr/Log",
+ "instance": "fedAvg2Clients_cifar10",
+ "output_dir": "/home/mdo/Framework/eflwr/Log",
+ "dvfs": {
+ "dummy": false,
+ "baseline": false,
+ "frequencies": [
+ 1000000,
+ 1400000,
+ 1800000,
+ 2200000
+ ]
+ },
"server": {
"command": "python3",
"args": [
- "/home/mdo/Test/eflwr/Flower_v1/server.py",
- "-r 1",
- "-s fedMedian"
+ "/home/mdo/Framework/eflwr/Flower_v1/server_1.py",
+ "-r 25",
+ "-s fedAvg2Clients"
],
"additional_env_var": [
""
],
- "ip": "172.16.66.26",
+ "ip": "172.16.66.76",
"port": 8080
},
"clients": [
@@ -19,37 +29,43 @@
"name": "client1",
"command": "python3",
"args": [
- "/home/mdo/Test/eflwr/Flower_v1/client.py",
- "cifar10"
+ "/home/mdo/Framework/eflwr/Flower_v1/client_1.py",
+ "cifar10",
+ "1",
+ "3"
],
"additional_env_var": [
""
],
- "ip": "172.16.66.38"
+ "ip": "172.16.66.77"
},
{
"name": "client2",
"command": "python3",
"args": [
- "/home/mdo/Test/eflwr/Flower_v1/client.py",
- "cifar10"
+ "/home/mdo/Framework/eflwr/Flower_v1/client_1.py",
+ "cifar10",
+ "2",
+ "3"
],
"additional_env_var": [
""
],
- "ip": "172.16.66.4"
+ "ip": "172.16.66.78"
},
{
"name": "client3",
"command": "python3",
"args": [
- "/home/mdo/Test/eflwr/Flower_v1/client.py",
- "cifar10"
+ "/home/mdo/Framework/eflwr/Flower_v1/client_1.py",
+ "cifar10",
+ "3",
+ "3"
],
"additional_env_var": [
""
],
- "ip": "172.16.66.65"
+ "ip": "172.16.66.79"
}
]
-}
+}
\ No newline at end of file
diff --git a/Run/get_freq.py b/Run/get_freq.py
new file mode 100644
index 0000000..f5bccd2
--- /dev/null
+++ b/Run/get_freq.py
@@ -0,0 +1,10 @@
+from expetator.leverages import dvfs
+#import subprocess
+
+#subprocess.run(["bash","/home/hdomai/.local/lib/python3.10/site-packages/expetator/leverages/dvfs_pct.sh", "init"], check=True)
+frequencies, pcts = dvfs.get_dvfs_values()
+
+print("Cpu freq available (min, max, delta):", frequencies)
+f=list(frequencies)
+print("List", f)
+print("4 freqquencies for testing", f[0], f[len(f)//3], f[len(f)*2//3], f[-1])
diff --git a/Run/measure.py b/Run/measure.py
index 6fb40be..9ed5480 100644
--- a/Run/measure.py
+++ b/Run/measure.py
@@ -6,7 +6,9 @@ import argparse
import json
import time
import expetator.experiment as experiment
-from expetator.monitors import Mojitos, kwollect
+#from expetator.monitors import Mojitos, kwollect
+from expetator.monitors import Mojitos
+from expetator.leverages import Dvfs
# Determine script directory
current_dir = Path(__file__).resolve().parent
@@ -48,6 +50,12 @@ except json.JSONDecodeError:
log_dir = config["output_dir"]
instance_name = config.get("instance", "default_instance")
+# Extract DVFS configuration from the config file
+dvfs_config = config.get("dvfs", {})
+dvfs_dummy = dvfs_config.get("dummy", False)
+dvfs_baseline = dvfs_config.get("baseline", False)
+dvfs_frequencies = dvfs_config.get("frequencies", None)
+
# Set the Flower log directory with the suffix and ensure it exists
flower_log_dir = os.path.join(log_dir, f"Flower_{args.suffix}", f"Flower_instance_{instance_name}", "Expetator")
os.makedirs(flower_log_dir, exist_ok=True)
@@ -55,6 +63,12 @@ os.makedirs(flower_log_dir, exist_ok=True)
# Path to the script that will be executed
script_dir = os.path.join(current_dir, 'run_flwr.py')
+# SCP the config file to the destination
+
+scp_command = f"scp {config_path} {flower_log_dir}"
+print(f"Executing SCP command: {scp_command}")
+os.system(scp_command)
+
class DemoBench:
def __init__(self, params=[args.suffix]):
self.names = {"flower"}
@@ -69,15 +83,18 @@ class DemoBench:
# Run the Flower script with the provided suffix argument
executor.local(f"python3 {script_dir} -c {args.config} -x {args.suffix}")
return time.time() - before, "flower"
-
+
if __name__ == "__main__":
+# Ensure DVFS settings are retrieved from the config file
+ dvfs = Dvfs(dummy=dvfs_dummy, baseline=dvfs_baseline, frequencies=dvfs_frequencies)
experiment.run_experiment(
flower_log_dir,
[DemoBench()],
- leverages=[],
+ leverages=[dvfs],
monitors=[
- Mojitos(sensor_set={'user', 'rxp', 'dram0'}),
- kwollect.Power(metric=kwollect.get_g5k_target_metric())
+ Mojitos(sensor_set={'user', 'rxp', 'dram0'})
+ # kwollect.Power(metric=kwollect.get_g5k_target_metric())
],
times=args.repeat
)
+
diff --git a/Run/measure_1.py b/Run/measure_1.py
new file mode 100644
index 0000000..1863c81
--- /dev/null
+++ b/Run/measure_1.py
@@ -0,0 +1,91 @@
+#python3 measure.py -c config_instance1.json -x test -r repeat
+
+from pathlib import Path
+import os
+import argparse
+import json
+import time
+import expetator.experiment as experiment
+from expetator.monitors import Mojitos, kwollect
+from expetator.leverages import Dvfs
+
+# Determine script directory
+current_dir = Path(__file__).resolve().parent
+parent_dir = current_dir.parent
+
+# Set up argument parser
+parser = argparse.ArgumentParser(description="Run a benchmark experiment using a specified config file.")
+parser.add_argument(
+ "-x", "--suffix", type=str, required=True,
+ help="Suffix for the log directory (e.g., experiment name or timestamp)"
+)
+parser.add_argument(
+ "-c", "--config", type=str, required=True,
+ help="Path to the config file (e.g., config_instance1.json or a glob pattern like config_instance*.json)"
+)
+parser.add_argument(
+ "-r", "--repeat", type=int, default=1, required=True,
+ help="Number of repeatation (e.g., 2, the exp will be repeated in 2 times)"
+)
+
+# Parse arguments
+args = parser.parse_args()
+
+# Dynamically set the path to the config.json file
+config_path = os.path.join(current_dir, args.config)
+
+# Read the output directory from config.json
+try:
+ with open(config_path, "r") as file:
+ config = json.load(file)
+except FileNotFoundError:
+ print(f"Error: Config file {config_path} not found!")
+ exit(1)
+except json.JSONDecodeError:
+ print(f"Error: Config file {config_path} contains invalid JSON!")
+ exit(1)
+
+# Base log directory and instance name from config.json
+log_dir = config["output_dir"]
+instance_name = config.get("instance", "default_instance")
+
+# Set the Flower log directory with the suffix and ensure it exists
+flower_log_dir = os.path.join(log_dir, f"Flower_{args.suffix}", f"Flower_instance_{instance_name}", "Expetator")
+os.makedirs(flower_log_dir, exist_ok=True)
+
+# Path to the script that will be executed
+script_dir = os.path.join(current_dir, 'run_flwr.py')
+
+# SCP the config file to the destination
+
+scp_command = f"scp {config_path} {flower_log_dir}"
+print(f"Executing SCP command: {scp_command}")
+os.system(scp_command)
+
+class DemoBench:
+ def __init__(self, params=[args.suffix]):
+ self.names = {"flower"}
+ self.params = params
+
+ def build(self, executor):
+ params = {"flower": self.params}
+ return params
+
+ def run(self, bench, param, executor):
+ before = time.time()
+ # Run the Flower script with the provided suffix argument
+ executor.local(f"python3 {script_dir} -c {args.config} -x {args.suffix}")
+ return time.time() - before, "flower"
+
+if __name__ == "__main__":
+ experiment.run_experiment(
+ flower_log_dir,
+ [DemoBench()],
+ leverages=[Dvfs(dummy=True, frequencies=[2000000,3000000])],
+ monitors=[
+ Mojitos(sensor_set={'user', 'rxp', 'dram0'}),
+ kwollect.Power(metric=kwollect.get_g5k_target_metric())
+ ],
+ times=args.repeat
+ )
+
diff --git a/Run/run_flwr.py b/Run/run_flwr.py
index 5b48054..680ba43 100644
--- a/Run/run_flwr.py
+++ b/Run/run_flwr.py
@@ -63,81 +63,6 @@ flower_dir = os.path.join(output_dir, f"Flower_{experiment_suffix}")
log_exp_dir = os.path.join(flower_dir, f"Flower_instance_{instance_name}", f"Flwr_{current_time}")
os.makedirs(log_exp_dir, exist_ok=True)
-# Define the log-checking function
-def check_flower_logs(client_logs, server_log, output_file):
- """
- Check Flower client and server logs for success indicators.
-
- Args:
- client_logs (list of str): Paths to the client log files.
- server_log (str): Path to the server log file.
- output_file (str): Path to the output log file to write the result.
- """
- client_success_keywords = ["Sent reply", "Received", "Disconnect and shut down"]
- server_success_keywords = ["[SUMMARY]", "Run finished", "History (metrics, distributed, evaluate)"]
-
- # Helper function to check if all keywords are in the log content
- def check_keywords_in_log(log_path, keywords):
- if not os.path.exists(log_path):
- return False, f"Log file not found: {log_path}"
- try:
- with open(log_path, "r") as file:
- lines = file.readlines()[-10:] # Read the last 10 lines for efficiency
- log_content = "".join(lines)
- return all(keyword in log_content for keyword in keywords), log_content
- except Exception as e:
- return False, f"Error reading log file {log_path}: {e}"
-
- # Check client logs
- client_results = []
- for log_path in client_logs:
- success, log_content = check_keywords_in_log(log_path, client_success_keywords)
- client_results.append(success)
- if not success:
- print(f"Client log check failed: {log_path}\nLog Content:\n{log_content}")
-
- # Check server log
- server_success, server_log_content = check_keywords_in_log(server_log, server_success_keywords)
- if not server_success:
- print(f"Server log check failed: {server_log}\nLog Content:\n{server_log_content}")
-
- # Determine final success status
- if all(client_results) and server_success:
- result = "Success all"
- else:
- result = "Failure"
-
- # Write the result to the output file
- with open(output_file, "w") as out_file:
- out_file.write(f"Result: {result}\n")
- if server_success:
- out_file.write(f"Server log content:\n{server_log_content}\n")
- else:
- out_file.write("Server log check failed.\n")
- for idx, (success, log_path) in enumerate(zip(client_results, client_logs)):
- if success:
- out_file.write(f"Client {idx + 1} ({log_path}) log check passed.\n")
- else:
- out_file.write(f"Client {idx + 1} ({log_path}) log check failed.\n")
-
- print(f"Check completed. Result: {result}. Details written to {output_file}")
- return result
-
-# Function to run kill.py on failure
-def run_kill_script():
- """
- Run the kill.py script if the experiment fails.
- """
- kill_script_path = os.path.join(current_dir, "kill.py")
- if not os.path.exists(kill_script_path):
- print("kill.py script not found.")
- return
- try:
- print("Executing kill.py due to failure...")
- subprocess.run(["python3", kill_script_path], check=True)
- except subprocess.CalledProcessError as e:
- print(f"Error executing kill.py: {e}")
-
# Run the server and clients
try:
# Start server process and store it
@@ -172,15 +97,7 @@ try:
for client_process in client_processes:
client_process.wait()
- print("========== Checking Logs ==========")
- output_file = os.path.join(log_exp_dir, "flower_log_summary.txt")
- result = check_flower_logs(client_log_paths, server_log_path, output_file)
-
- if result == "Failure":
- run_kill_script()
-
- print("========== Experiment Finished ==========")
-
except Exception as e:
print(f"An error occurred: {e}")
sys.exit(1)
+
--
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