Ultralytics
YOLO v8 n + Flower Framework #9440
Replies: 1 comment 3 replies
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@seifHesham23 certainly! Integrating YOLOv8 with the Flower framework for federated learning is an exciting endeavor. Here's a high-level overview of how you can approach it:
from flwr.server.strategy import FedAvg
from flwr.server import start_server
from flwr.client import NumPyClient, start_client
import yolov8
class YOLOClient(NumPyClient):
def __init__(self):
self.model = yolov8.load_model("path_to_your_model/model_name.pt")
def get_parameters(self):
# Return model parameters as a list of NumPy ndarrays
return yolov8.get_model_parameters(self.model)
def set_parameters(self, parameters):
# Update model parameters from the server
yolov8.set_model_parameters(self.model, parameters)
def fit(self, parameters, config):
# Define your training here
# Remember to set the parameters first
self.set_parameters(parameters)
yolov8.train(self.model)
return self.get_parameters(), len(dataset), {}
def evaluate(self, parameters, config):
# Define your evaluation here
# Remember to set the parameters first
self.set_parameters(parameters)
loss, accuracy = yolov8.evaluate(self.model)
return loss, len(dataset), {"accuracy": accuracy}
# Example of starting Flower server
if __name__ == "__main__":
start_server(server_address="0.0.0.0:8080", config={"num_rounds": 3}, strategy=FedAvg())
# Example of starting Flower client
if __name__ == "__main__":
start_client(server_address="0.0.0.0:8080", client=YOLOClient())Remember, this example is quite simplified. The actual implementation would depend on how you load, train, and evaluate your YOLOv8 model, as well as how your federated learning setup is configured within the Flower framework.
I hope this helps you get started! If you encounter specific issues along the way, feel free to raise them. Happy coding! 😊 |
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I really appreciate the help. I'm trying to run a simulation of federated learning on Flower, I tried the code you provided but it didn't work for me. I'm working on Google Colab , I don't know why it didn't work but I will share my notebook. If it's possible please check if there's something wrong with the setup. Thank you https://colab.research.google.com/drive/1p9BXnl0J7Nkza_3JZRMzIpfZ3QYGlV9w?usp=sharing |
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here's the error I get when I run the notebook: INFO flwr 2024-04-02 04:14:04,114 | app.py:178 | Starting Flower simulation, config: ServerConfig(num_rounds=5, round_timeout=None) The above exception was the direct cause of the following exception: ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>)
ERROR:flwr:Traceback (most recent call last): The above exception was the direct cause of the following exception: ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>)
ERROR flwr 2024-04-02 04:14:19,182 | ray_client_proxy.py:162 | ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>) The above exception was the direct cause of the following exception: ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>)
The above exception was the direct cause of the following exception: ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>)
The above exception was the direct cause of the following exception: ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>)
The above exception was the direct cause of the following exception: ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>)
The above exception was the direct cause of the following exception: ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>)
ERROR:flwr:Traceback (most recent call last): The above exception was the direct cause of the following exception: ray::DefaultActor.run() (pid=6039, ip=172.28.0.12, actor_id=a1f7c8b5c19d72eaeeb73c7b01000000, repr=<flwr.simulation.ray_transport.ray_actor.DefaultActor object at 0x78df314f5c60>)
ERROR flwr 2024-04-02 04:14:19,188 | app.py:315 | Your simulation crashed :(. This could be because of several reasons. The most common are:
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@seifHesham23 hey there! 👋 It looks like the main issue is with the It seems like you're trying to call Here's a quick example of what loading a YOLO model often looks like (this will differ based on how you've set up YOLOv8): from ultralytics import YOLO # Make sure this import matches your YOLOv8 setup
# Then, in your client code
model = YOLO('path/to/your/model.pt')Ensure the YOLOv8 library is properly installed in your Colab environment too, and validate that It's also important that the rest of your simulation config/settings in Flower are correctly set up to handle the resources needed for running YOLOv8 inference. Based on the errors, it might be worth checking your resource allocation ( Hope this helps to get you on the right track! If the problem persists, it might be useful to share more specific details about your |
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Hey, did you manage to do it? |
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@kvnptl to integrate YOLOv8 with the Flower framework for federated learning, you can start by checking the Flower documentation for guidance on model integration. If you encounter specific issues, feel free to ask! |
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Can anyone provide help on how to use YOLO v8 with Flower framework. I'm trying to make Federated learning for People detection using Yolo
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