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# # %%writefile app.py
# import streamlit as st
# import matplotlib.pyplot as plt
# import torch
# from transformers import AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification, AdamW
# from datasets import load_dataset, Dataset
# from evaluate import load as load_metric
# from torch.utils.data import DataLoader
# import pandas as pd
# import random
# from collections import OrderedDict
# import flwr as fl
# DEVICE = torch.device("cpu")
# def load_data(dataset_name, train_size=20, test_size=20, num_clients=2):
# raw_datasets = load_dataset(dataset_name)
# raw_datasets = raw_datasets.shuffle(seed=42)
# del raw_datasets["unsupervised"]
# tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
# def tokenize_function(examples):
# return tokenizer(examples["text"], truncation=True)
# tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
# tokenized_datasets = tokenized_datasets.remove_columns("text")
# tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
# train_datasets = []
# test_datasets = []
# for _ in range(num_clients):
# train_dataset = tokenized_datasets["train"].select(random.sample(range(len(tokenized_datasets["train"])), train_size))
# test_dataset = tokenized_datasets["test"].select(random.sample(range(len(tokenized_datasets["test"])), test_size))
# train_datasets.append(train_dataset)
# test_datasets.append(test_dataset)
# data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# return train_datasets, test_datasets, data_collator
# def read_log_file():
# with open("./log.txt", "r") as file:
# return file.read()
# def train(net, trainloader, epochs):
# optimizer = AdamW(net.parameters(), lr=5e-5)
# net.train()
# for _ in range(epochs):
# for batch in trainloader:
# batch = {k: v.to(DEVICE) for k, v in batch.items()}
# outputs = net(**batch)
# loss = outputs.loss
# loss.backward()
# optimizer.step()
# optimizer.zero_grad()
# def test(net, testloader):
# metric = load_metric("accuracy")
# net.eval()
# loss = 0
# for batch in testloader:
# batch = {k: v.to(DEVICE) for k, v in batch.items()}
# with torch.no_grad():
# outputs = net(**batch)
# logits = outputs.logits
# loss += outputs.loss.item()
# predictions = torch.argmax(logits, dim=-1)
# metric.add_batch(predictions=predictions, references=batch["labels"])
# loss /= len(testloader)
# accuracy = metric.compute()["accuracy"]
# return loss, accuracy
# class CustomClient(fl.client.NumPyClient):
# def __init__(self, net, trainloader, testloader, client_id):
# self.net = net
# self.trainloader = trainloader
# self.testloader = testloader
# self.client_id = client_id
# self.losses = []
# self.accuracies = []
# def get_parameters(self, config):
# return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
# def set_parameters(self, parameters):
# params_dict = zip(self.net.state_dict().keys(), parameters)
# state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
# self.net.load_state_dict(state_dict, strict=True)
# def fit(self, parameters, config):
# self.set_parameters(parameters)
# train(self.net, self.trainloader, epochs=1)
# loss, accuracy = test(self.net, self.testloader)
# self.losses.append(loss)
# self.accuracies.append(accuracy)
# return self.get_parameters(config={}), len(self.trainloader.dataset), {}
# def evaluate(self, parameters, config):
# self.set_parameters(parameters)
# loss, accuracy = test(self.net, self.testloader)
# return float(loss), len(self.testloader.dataset), {"accuracy": float(accuracy)}
# def plot_metrics(self, round_num, plot_placeholder):
# if self.losses and self.accuracies:
# plot_placeholder.write(f"#### Client {self.client_id} Metrics for Round {round_num}")
# plot_placeholder.write(f"Loss: {self.losses[-1]:.4f}")
# plot_placeholder.write(f"Accuracy: {self.accuracies[-1]:.4f}")
# fig, ax1 = plt.subplots()
# color = 'tab:red'
# ax1.set_xlabel('Round')
# ax1.set_ylabel('Loss', color=color)
# ax1.plot(range(1, len(self.losses) + 1), self.losses, color=color)
# ax1.tick_params(axis='y', labelcolor=color)
# ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis
# color = 'tab:blue'
# ax2.set_ylabel('Accuracy', color=color)
# ax2.plot(range(1, len(self.accuracies) + 1), self.accuracies, color=color)
# ax2.tick_params(axis='y', labelcolor=color)
# fig.tight_layout()
# plot_placeholder.pyplot(fig)
# def main():
# st.write("## Federated Learning with Dynamic Models and Datasets for Mobile Devices")
# dataset_name = st.selectbox("Dataset", ["imdb", "amazon_polarity", "ag_news"])
# model_name = st.selectbox("Model", ["bert-base-uncased","facebook/hubert-base-ls960", "distilbert-base-uncased"])
# NUM_CLIENTS = st.slider("Number of Clients", min_value=1, max_value=10, value=2)
# NUM_ROUNDS = st.slider("Number of Rounds", min_value=1, max_value=10, value=3)
# train_datasets, test_datasets, data_collator = load_data(dataset_name, num_clients=NUM_CLIENTS)
# trainloaders = []
# testloaders = []
# clients = []
# for i in range(NUM_CLIENTS):
# st.write(f"### Client {i+1} Datasets")
# train_df = pd.DataFrame(train_datasets[i])
# test_df = pd.DataFrame(test_datasets[i])
# st.write("#### Train Dataset")
# edited_train_df = st.data_editor(train_df, key=f"train_{i}")
# st.write("#### Test Dataset")
# edited_test_df = st.data_editor(test_df, key=f"test_{i}")
# edited_train_dataset = Dataset.from_pandas(edited_train_df)
# edited_test_dataset = Dataset.from_pandas(edited_test_df)
# trainloader = DataLoader(edited_train_dataset, shuffle=True, batch_size=32, collate_fn=data_collator)
# testloader = DataLoader(edited_test_dataset, batch_size=32, collate_fn=data_collator)
# trainloaders.append(trainloader)
# testloaders.append(testloader)
# net = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2).to(DEVICE)
# client = CustomClient(net, trainloader, testloader, client_id=i+1)
# clients.append(client)
# if st.button("Start Training"):
# def client_fn(cid):
# return clients[int(cid)]
# def weighted_average(metrics):
# accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
# losses = [num_examples * m["loss"] for num_examples, m in metrics]
# examples = [num_examples for num_examples, _ in metrics]
# return {"accuracy": sum(accuracies) / sum(examples), "loss": sum(losses) / sum(examples)}
# strategy = fl.server.strategy.FedAvg(
# fraction_fit=1.0,
# fraction_evaluate=1.0,
# evaluate_metrics_aggregation_fn=weighted_average,
# )
# for round_num in range(NUM_ROUNDS):
# st.write(f"### Round {round_num + 1}")
# plot_placeholders = [st.empty() for _ in range(NUM_CLIENTS)]
# fl.common.logger.configure(identifier="myFlowerExperiment", filename="./log.txt")
# fl.simulation.start_simulation(
# client_fn=client_fn,
# num_clients=NUM_CLIENTS,
# config=fl.server.ServerConfig(num_rounds=1),
# strategy=strategy,
# client_resources={"num_cpus": 1, "num_gpus": 0},
# ray_init_args={"log_to_driver": False, "num_cpus": 1, "num_gpus": 0}
# )
# for i, client in enumerate(clients):
# st.markdown("LOGS : "+ read_log_file())
# client.plot_metrics(round_num + 1, plot_placeholders[i])
# st.write(" ")
# st.success("Training completed successfully!")
# # Display final metrics
# st.write("## Final Client Metrics")
# for client in clients:
# st.write(f"### Client {client.client_id}")
# st.write(f"Final Loss: {client.losses[-1]:.4f}")
# st.write(f"Final Accuracy: {client.accuracies[-1]:.4f}")
# client.plot_metrics(NUM_ROUNDS, st.empty())
# st.write(" ")
# else:
# st.write("Click the 'Start Training' button to start the training process.")
# if __name__ == "__main__":
# main()
# %%writefile app.py
import streamlit as st
import matplotlib.pyplot as plt
import torch
from transformers import AutoTokenizer, DataCollatorWithPadding, AutoModelForSequenceClassification, AdamW
from datasets import load_dataset, Dataset
from evaluate import load as load_metric
from torch.utils.data import DataLoader
import pandas as pd
import random
from collections import OrderedDict
import flwr as fl
from logging import INFO, DEBUG
from flwr.common.logger import log
DEVICE = torch.device("cpu")
def load_data(dataset_name, train_size=20, test_size=20, num_clients=2):
raw_datasets = load_dataset(dataset_name)
raw_datasets = raw_datasets.shuffle(seed=42)
del raw_datasets["unsupervised"]
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True)
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
tokenized_datasets = tokenized_datasets.remove_columns("text")
tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
train_datasets = []
test_datasets = []
for _ in range(num_clients):
train_dataset = tokenized_datasets["train"].select(random.sample(range(len(tokenized_datasets["train"])), train_size))
test_dataset = tokenized_datasets["test"].select(random.sample(range(len(tokenized_datasets["test"])), test_size))
train_datasets.append(train_dataset)
test_datasets.append(test_dataset)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
return train_datasets, test_datasets, data_collator, raw_datasets
def train(net, trainloader, epochs):
optimizer = AdamW(net.parameters(), lr=5e-5)
net.train()
for _ in range(epochs):
for batch in trainloader:
batch = {k: v.to(DEVICE) for k, v in batch.items()}
outputs = net(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
def test(net, testloader
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