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☆ 问题描述
streamlit之下使用optuna做多进程调参
★ 解决方案
import streamlit as st
import optuna
import multiprocessing
import time# 模拟一个简单的目标函数
def objective(trial):x = trial.suggest_float('x', -10, 10)return (x - 2) ** 2# 优化函数
def optimize(study, n_trials, progress_queue):for i in range(n_trials):study.optimize(objective, n_trials=1)progress_queue.put(study.best_value)def main():st.title("Optuna Optimization Progress Display")chose_n_trials = st.number_input("Choose number of trials", min_value=10, max_value=1000, value=100)# 创建或加载Optuna studystudy_name = 'optuna_study'try:study = optuna.create_study(study_name=study_name, storage='sqlite:///optuna_study.db', direction='minimize')except:study = optuna.load_study(study_name=study_name, storage='sqlite:///optuna_study.db')if st.button("Start Optimization"):progress_queue = multiprocessing.Queue()n_processes = 4 # 设置并行进程数trials_per_process = chose_n_trials // n_processes# 启动多进程优化processes = []for _ in range(n_processes):p = multiprocessing.Process(target=optimize, args=(study, trials_per_process, progress_queue))p.start()processes.append(p)# 初始化进度条progress_bar = st.progress(0)best_score = float('inf')for _ in range(chose_n_trials):best_value = progress_queue.get()if best_value < best_score:best_score = best_valueprogress = (_ + 1) / chose_n_trialsprogress_bar.progress(progress)st.write(f"Best score: {best_score}")# 确保所有进程完成for p in processes:p.join()st.success("Optimization completed!")if __name__ == "__main__":main()✅ 总结
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