algorithm_platform/models_processing/model_tf/async_query_task.py

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# @FileName  :sync_query.py
# @Time      :2025/3/5 12:55
# @Author    :David
# @Company: shenyang JY


from flask import jsonify
import threading
import uuid
import time
import traceback
from collections import defaultdict

# 全局存储训练进度（生产环境建议使用Redis）
training_progress = defaultdict(dict)
progress_lock = threading.Lock()


@app.route('/nn_bp_training', methods=['POST'])
def start_training():
    """启动训练任务接口"""
    task_id = str(uuid.uuid4())

    # 初始化任务进度
    with progress_lock:
        training_progress[task_id] = {
            'status': 'pending',
            'progress': 0,
            'message': '任务已创建',
            'result': None,
            'start_time': time.time(),
            'end_time': None
        }

    # 启动异步训练线程
    thread = threading.Thread(
        target=async_training_task,
        args=(task_id,),
        daemon=True
    )
    thread.start()

    return jsonify({
        'success': 1,
        'task_id': task_id,
        'message': '训练任务已启动'
    })


@app.route('/training_progress/')
def get_progress(task_id):
    """查询训练进度接口"""
    with progress_lock:
        progress = training_progress.get(task_id, {
            'status': 'not_found',
            'progress': 0,
            'message': '任务不存在'
        })

    return jsonify(progress)


@app.route('/training_progress/')
def get_progress(task_id):
    """查询训练进度接口"""
    with progress_lock:
        progress = training_progress.get(task_id, {
            'status': 'not_found',
            'progress': 0,
            'message': '任务不存在'
        })

    return jsonify(progress)

def async_training_task(task_id):
    """异步训练任务"""
    args = {}  # 根据实际情况获取参数
    result = {}
    start_time = time.time()

    try:
        # 更新任务状态
        update_progress(task_id, 10, '数据准备中...')

        # ------------ 数据准备 ------------
        train_data = get_data_from_mongo(args)
        train_x, train_y, valid_x, valid_y, scaled_train_bytes, scaled_target_bytes = dh.train_data_handler(
            train_data, bp_data=True)

        # ------------ 模型训练 ------------
        update_progress(task_id, 30, '模型训练中...')
        bp.opt.Model['input_size'] = train_x.shape[1]

        # 包装训练函数以跟踪进度
        def training_callback(epoch, total_epoch):
            progress = 30 + 60 * (epoch / total_epoch)
            update_progress(task_id, progress, f'训练第 {epoch}/{total_epoch} 轮')

        bp_model = bp.training([train_x, train_y, valid_x, valid_y],
                               callback=training_callback)

        # ------------ 保存结果 ------------
        update_progress(task_id, 95, '保存模型中...')
        args['params'] = json.dumps(args)
        args['descr'] = '测试'
        args['gen_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())
        insert_trained_model_into_mongo(bp_model, args)
        insert_scaler_model_into_mongo(scaled_train_bytes, scaled_target_bytes, args)

        # 最终结果
        result.update({
            'success': 1,
            'args': args,
            'start_time': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time)),
            'end_time': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()),
        })

        update_progress(task_id, 100, '训练完成', result=result)

    except Exception as e:
        error_msg = traceback.format_exc().replace("\n", "\t")
        result = {
            'success': 0,
            'msg': error_msg,
            'start_time': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time)),
            'end_time': time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()),
        }
        update_progress(task_id, -1, '训练失败', result=result)


def update_progress(task_id, progress, message, result=None):
    """更新进度工具函数"""
    with progress_lock:
        training_progress[task_id]['progress'] = progress
        training_progress[task_id]['message'] = message
        training_progress[task_id]['status'] = 'running'

        if progress >= 100:
            training_progress[task_id]['status'] = 'completed'
            training_progress[task_id]['end_time'] = time.time()
        elif progress < 0:
            training_progress[task_id]['status'] = 'failed'
            training_progress[task_id]['end_time'] = time.time()

        if result:
            training_progress[task_id]['result'] = result