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语言：Python
框架：Flask、Tensorflow
功能描述：存在两个接口，一个接口实现开始训练模型的功能，一个接口实现停止训练的功能。
实现：用一个全局变量存储在训练中的模型。

# 存储所有训练任务
training_tasks = {}

# 训练模型的接口
@train_model.route("/train", methods=["POST"])
def train():try:data = request.get_data()data = json.loads(data)print(data)modelId = data["modelId"]if modelId in training_tasks:return {"success": False, "message": f"{modelId} 已经在训练中"}stop_event = threading.Event()# 在新线程中启动训练train_thread = threading.Thread(target=start_train,args=(data, stop_event))training_tasks[modelId] = {'thread': train_thread,'stop_event': stop_event}train_thread.start()return {"success": "success", "message": "开始训练"}except Exception as e:return  {"success": False, "message": str(e)}

def start_train(data, stop_event):try:# 获取任务参数modelId = data["modelId"]except Exception as e:response_data = {"success": False, "message": str(e)}return response_data

class StopTrainingCallback(keras.callbacks.Callback):def __init__(self, model, modelId, stop_event):super().__init__()self.model = modelself.modelId = modelIdself.stop_event = stop_eventdef on_train_begin(self, logs=None):if self.stop_event.is_set():self.model.stop_training = True # 设置此标志会使model.fit提前终止print(f"训练在开始前被停止")def on_batch_begin(self, batch, logs=None):if self.stop_event.is_set():self.model.stop_training = True # 设置此标志会使model.fit提前终止print(f"训练在批次被停止")# 强制抛出一个异常以确保立即停止raise KeyboardInterrupt("训练被用户停止")

# 模型真正训练的函数
def start_train(data, stop_event):# 定义模型及训练数据model = "xxx"modelId = "xxx"train_dataset = "xxx"test_dataset = "xxx"train_steps = len(list(train_dataset))test_steps = len(list(test_dataset))epochs = "xxx"stoptrainingcallback = StopTrainingCallback(model, modelId, stop_event)try:# 在开始训练前立即检查停止事件if stop_event.is_set():log.info(f"训练 {modelId} 在开始前被停止")callback_log.info("模型训练在开始前被停止")raise KeyboardInterrupt("Training stopped before start")model.fit(train_dataset,steps_per_epoch=train_steps,epochs=epochs,verbose=2,shuffle=True,validation_data=test_dataset,validation_steps=test_steps,callbacks=[stoptrainingcallback])response_data = {"success": True, "message": "Success"}except KeyboardInterrupt:response_data = {"success": False, "message": "模型训练被用户停止."}except tf.errors.ResourceExhaustedError as e:# 显存不足错误response_data = {"success": False, "message": "GPU内存不足，请调整训练参数."}except Exception as e:print("模型训练失败")response_data = {"success": False, "message": str(e)}finally:if data["modelId"] in training_tasks:del training_tasks[data["modelId"]]return response_data

# 停止训练的接口
@stop_train.route('/stop', methods=['POST'])
def stop():data = request.get_data()try:data = json.loads(data)modelId = data.get("modelId",'') # 每个模型有一个唯一的UUIDif modelId == '':return jsonify({"success": False, "message": "modelId为空，无法停止训练.", "data": ''})except Exception as e:print("停止模型训练接口请求数据出错：", str(e))return jsonify({"success": False, "message": "参数错误.", "data": ''})# 调用服务层停止训练result = stop_train_service(modelId)print(result["message"])# 返回响应return jsonify(result)

# 调用服务层停止训练
def stop_train_service(modelId):# 检查模型是否存在if modelId not in training_tasks:return {"success": "error", "message": f"没有找到模型 {modelId} 的训练任务"}# 获取停止事件并设置stop_event = training_tasks[modelId].get('stop_event')if stop_event:stop_event.set()# 清理任务记录del training_tasks[modelId]return {"success": "success", "message": f"停止 {modelId} 模型训练的请求已发送"}else:return {"success": "error", "message": f"模型 {modelId} 的停止训练事件不存在"}