如何在具有tensorflow2和keras的多GPU上训练模型？

我有一个要在多个GPU上训练的LSTM模型。我将代码转换为执行此操作，然后nvidia-smi我可以看到它正在使用所有GPU的所有内存，并且每个GPU都使用了大约40％的BUT，估计每批的训练时间几乎与1 gpu相同。

有人可以引导我并告诉我如何在多个GPU上正确训练吗？

我的代码：

import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dropout

import os
from tensorflow.keras.callbacks import ModelCheckpoint



checkpoint_path = "./model/"
checkpoint_dir = os.path.dirname(checkpoint_path)
cp_callback = ModelCheckpoint(filepath=checkpoint_path, save_freq= 'epoch', verbose=1 )

# NNET - LSTM
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
    regressor = Sequential()

    regressor.add(LSTM(units = 180, return_sequences = True, input_shape = (X_train.shape[1], 3)))
    regressor.add(Dropout(0.2))

    regressor.add(LSTM(units = 180, return_sequences = True))
    regressor.add(Dropout(0.2))

    regressor.add(LSTM(units = 180))
    regressor.add(Dropout(0.2))

    regressor.add(Dense(units = 4))

    regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')

regressor.fit(X_train, y_train, epochs = 10, batch_size = 32, callbacks=[cp_callback])