在Keras中，可以使用深度学习分发库（distribution strategy）来将大型张量（large tensor）分配到多个GPU上。下面是使用Horovod库和TensorFlow分布式策略的代码示例，以将大型张量分配到两个GPU上进行训练。

import tensorflow as tf
import horovod.tensorflow.keras as hvd

# Initialize Horovod
hvd.init()

# Define distribution strategy
strategy = tf.distribute.HorovodStrategy()

# Define number of GPUs
num_gpus = strategy.num_replicas_in_sync

# Load data
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Normalize data
x_train, x_test = x_train / 255.0, x_test / 255.0

# Define model
def create_model():
    model = tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(1024, activation='relu'),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10)
    ])

    # Compile model with distributed strategy
    optimizer = tf.keras.optimizers.Adam(0.001 * num_gpus * hvd.size())
    model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  optimizer=optimizer,
                  metrics=['accuracy'])

    return model

# Create model with distribution strategy
with strategy.scope():
    model = create_model()

# Train model with distribution strategy
model.fit(x_train, y_train,
          epochs=3,
          batch_size=64 * num_gpus,
          validation_data=(x_test, y_test))

这里引入了Horovod库和TensorFlow的分布式策略。首先，我们初始化Horovod。然后，我们定义了分发策略，Horovod策略基于MPI。接下来，我们确定使用的GPU数量，并加载数据集。我们对数据进行归一化处理