# Why the my Tensorflow code just use one GPU when I assign more than one

I am trying to run my code on a supercomputer with 8 gpus. Though, I assign 8 gpus but one of them is just occupied. I read some notes in websites and it seems that Tensorflow automatically use gpu if it is applicable but still I don't know how I can use all the gpus. The code is just a deep network to be trained using model.fit() and then predict the test data using model.predict()

TensorFlow 2.0 now has the tf.distribute module to distribute training across multiple GPUs, multiple machines or TPUs. It builds on the concept of "distribution strategies". You can use tf.distribute.MirroredStrategy() as a scope, like
strategy = tf.distribute.MirroredStrategy()