How can I solve 'ran out of gpu memory' in TensorFlow

Question

I ran the MNIST demo in TensorFlow with 2 conv layers and a full-conect layer, I got an message that \'ran out of memeory trying to allocate 2.59GiB\' , but it shows that to

Answer 1

For Tensorflow 2:

config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)

Answer 2

By default, TensorFlow maps nearly all of the GPU memory of all GPUs (subject to CUDA_VISIBLE_DEVICES) visible to the process. This is done to more efficiently use the relatively precious GPU memory resources on the devices by reducing memory fragmentation.

TensorFlow provides two Config options on the Session to control this.

The first is the allow_growth option, which attempts to allocate only as much GPU memory based on runtime allocations:

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)

The second method is the per_process_gpu_memory_fraction option, which determines the fraction of the overall amount of memory that each visible GPU should be allocated. For example, you can tell TensorFlow to only allocate 40% of the total memory of each GPU by:

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.4
session = tf.Session(config=config)