i am using Rtx 3090 24 GB RAM the DeepFace utilize all the memmory of gpu how can i control its utilization

[serengil]

Especially analyze method requires many complex models. unfortunately, nothing to do. I recommend you to use cpu.

[Uzair1947]

ok thanks how can I shift the load to the CPU

[serengil]

import os os.environ["CUDA_VISIBLE_DEVICES"]=""

or

os.environ["CUDA_VISIBLE_DEVICES"]="-1"

[12bitmisfit]

I could be wrong but I think tensorflow by default allocates all available GPU memory.

This worked for me:

import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

[sharkykittens]

I could be wrong but I think tensorflow by default allocates all available GPU memory.

This worked for me:

import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

Thanks for this, I've seen other projects where tensorflow allocates all available memory as well, however when running your code deepface ended up not using gpu at all. I made just a small tweak to your code to get deepface to use gpu but not allocate all available memory:

import tensorflow as tf

gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.set_visible_devices(gpus, 'GPU')
for gpu in gpus:
    print(gpu)
    tf.config.experimental.set_memory_growth(gpu, True)