tf_alloc

Simpliying GPU allocation for Tensorflow

⭐️ Why tf_alloc? Problems?

• Compare to pytorch, tensorflow allocate all GPU memory to single training.
• However, it is too much waste because, some training does not use whole GPU memory.
• To solve this problem, TF engineers use two methods.

1. Limit to use only single GPU
2. Limit the use of only a certain percentage of GPUs.

• However, these methods require complex code and memory management.

⭐️ Why tf_alloc? How to solve?

tf_alloc simplfy and automate GPU allocation using two methods.

⭐️ How to allocate?

• Before using tf_alloc, you have to install tensorflow fits for your environment.
• This library does not install specific tensorflow version.

# On the top of the code
from tf_alloc import allocate as talloc
talloc(gpu=1, percentage=0.5)

import tensorflow as tf
""" your code"""

It is only code for allocating GPU in certain percentage.

Parameters:

• gpu = which gpu you want to use (if you have two gpu than [0, 1] is possible)
• percentage = the percentage of memory usage on single gpu. 1.0 for maximum use.

⭐️ Additional Function.

GET GPU Objects

gpu_objs = get_gpu_objects()

• To use this code, you can get gpu objects that contains gpu information.
• You can set GPU backend by using this function.

GET CURRENT STATE

Defualt

current(
    gpu_id = False, 
    total_memory=False, 
    used = False, 
    free = False, 
    percentage_of_use = False,
    percentage_of_free = False,
)

• You can use this functions to see current GPU state and possible maximum allocation percentage.
• Without any parameters, than it only visualize possible maximum allocation percentage.
• It is cmd line visualizer. It doesn't return values.

Parameters

• gpu_id = visualize the gpu id number
• total_memory = visualize the total memory of GPU
• used = visualize the used memory of GPU
• free = visualize the free memory of GPU
• percentage_of_used = visualize the percentage of used memory of GPU
• percentage_of_free = visualize the percentage of free memory of GPU