Simplication of GPU allocation for Tensorflow2
Project description
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.
- Limit to use only single GPU
- 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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
tf_alloc-0.0.3.tar.gz
(5.4 kB
view details)
Built Distribution
File details
Details for the file tf_alloc-0.0.3.tar.gz
.
File metadata
- Download URL: tf_alloc-0.0.3.tar.gz
- Upload date:
- Size: 5.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.1 importlib_metadata/4.8.1 pkginfo/1.8.2 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 23de50f38934bcfc77cc809f1e6fd3abc48ca49c2e37d88755b0c8487f8bf097 |
|
MD5 | 2e395262a68477d67f0c50fb5888c28d |
|
BLAKE2b-256 | 7f3dcef94db03038214598eb10b687f9ba4e5f17207d4d6709135d3c8515ead9 |
File details
Details for the file tf_alloc-0.0.3-py3-none-any.whl
.
File metadata
- Download URL: tf_alloc-0.0.3-py3-none-any.whl
- Upload date:
- Size: 6.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.7.1 importlib_metadata/4.8.1 pkginfo/1.8.2 requests/2.22.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 30d121647ea9d66f946d4447d5d4ff26b6d6867941ce50f48216163e6853d809 |
|
MD5 | bdc95b496e16666432c76aba44afb237 |
|
BLAKE2b-256 | 511ee3fafbbb7ab35d331c1f724cc6c2dbc4d842d712da8b35310aff863c5dbf |