Develop Reference

Run python code on specific gpu for lower python versions

I am trying to run a python code on a specific GPU on our server. The server has four GPUs. When I run the code using a virtual environment installed with python 3.8 and tensorflow 2.2, it works correctly on the specific GPU just by adding the below few lines at the first of the script.
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2" # run the code on a specified GPU
Many threads recommend use the above code to run python scripts on a specific GPU such as here and here.
However, When I tried to use the same way to run another python code on another virtual environment (with lower specifications) that was installed with python version 3.6.9 and tensorflow 1.12, it does not run on the GPU but on the CPU.
How can I run python code on a specific GPU in the case of the second virtual environment?

You can use export CUDA_VISIBLE_DEVICES to define which GPUs are visible to the application. For example, if you want GPUs 0 and 2 visible, use export CUDA_VISIBLE_DEVICES=0,2.

How to disable TensorFlow GPU?

I first created my TensorFlow code in python on my GPU using :
import tensorflow-gpu as tf
I used it for training purpose and everything went very well. But now, I want to deploy my python scripts on a device without GPU. So, I uninstalled tensorflow-gpu with pip and import normal TensorFlow :
import tensorflow as tf
But when I run the script, it is still using the gpu :
I tried this code :
try:
# Disable all GPUS
tf.config.set_visible_devices([], 'GPU')
visible_devices = tf.config.get_visible_devices()
print(visible_devices)
for device in visible_devices:
assert device.device_type != 'GPU'
except Exception as e:
# Invalid device or cannot modify virtual devices once initialized.
print(e)
But still not working (even if the gpu seems disable as you can see in white on the screenshot).
I just want to return to the default TensorFlow installation without GPU features.
I tried to uninstall and install tensorflow, remove the virtual environment and create a new one, nothing worked.
Thanks for your help !

Tensorflow > 2.x has default GPU support. To know more please visit Tensorflow site.
As per the above screenshot, it is showing only CPU.
And also observe Adding visible GPU devices: 0
If you still want to use only CPU enable Tensorflow use Tensorflow==1.15

Training Keras models in two seperate juypter notebooks on CPU and GPU

I am training Keras CNN models for two different applications on the Jupyter Notebook. Given that I want to utilize the full resources of my PC, can I use Keras-GPU in one notebook and another notebook using CPU.
I learned that Keras uses GPU by default - if available- and I can force Keras to use CPU as
in Can Keras with Tensorflow backend be forced to use CPU or GPU at will?. My question is that by running this line of code,
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
will the default settings change in all the running notebooks or in that particular notebook only?

by running this line of code,
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
the default settings change in that particular notebook only
You can use
os.environ['CUDA_VISIBLE_DEVICES'] = ''
to train on CPU

Specifying the GPU ID using tensorflow backend

I am trying to train the two different models using GPU ID.
I have tried the command CUDA_VISIBLE_DEVICES=1 python filename.py but it picks up the GPU 0 rather than 1.
I also added the os environment variable in my code, but I got the same behavior.
I am not sure how can I fix this as this is first time to use GPU.

Numba GPU Acceleration on "High Performance GPU" instead of integrated? Python-3.x [duplicate]

I have two GPUs and would like to run two different networks via ipynb simultaneously, however the first notebook always allocates both GPUs.
Using CUDA_VISIBLE_DEVICES, I can hide devices for python files, however I am unsure of how to do so within a notebook.
Is there anyway to hide different GPUs in to notebooks running on the same server?

You can set environment variables in the notebook using os.environ. Do the following before initializing TensorFlow to limit TensorFlow to first GPU.
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"]="0"
You can double check that you have the correct devices visible to TF
from tensorflow.python.client import device_lib
print device_lib.list_local_devices()
I tend to use it from utility module like notebook_util
import notebook_util
notebook_util.pick_gpu_lowest_memory()
import tensorflow as tf

You can do it faster without any imports just by using magics:
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=0
Notice that all env variable are strings, so no need to use ". You can verify that env-variable is set up by running: %env <name_of_var>. Or check all of them with %env.

You can also enable multiple GPU cores, like so:
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0,2,3,4"