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Tensorflow with GPU, how to see tensorflow is using the GPU?

Trying to install tensorflow to work with the GPU. Some documentation I see says tensorflow comes out of box with gpu support when detected. If so, what command can I use to see tensorflow is using my GPU? I have seen other documentation saying you need tensorflow-gpu installed. I have tried both but do not see how my GPU is being used?

Go to command line and run Python
The following example lists the number of visible GPUs on the host.
Docs
import tensorflow as tf
devices = tf.config.list_physical_devices('GPU')
print(len(devices))
For CUDA
Docs
import tensorflow as tf
tf.test.is_built_with_cuda()
Returns whether TensorFlow was built with CUDA (GPU) support.
Docs
You can check with following function too but it's deprecated**
import tensorflow as tf
tf.test.is_gpu_available()
Both returns True if your GPU is available

Tensorflow 2 GPU checking can be found here,
Deprecated, https://www.tensorflow.org/api_docs/python/tf/test/is_gpu_available
https://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices
Check if Tensorflow was built with CUDA (GPU) support,
https://www.tensorflow.org/api_docs/python/tf/test/is_built_with_cuda
Tensorflow GPU guide,
https://www.tensorflow.org/guide/gpu
Code
import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))
# [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
print(tf.test.is_built_with_cuda)
# <function is_built_with_cuda at 0x7f4f5730fbf8>
print(tf.test.gpu_device_name())
# /device:GPU:0
print(tf.config.get_visible_devices())
# [PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Can I get tensorflow-gpu to work with NVIDIA GeForce MX130?

I understand this is not a recommended setup for machine learning in any sense, but I would like to work with what I have.
Not being an expert, I have been told that tf-gpu should work with any device supported by cuda.
When I run:
from numba import cuda
cuda.detect()
I get:
Found 1 CUDA devices
id 0 b'GeForce MX130' [SUPPORTED]
compute capability: 5.0
pci device id: 0
pci bus id: 1
Summary:
1/1 devices are supported
And I can get the GPU to work with some basic 'vectorized' tasks.
Also, running:
import tensorflow as tf
tf.test.is_built_with_cuda()
will return True
However, running
tf.config.experimental.list_physical_devices('gpu')
will return an empty list.
Running:
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
Will return:
Num GPUs Available: 0
Running:
strategy = tf.distribute.MirroredStrategy()
print("Number of devices: {}".format(strategy.num_replicas_in_sync))
will return:
WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
Number of devices: 1
I have trained some basic models with the non-gpu version of tensorflow but I have no clue about how to deal with tf-gpu. I was able to fit a model with CuDNNLSTM layers, but the script didn't use the GPU, according to task manager.
I will appreciate any advice on how to get it to use my 'gpu' or a confirmation that it is not possible. Thanks!
EDITED:
I uninstalled keras and both tensorflow versions and installed only tensorflow-gpu. Nothing changed.

Unfortunately No.
Even though the official specs stated 'Yes', the CUDA GPU list did not mentioned MX130 as part of its list.
(I also running MX130 on my notebook)
reference:
official specs: https://www.nvidia.com/en-us/geforce/gaming-laptops/mx130/specifications/
CUDA enabled GPU list: https://developer.nvidia.com/cuda-gpus

Absolutely YES!
I assume that the compute capability: 5.0 is enough.
I tested my Geforce MX130 with tensorflow-gpu installed by conda (which handles the cuda, versions compatibility, etc.) in Python 3.7
conda install tensorflow-gpu
That's it! no more actions were required.
The following versions were installed:
tensorflow-gpu: 2.1.0
cudatoolkit: 10.1.243
cudnn: 7.6.5
... and it worked!

Install Cuda and CuDNN both. Set the paths for them. For checking if TensorFlow is using GPU, use this:
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
It should show your GPU name in its output.

Yes you can...tensorflow-gpu 2.5.0 + CUDA 11.2 + CUDNN 8.1
Review your enviroment path variable if you are using Windows. In my system it is pointing to...
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\bin;
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\libnvvp;
C:\Apps\CUDNN8.1\bin;

Installing Tensorflow and Keras on Intel Pentium

For a university we are supposed to implement a TensorFlow project using the python libraries for tensorflow and keras. I can install both of them just fine using pip3, but executing any piece of code results in some kind of error.
I've settled on testing the very complicated code:
import keras
Using python 3.6 and the newest tensorflow and keras (pip3 install tensorflow keras) I get the error ModuleNotFoundError: No module named 'tensorflow.python'; 'tensorflow' is not a package. I checked, and import tensorflow finds the package, but returns some error about AVX instructions and dumps the core.
I researched, and my CPU does not support AVX instructions which are part of tensorflow >= 1.6.0. I could not find a precompiled version that runs on my laptop without AVX, and I don't have the time to compile myself.
I tried downgrading to tensorflow == 1.5.0 and keras == 2.1.3 which was the version when tensorflow == 1.5.0 was around, but I still get missing errors, for each version and import statement a different one.
For example when I use the code:
import keras
from keras.datasets import mnist
I instead get the error AttributeError: module 'keras.utils' has no attribute 'Sequence'. I'm on an Intel Pentium, which I assume is the problem. I am fully aware that my setup is in no way suitable for machine learning, and it isn't supposed to be, but nevertheless I'd like to work on that assignment.
Anyone got experience with installing TensorFlow on older machines?
System:
Ubuntu 18.04.2 LTS
Intel(R) Pentium(R) 3556U # 1.70GHz (Dual Core)
4GB RAM

I had the same trouble, but it seems to have solved it. (However, the Python version shall be 3.5. )
For CPUs that do not support AVX, the tensorflow must be version 1.5 or lower.
If you want to install Tensorflow 1.5, the Python version must be 3.5 or lower.
The successful procedure is as follows.
(1) Uninstall your Anaconda.
(2) Download the following version of Anaconda from the following
URL. Version: Anaconda3-4.2.0-Windows-x86_64.exe
URL:https://repo.anaconda.com/archive/ or https://repo.anaconda.com/archive/Anaconda3-4.2.0-Windows-x86_64.exe
(3) Double-click the anaconda icon of “(2)” above, and install the
anaconda according to the GUI instructions.
(4) Start Anaconda Prompt
(5) Enter “pip install tensorflow==1.5” in Anaconda Prompt and press
the return key. Wait for the installation to finish. (See the log)
(6) Enter "pip install keras==2.2.4" in Anaconda Prompt and press the
return key. Wait for the installation to finish.(See the log)
This completes the installation. If you Enter " import tensorflow " on Jupiter notebook, some future error may displayed.(See this log.)
System:
My PC does not support AVX like your PC. My PC's specs are as follows.
PC:Surface Go
CPU:Intel(R) Pentium(R) CPU 4415Y　＠　1.60 GHz
Windows10:64bit
How to test ?
Enter and execute the following command on Jupiter Note. Or use this file.
import tensorflow as tf
print(tf.__version__)
print(tf.keras.__version__)
or
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
If your install is successful, then following message will be displayed on your Jupiter notebook
1.5.0
2.1.2-tf
P.S.
I'm not very good at English, so I'm sorry if I have some impolite or unclear expressions.

Sticking to the Pentium configuration is not recommended for default tensorflow builds because of AVX dependencies. Also many recent advances in this area are not available in earlier builds of TF and you will find it difficult to replicate research works. Options below:
Get a Google Colab (https://colab.research.google.com/) notebook, install Keras and TF and get going with your work
There have been genuine requests for this support, refer to this link [https://github.com/tensorflow/tensorflow/issues/18689] where unofficial builds are provided. See if one of them works
Build Tensorflow from scratch (very hard option), with the right set of flags for Bazel (remove all AVX/threading options)

does tensorflow-gpu library automatically run tensorflow code (non GPU) on GPU?

Do I have to customize code written for the non GPU Tensorflow library to suit tensorflow-gpu library?
I have a gpu and would like to run the python code written for only non GPU tensorflow library. Can I simply install the tensor-flow gpu module and run the code? Or is there any code changes that I will have to make in order to run the code on GPU?

If you have installed tensorflow-gpu and everything runs fine, by default, it should run on your GPU except if:
You specifically state in your code that the graph should run on CPU with something like with tf.device('/cpu:0'),
You hide your GPU from tensorflow with os.environ['CUDA_VISIBLE_DEVICES'] = ''.
You can check that tensorflow finds your GPU using (credit to this answer)
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
Also, if you have an NVIDIA gpu, you can check the usage of your GPU using nvidia-smi and see that tensorflow is indeed using your GPU.
Now, if you'd like to allocate a specific GPU and/or limit the memory that a graph can allocate on the GPU memory, you might want to have a look here for more details.

How do I resolve these tensorflow warnings?

I just installed Tensorflow 1.0.0 using pip. When running, I get warnings like the one shown below.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
I get 5 more similar warning for SSE4.1, SSE4.2, AVX, AVX2, FMA.
Despite these warnings the program seems to run fine.

export TF_CPP_MIN_LOG_LEVEL=2 solved the problem for me on Ubuntu.
https://github.com/tensorflow/tensorflow/issues/7778

My proposed way to solve the problem:
#!/usr/bin/env python3
import os
import tensorflow as tf
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
Should work at least on any Debian or Ubuntu systems.

I don't know much about C, but I found this
bazel build --linkopt='-lrt' -c opt --copt=-mavx --copt=-msse4.2 --copt=-msse4.1 --copt=-msse3-k //tensorflow/tools/pip_package:build_pip_package
How you build you program?

It seems that even if you don't have a compatible (i.e. Nvidia) GPU, you can actually still install the precompiled package for tensorflow-gpu via pip install tensorflow-gpu. It looks like in addition to the GPU support it also supports (or at least doesn't complain about) the CPU instruction set extensions like SSE3, AVX, etc. The only downside I've observed is that the Python wheel is a fair bit larger: 90MB for tensorflow-gpu instead of 42MB for plain tensorflow.
On my machine without an Nvidia GPU I've confirmed that tensorflow-gpu 1.0 runs fine without displaying the cpu_feature_guard warnings.

It would seem that the PIP build for the GPU is bad as well as I get the warnings with the GPU version and the GPU installed...

Those are simply warnings.
They are just informing you if you build TensorFlow from source it can be faster on your machine.
Those instructions are not enabled by default on the builds available I think to be compatible with more CPUs as possible.

As the warnings say you should only compile TF with these flags if you need to make TF faster.
You can use TF environment variable TF_CPP_MIN_LOG_LEVEL and it works as follows:
It defaults to 0, displaying all logs
To filter out INFO logs set it to 1
WARNINGS additionally, 2
and to additionally filter out ERROR logs set it to 3
So you can do the following to silence the warnings:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf