Tensorflow not running on GPU

user_5127598 0
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I have aldready spent a considerable of time digging around on stack overflow and else looking for the answer, but couldn't find anything

Hi all,

I am running Tensorflow with Keras on top. I am 90% sure I installed Tensorflow GPU, is there any way to check which install I did?

I was trying to do run some CNN models from Jupyter notebook and I noticed that Keras was running the model on the CPU (checked task manager, CPU was at 100%).

I tried running this code from the tensorflow website:

# Creates a graph.
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))

And this is what I got:

MatMul: (MatMul): /job:localhost/replica:0/task:0/cpu:0
2017-06-29 17:09:38.783183: I c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] MatMul: (MatMul)/job:localhost/replica:0/task:0/cpu:0
b: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-06-29 17:09:38.784779: I c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] b: (Const)/job:localhost/replica:0/task:0/cpu:0
a: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-06-29 17:09:38.786128: I c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] a: (Const)/job:localhost/replica:0/task:0/cpu:0
[[ 22.  28.]
 [ 49.  64.]]

Which to me shows I am running on my CPU, for some reason.

I have a GTX1050 (driver version 382.53), I installed CUDA, and Cudnn, and tensorflow installed without any problems. I installed Visual Studio 2015 as well since it was listed as a compatible version.

I remember CUDA mentioning something about an incompatible driver being installed, but if I recall correctly CUDA should have installed its own driver.

Edit: I ran theses commands to list the available devices

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

and this is what I get

[name: "/cpu:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 14922788031522107450
]

and a whole lot of warnings like this

2017-06-29 17:32:45.401429: W c:\tf_jenkins\home\workspace\release-win\m\windows\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.

Edit 2

Tried running

pip3 install --upgrade tensorflow-gpu

and I get

Requirement already up-to-date: tensorflow-gpu in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages
Requirement already up-to-date: markdown==2.2.0 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: html5lib==0.9999999 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: werkzeug>=0.11.10 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: wheel>=0.26 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: bleach==1.5.0 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: six>=1.10.0 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: protobuf>=3.2.0 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: backports.weakref==1.0rc1 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: numpy>=1.11.0 in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from tensorflow-gpu)
Requirement already up-to-date: setuptools in c:\users\xxx\appdata\local\programs\python\python35\lib\site-packages (from protobuf>=3.2.0->tensorflow-gpu)

Solved: Check comments for solution. Thanks to all who helped!

I am new to this, so any help is greatly appreciated! Thank you.

cudnn keras tensorflow nvidia
7 Answers

I was still having trouble getting GPU support even after correctly installing tensorflow-gpu via pip. My problem was that I had installed tensorflow 1.5, and CUDA 9.1 (the default version Nvidia directs you to), whereas the precompiled tensorflow 1.5 works with CUDA versions <= 9.0. Here is download page on nvidia's site to get the correct CUDA 9.0:

https://developer.nvidia.com/cuda-90-download-archive

Also make sure to update your cuDNN to a version compatible with CUDA 9.0 https://developer.nvidia.com/cudnn https://developer.nvidia.com/rdp/cudnn-download

user_58476620

If you happen to using Anaconda to manage your environments => uninstall all existing versions of tensorflow

pip uninstall tensorflow
pip3 uninstall tensorflow

Install tensorflow-gpu using conda

conda install tensorflow-gpu

If you don't mind starting from a new environment tho the easiest way to do so without

conda create --name tf_gpu tensorflow-gpu

creates a new conda environment with the name tf_gpu with tensorflow gpu installed

user_130117990

For me the following worked.

I used conda environment, as python environment meant setting LD_LIBRARY_PATH and installing Cuda manually which is an another mess.

In the mentioned blog, he have installed cudatoolkit and cudann inside conda and then installed tensorflow-gpu later which fixed the problem.

P.S, as far as I read, cudatoolkit and cudann plays huge role in getting your code running on tensorflow-gpu.

user_90639710

I ran into a similar problem I had the follwing versions of tensor flow libraries.

tensorboard               2.4.1              pyhd8ed1ab_1    conda-forge
tensorboard-plugin-wit    1.8.0              pyh44b312d_0    conda-forge
tensorflow                2.4.1            py39hf3d152e_0    conda-forge
tensorflow-base           2.4.1            py39h23a8cbf_0    conda-forge
tensorflow-estimator      2.4.0              pyh9656e83_0    conda-forge
tensorflow-gpu            2.4.1                h30adc30_0

The same version of libraries were installed in another machine where it was able to utilise the GPU. The Cuda toolkit version and driver versions were the same in both machines( the machine where it was working and the one where it wasnt).

Turns out the reason was that tensorflow-gpu=2.4.1 is compatible with python version 3.8.10. Changing my python version to 3.8.10 and keeping all other things unchanged worked for me !

user_105742610

If you have problems with running tensorflow on gpu you should check if you have good versions of cuda and cuDNN installed. The versions should be exactly the same as here. For example for tensorflow v2.8.0 you should have cuda v11.2 (not newer) and cuDNN v8.1.

Also, you should add cuda /bin folder and /libnvvp to path (for windows).

This answer is based on this tutorial Tensorflow 2021 install tutorial. If you still can not make it runing check for some missing steps.

user_106017080
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