I wanted to use tensorflow for GPU for my current project so I followed a tutorial from youtube to install it without using conda (https://www.youtube.com/watch?v=-Q6SM_usn84)
I followed the tutorial and turns out there is no CUDA 11.2 for windows 11. Hence I installed the latest version i.e. CUDA 12 and followed the rest of the tutorial.
I then proceeded to create a virtualenv using python 3.10.10 and installed tensorflow using:
pip3 install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.11.0-cp310-cp310-win_amd64.whl
It did install successfully and imports but does not list my GPU as a physical device available.
After a bit of research I came across this "https://forums.developer.nvidia.com/t/how-do-i-install-cuda-11-0-on-windows-11-not-wsl2-windows-itself/192251" and reinstalled CUDA 11.2 for windows 10 in my windows 11 machine. Everything is still the same.
It imports but does not list my GPU as a physical device available.
I have installed both tensorflow 2.2.0 and tensorflow 1.15.0(by pip install tensorflow-gpu==1.15.0). The tensorflow 2 is installed in the base environment of Anaconda 3, while the tensorflow 1 is installed in a separate environment.
The tensorflow 2.2.0 can recognize gpu based on a simple test:
if tf.test.gpu_device_name():
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
//output: Default GPU Device: /device:GPU:0
But the tensorflow 1.15.0 can not detect gpu.
For your information, my system environment is python + cuda 10.1 + vs 2015.
The tensosflow versions 1.15.0 to 1.15.3 (the latest version) are all compiled against Cuda 10.0. Downgrading the cuda 10.1 to cuda 10.0 solved the problem.
Also be aware of the python version. It is recommended to install the tensorflow .whl file (as listed at https://nero-mirror.stanford.edu/pypi/simple/tensorflow-gpu/) for the specific python version. As for installation, see How do I install a Python package with a .whl file?
Tensorflow 1.15 expects cuda 10.0 , but I managed to make it work with cuda 10.1 by installing the following packages with Anaconda: cudatoolkit (10.0) and cudnn (7.6.5). So, after running
conda install cudatoolkit=10.0
conda install cudnn=7.6.5
tensorflow 1.15 was able to find and use GPU (which is using cuda 10.1).
PS: I understand your environment is Windows based, but this question pops on Google for the same problem happening on Linux (where I tested this solution). Might be useful also on Windows.
It might have to do with the version compatibility of TF, Cuda and CuDNN. This post has it discussed thoroughly.
Have you tried installing Anaconda? it downloads all the requirements and make it easy for you with just a few clicks.
I've been able to successfully set up an Ubuntu 18.04 server with nvidia-smi 418.39, Driver version 418.39, and CUDA 10.1
I now have a user who wants to run TensorFlow but insists that it is not compatible with CUDA 10.1, only CUDA 10. There is no statement confirming this online anywhere that I can find, nor is it in any release patch notes from TF. Because setting this system up was kind of a pain to do, I'm a little hesitant to try downgrading just one version.
Does anyone have verification whether TensorFlow 1.12 does or does not work with CUDA 10.1?
I can confirm that even tf 1.13.1 only works with CUDA 10.0 for me, not 10.1.
Don't know if symlink will work through.
If you try to run tf 1.13.1 on CUDA 10.1, it will give you "ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory"
TensorFlow 1.12 (and even later versions 1.13.1 and 2.0.0-alpha0) could not be built against CUDA 10.1, thus can be considered incompatible.
I have tried building TensorFlow from source with GPU support. The TensorFlow versions I considered were 1.13.1 and 2.0.0-alpha0. The machine I used runs CentOS 7.6 with GCC 4.8.5. I have the NVIDIA Driver version 418.67 installed (which has the release date 2019.5.7 and supports CUDA Toolkit 10.1).
I succeeded in building both TensorFlow versions with CUDA 10.0 and cuDNN 7.6.0 + NCCL 2.4.7 (for CUDA 10.0). Note that you don't need to have the GPU attached to the machine (especially if you're using a VM in the cloud) while you're building TensorFlow with GPU support.
However, when I switched to CUDA 10.1 and cuDNN 7.6.0 + NCCL 2.4.7 (for CUDA 10.1), none of these TensorFlow versions could be built. Besides the changes in location of libcublas, another source of the error is no libcudart.so* are found in cuda-10.1/lib64/ (while they do exist in cuda-10.0/lib64/).
I can also confirm that tf 1.13.1 does not work with CUDA 10.1. While importing tensorflow you will get the following error
ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory
running ldconfig -v shows the difference
libcublas.so.10.0 vs libcublas.so.10.1.0.105
i've imported tensorflow gpu, but pycharm doesn't recognize it
details:
IDE -pycharm
GPU- grid p40-1Q
cuda - 8
cudnn - 7.1
python - 3.5
i'm getting this message:
Have you checked with
nvidia-smi
and see if your graphic driver is working? Also, have you checked the version of your tensorflow-gpu ? check for compatible configurations here https://www.tensorflow.org/install/source#tested_source_configurations
I tried with the following configuration on pycharm with windows 10 and it worked!!
Anaconda navigator with python 3.6.8
CUDA 9.0
CUDNN 7.1
Nvidia GeForce 1050 Ti GPU
Follow the instructions mentioned in https://stackoverflow.com/a/51307381/2562870
I've tried tensorflow on both cuda 7.5 and 8.0, w/o cudnn (my GPU is old, cudnn doesn't support it).
When I execute device_lib.list_local_devices(), there is no gpu in the output. Theano sees my gpu, and works fine with it, and examples in /usr/share/cuda/samples work fine as well.
I installed tensorflow through pip install. Is my gpu too old for tf to support it? gtx 460
I came across this same issue in jupyter notebooks. This could be an easy fix.
$ pip uninstall tensorflow
$ pip install tensorflow-gpu
You can check if it worked with:
tf.test.gpu_device_name()
Update 2020
It seems like tensorflow 2.0+ comes with gpu capabilities therefore
pip install tensorflow should be enough
Summary:
check if tensorflow sees your GPU (optional)
check if your videocard can work with tensorflow (optional)
find versions of CUDA Toolkit and cuDNN SDK, compatible with your tf version
install CUDA Toolkit
install cuDNN SDK
pip uninstall tensorflow; pip install tensorflow-gpu
check if tensorflow sees your GPU
* source - https://www.tensorflow.org/install/gpu
Detailed instruction:
check if tensorflow sees your GPU (optional)
from tensorflow.python.client import device_lib
def get_available_devices():
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos]
print(get_available_devices())
# my output was => ['/device:CPU:0']
# good output must be => ['/device:CPU:0', '/device:GPU:0']
check if your card can work with tensorflow (optional)
my PC: GeForce GTX 1060 notebook (driver version - 419.35), windows 10, jupyter notebook
tensorflow needs Compute Capability 3.5 or higher. (https://www.tensorflow.org/install/gpu#hardware_requirements)
https://developer.nvidia.com/cuda-gpus
select "CUDA-Enabled GeForce Products"
result - "GeForce GTX 1060 Compute Capability = 6.1"
my card can work with tf!
find versions of CUDA Toolkit and cuDNN SDK, that you need
a) find your tf version
import sys
print (sys.version)
# 3.6.4 |Anaconda custom (64-bit)| (default, Jan 16 2018, 10:22:32) [MSC v.1900 64 bit (AMD64)]
import tensorflow as tf
print(tf.__version__)
# my output was => 1.13.1
b) find right versions of CUDA Toolkit and cuDNN SDK for your tf version
https://www.tensorflow.org/install/source#linux
* it is written for linux, but worked in my case
see, that tensorflow_gpu-1.13.1 needs: CUDA Toolkit v10.0, cuDNN SDK v7.4
install CUDA Toolkit
a) install CUDA Toolkit 10.0
https://developer.nvidia.com/cuda-toolkit-archive
select: CUDA Toolkit 10.0 and download base installer (2 GB)
installation settings: select only CUDA
(my installation path was: D:\Programs\x64\Nvidia\Cuda_v_10_0\Development)
b) add environment variables:
system variables / path must have:
D:\Programs\x64\Nvidia\Cuda_v_10_0\Development\bin
D:\Programs\x64\Nvidia\Cuda_v_10_0\Development\libnvvp
D:\Programs\x64\Nvidia\Cuda_v_10_0\Development\extras\CUPTI\libx64
D:\Programs\x64\Nvidia\Cuda_v_10_0\Development\include
install cuDNN SDK
a) download cuDNN SDK v7.4
https://developer.nvidia.com/rdp/cudnn-archive (needs registration, but it is simple)
select "Download cuDNN v7.4.2 (Dec 14, 2018), for CUDA 10.0"
b) add path to 'bin' folder into "environment variables / system variables / path":
D:\Programs\x64\Nvidia\cudnn_for_cuda_10_0\bin
pip uninstall tensorflow
pip install tensorflow-gpu
check if tensorflow sees your GPU
- restart your PC
- print(get_available_devices())
- # now this code should return => ['/device:CPU:0', '/device:GPU:0']
If you are using conda, you might have installed the cpu version of the tensorflow. Check package list (conda list) of the environment to see if this is the case . If so, remove the package by using conda remove tensorflow and install keras-gpu instead (conda install -c anaconda keras-gpu. This will install everything you need to run your machine learning codes in GPU. Cheers!
P.S. You should check first if you have installed the drivers correctly using nvidia-smi. By default, this is not in your PATH so you might as well need to add the folder to your path. The .exe file can be found at C:\Program Files\NVIDIA Corporation\NVSMI
When I look up your GPU, I see that it only supports CUDA Compute Capability 2.1. (Can be checked through https://developer.nvidia.com/cuda-gpus) Unfortunately, TensorFlow needs a GPU with minimum CUDA Compute Capability 3.0.
https://www.tensorflow.org/get_started/os_setup#optional_install_cuda_gpus_on_linux
You might see some logs from TensorFlow checking your GPU, but ultimately the library will avoid using an unsupported GPU.
The following worked for me, hp laptop. I have a Cuda Compute capability
(version) 3.0 compatible Nvidia card. Windows 7.
pip3.6.exe uninstall tensorflow-gpu
pip3.6.exe uninstall tensorflow-gpu
pip3.6.exe install tensorflow-gpu
I had a problem because I didn't specify the version of Tensorflow so my version was 2.11. After many hours I found that my problem is described in install guide:
Caution: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin
Before that, I read most of the answers to this and similar questions. I followed #AndrewPt answer. I already had installed CUDA but updated the version just in case, installed cudNN, and restarted the computer.
The easiest solution for me was to downgrade to 2.10 (you can try different options mentioned in the install guide). I first uninstalled all of these packages (probably it's not necessary, but I didn't want to see how pip messed up versions at 2 am):
pip uninstall keras
pip uninstall tensorflow-io-gcs-filesystem
pip uninstall tensorflow-estimator
pip uninstall tensorflow
pip uninstall Keras-Preprocessing
pip uninstall tensorflow-intel
because I wanted only packages required for the old version, and I didn't do it for all required packages for 2.11 version. After that I installed tensorflow 2.10:
pip install tensorflow<2.11
and it worked.
I used this code to check if GPU is visible:
import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))
So as of 2022-04, the tensorflow package contains both CPU and GPU builds. To install a GPU build, search to see what's available:
λ conda search tensorflow
Loading channels: done
# Name Version Build Channel
tensorflow 0.12.1 py35_1 conda-forge
tensorflow 0.12.1 py35_2 conda-forge
tensorflow 1.0.0 py35_0 conda-forge
…
tensorflow 2.5.0 mkl_py39h1fa1df6_0 pkgs/main
tensorflow 2.6.0 eigen_py37h37bbdb1_0 pkgs/main
tensorflow 2.6.0 eigen_py38h63d3545_0 pkgs/main
tensorflow 2.6.0 eigen_py39h855417c_0 pkgs/main
tensorflow 2.6.0 gpu_py37h3e8f0e3_0 pkgs/main
tensorflow 2.6.0 gpu_py38hc0e8100_0 pkgs/main
tensorflow 2.6.0 gpu_py39he88c5ba_0 pkgs/main
tensorflow 2.6.0 mkl_py37h9623b36_0 pkgs/main
tensorflow 2.6.0 mkl_py38hdc16138_0 pkgs/main
tensorflow 2.6.0 mkl_py39h31650da_0 pkgs/main
You can see that there are builds of TF 2.6.0 that support Python 3.7, 3.8 and 3.9, and that are built for MKL (Intel CPU), Eigen, or GPU.
To narrow it down, you can use wildcards in the search. This will find any Tensorflow 2.x version that is built for GPU, for instance:
λ conda search tensorflow=2*=gpu*
Loading channels: done
# Name Version Build Channel
tensorflow 2.0.0 gpu_py36hfdd5754_0 pkgs/main
tensorflow 2.0.0 gpu_py37h57d29ca_0 pkgs/main
tensorflow 2.1.0 gpu_py36h3346743_0 pkgs/main
tensorflow 2.1.0 gpu_py37h7db9008_0 pkgs/main
tensorflow 2.5.0 gpu_py37h23de114_0 pkgs/main
tensorflow 2.5.0 gpu_py38h8e8c102_0 pkgs/main
tensorflow 2.5.0 gpu_py39h7dc34a2_0 pkgs/main
tensorflow 2.6.0 gpu_py37h3e8f0e3_0 pkgs/main
tensorflow 2.6.0 gpu_py38hc0e8100_0 pkgs/main
tensorflow 2.6.0 gpu_py39he88c5ba_0 pkgs/main
To install a specific version in an otherwise empty environment, you can use a command like:
λ conda activate tf
(tf) λ conda install tensorflow=2.6.0=gpu_py39he88c5ba_0
…
The following NEW packages will be INSTALLED:
_tflow_select pkgs/main/win-64::_tflow_select-2.1.0-gpu
…
cudatoolkit pkgs/main/win-64::cudatoolkit-11.3.1-h59b6b97_2
cudnn pkgs/main/win-64::cudnn-8.2.1-cuda11.3_0
…
tensorflow pkgs/main/win-64::tensorflow-2.6.0-gpu_py39he88c5ba_0
tensorflow-base pkgs/main/win-64::tensorflow-base-2.6.0-gpu_py39hb3da07e_0
…
As you can see, if you install a GPU build, it will automatically also install compatible cudatoolkit and cudnn packages. You don't need to manually check versions for compatibility, or manually download several gigabytes from Nvidia's website, or register as a developer, as it says in other answers or on the official website.
After installation, confirm that it worked and it sees the GPU by running:
λ python
Python 3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'2.6.0'
>>> tf.config.list_physical_devices()
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Getting conda to install a GPU build and other packages you want to use is another story, however, because there are a lot of package incompatibilities for me. I think the best you can do is specify the installation criteria using wildcards and cross your fingers.
This tries to install any TF 2.x version that's built for GPU and that has dependencies compatible with Spyder and matplotlib's dependencies, for instance:
λ conda install tensorflow=2*=gpu* spyder matplotlib
For me, this ended up installing a two year old GPU version of tensorflow:
matplotlib pkgs/main/win-64::matplotlib-3.5.1-py37haa95532_1
spyder pkgs/main/win-64::spyder-5.1.5-py37haa95532_1
tensorflow pkgs/main/win-64::tensorflow-2.1.0-gpu_py37h7db9008_0
I had previously been using the tensorflow-gpu package, but that doesn't work anymore. conda typically grinds forever trying to find compatible packages to install, and even when it's installed, it doesn't actually install a gpu build of tensorflow or the CUDA dependencies:
λ conda list
…
cookiecutter 1.7.2 pyhd3eb1b0_0
cryptography 3.4.8 py38h71e12ea_0
cycler 0.11.0 pyhd3eb1b0_0
dataclasses 0.8 pyh6d0b6a4_7
…
tensorflow 2.3.0 mkl_py38h8557ec7_0
tensorflow-base 2.3.0 eigen_py38h75a453f_0
tensorflow-estimator 2.6.0 pyh7b7c402_0
tensorflow-gpu 2.3.0 he13fc11_0
I have had an issue where I needed the latest TensorFlow (2.8.0 at the time of writing) with GPU support running in a conda environment. The problem was that it was not available via conda. What I did was
conda install cudatoolkit==11.2
pip install tensorflow-gpu==2.8.0
Although I've cheched that the cuda toolkit version was compatible with the tensorflow version, it was still returning an error, where libcudart.so.11.0 was not found. As a result, GPUs were not visible. The remedy was to set environmental variable LD_LIBRARY_PATH to point to your anaconda3/envs/<your_tensorflow_environment>/lib with this command
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/<user>/anaconda3/envs/<your_tensorflow_environment>/lib
Unless you make it permanent, you will need to create this variable every time you start a terminal prior to a session (jupyter notebook). It can be conveniently automated by following this procedure from conda's official website.
In my case, I had a working tensorflow-gpu version 1.14 but suddenly it stopped working. I fixed the problem using:
pip uninstall tensorflow-gpu==1.14
pip install tensorflow-gpu==1.14
I experienced the same problem on my Windows OS. I followed tensorflow's instructions on installing CUDA, cudnn, etc., and tried the suggestions in the answers above - with no success.
What solved my issue was to update my GPU drivers. You can update them via:
Pressing windows-button + r
Entering devmgmt.msc
Right-Clicking on "Display adapters" and clicking on the "Properties" option
Going to the "Driver" tab and selecting "Updating Driver".
Finally, click on "Search automatically for updated driver software"
Restart your machine and run the following check again:
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
[x.name for x in local_device_protos]
Sample output:
2022-01-17 13:41:10.557751: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce 940MX major: 5 minor: 0 memoryClockRate(GHz): 1.189
pciBusID: 0000:01:00.0
2022-01-17 13:41:10.558125: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2022-01-17 13:41:10.562095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2022-01-17 13:45:11.392814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-01-17 13:45:11.393617: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187] 0
2022-01-17 13:45:11.393739: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0: N
2022-01-17 13:45:11.401271: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/device:GPU:0 with 1391 MB memory) -> physical GPU (device: 0, name: GeForce 940MX, pci bus id: 0000:01:00.0, compute capability: 5.0)
>>> [x.name for x in local_device_protos]
['/device:CPU:0', '/device:GPU:0']