'Tensorflow doesn't seem to see my gpu

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

tensorflow


Solution 1:[1]

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.

Solution 2:[2]

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

Solution 3:[3]

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

Solution 4:[4]

Summary:

  1. check if tensorflow sees your GPU (optional)
  2. check if your videocard can work with tensorflow (optional)
  3. find versions of CUDA Toolkit and cuDNN SDK, compatible with your tf version
  4. install CUDA Toolkit
  5. install cuDNN SDK
  6. pip uninstall tensorflow; pip install tensorflow-gpu
  7. check if tensorflow sees your GPU

* source - https://www.tensorflow.org/install/gpu

Detailed instruction:

  1. 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']

  2. check if your card can work with tensorflow (optional)

  3. 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

  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

  5. 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

  6. pip uninstall tensorflow pip install tensorflow-gpu

  7. check if tensorflow sees your GPU

    - restart your PC
- print(get_available_devices()) 
- # now this code should return => ['/device:CPU:0', '/device:GPU:0']

Solution 5:[5]

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

Solution 6:[6]

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:

? 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:

? 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:

? conda activate tf

(tf) ? 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:

? 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:

? 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:

? 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

Solution 7:[7]

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.

Solution 8:[8]

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

Solution 9:[9]

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:

  1. Pressing windows-button + r
  2. Entering devmgmt.msc
  3. Right-Clicking on "Display adapters" and clicking on the "Properties" option
  4. Going to the "Driver" tab and selecting "Updating Driver".
  5. Finally, click on "Search automatically for updated driver software"
  6. 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']

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Source: Stack Overflow

Solution Source
Solution 1 gunan
Solution 2
Solution 3 Junelle Rey
Solution 4 brasofilo
Solution 5 Björn Berglund
Solution 6
Solution 7
Solution 8 Ibraheem
Solution 9 M M

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