'Why is Tensorflow not recognizing my GPU after conda install?
I am new to deep learning and I have been trying to install tensorflow-gpu version in my pc in vain for the last 2 days. I avoided installing CUDA and cuDNN drivers since several forums online don't recommend it due to numerous compatibility issues. Since I was already using the conda distribution of python before, I went for the conda install -c anaconda tensorflow-gpu as written in their official website here: https://anaconda.org/anaconda/tensorflow-gpu .
However even after installing the gpu version in a fresh virtual environment (to avoid potential conflicts with pip installed libraries in the base env), tensorflow doesn't seem to even recognize my GPU for some mysterious reason.
Some of the code snippets I ran(in anaconda prompt) to understand that it wasn't recognizing my GPU:-
1.
>>>from tensorflow.python.client import device_lib
>>>print(device_lib.list_local_devices())
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 7692219132769779763
]
As you can see it completely ignores the GPU.
2.
>>>tf.debugging.set_log_device_placement(True)
>>>a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
2020-12-13 10:11:30.902956: I tensorflow/core/platform/cpu_feature_guard.cc:142] This
TensorFlow
binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU
instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
>>>b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
>>>c = tf.matmul(a, b)
>>>print(c)
tf.Tensor(
[[22. 28.]
[49. 64.]], shape=(2, 2), dtype=float32)
Here, it was supposed to indicate that it ran with a GPU by showing Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0 (as written here: https://www.tensorflow.org/guide/gpu) but nothing like that is present. Also I am not sure what the message after the 2nd line means.
I have also searched for several solutions online including here but almost all of the issues are related to the first manual installation method which I haven't tried yet since everyone recommended this approach.
I don't use cmd anymore since the environment variables somehow got messed up after uninstalling tensorflow-cpu from the base env and on re-installing, it worked perfectly with anaconda prompt but not cmd. This is a separate issue (and widespread also) but I mentioned it in case that has a role to play here. I installed the gpu version in a fresh virtual environment to ensure a clean installation and as far as I understand path variables need to be set up only for manual installation of CUDA and cuDNN libraries.
The card which I use:-(which is CUDA enabled)
C:\WINDOWS\system32>wmic path win32_VideoController get name
Name
NVIDIA GeForce 940MX
Intel(R) HD Graphics 620
Tensorflow and python version I am using currently:-
>>> import tensorflow as tf
>>> tf.__version__
'2.3.0'
Python 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
System information: Windows 10 Home, 64-bit operating system, x64-based processor.
Any help would be really appreciated. Thanks in advance.
Solution 1:[1]
August 2021 Conda install may be working now, as according to @ComputerScientist in the comments below, conda install tensorflow-gpu==2.4.1 will give cudatoolkit-10.1.243 and cudnn-7.6.5
The following was written in Jan 2021 and is out of date
Currently conda install tensorflow-gpu installs tensorflow v2.3.0 and does NOT install the conda cudnn or cudatoolkit packages. Installing them manually (e.g. with conda install cudatoolkit=10.1) does not seem to fix the problem either.
A solution is to install an earlier version of tensorflow, which does install cudnn and cudatoolkit, then upgrade with pip
conda install tensorflow-gpu=2.1
pip install tensorflow-gpu==2.3.1
(2.4.0 uses cuda 11.0 and cudnn 8.0, however cudnn 8.0 is not in anaconda as of 16/12/2020)
Edit: please also see @GZ0's answer, which links to a github discussion with a one-line solution
Solution 2:[2]
The tensorflow build automatically selected by Anaconda on Windows 10 during the installation of tensorflow-gpu 2.3 seems to be faulty. Please find a workaround here (consider upvoting the GitHub answer if you have a GitHub account).
Windows only:
Python 3.7: conda install tensorflow-gpu=2.3 tensorflow=2.3=mkl_py37h936c3e2_0
Python 3.8: conda install tensorflow-gpu=2.3 tensorflow=2.3=mkl_py38h1fcfbd6_0
Solution 3:[3]
@geometrikal solution almost worked for me. But in between installing tensorflow-gpu with conda and installing tensorflow 2.3 with pip, I needed to uninstall the tensorflow parts of the package tensorflow-gpu to avoid conistency warnings by pip. Conda would have uninstalled the whole package. I know Conda does not recommend mixing pip with conda but this is the solution worked that worked and I am tired of spending another day with this issue.
conda create -n tfgpu python=3.7
conda activate tfgpu
conda install tensorflow-gpu=2.1
pip uninstall tensorflow
pip uninstall tensorflow-estimator
pip uninstall tensorboard
pip uninstall tensorboard-plugin-wit
pip install tensorflow==2.3
pip check
Solution 4:[4]
I also have been unable (yet) to get TF 2.3.0 to recognize my Nvidia Quadro Pro 620 GPU.
Note: I have 2 other 'environments' on this PC (windows Pro) All installed via Anaconda:
- Python 3.7.8 TF 2.0.0... recognizes (and uses) the Nvidia GPU
- Python 3.6.9 TF 2.1.0... recognizes (and uses) the Nvidia GPU
- Python 3.8.6 TF 2.3.0... does NOT see the GPU
My Machine has Cuda 11.1; cuDNN 8.0.5
My next thought is to consider downgrading Python from 3.8.6 to 3.7.8 in the 3rd configuration where TF = 2.3.0
Steve
Solution 5:[5]
You will need to install cuDNN and the CUDA toolkit to use your GPU.
First check for the compatible version here.
cuDNN can be found here (requires free account).
CUDA toolkit can be found here.
Again, check for a compatible version BEFORE installing. Newer versions are not backwards compatible.
Solution 6:[6]
I see that your GPU has compute capability 5.0 which is OK, TensorFlow should like it. Thus I assume something went wrong during the environment setup. Please try creating a new environment using:
conda create --name tf_gpu tensorflow-gpu
Then install all other packages you want in tf_gpu and try again.
P.S: it is really important that in the environment you have only one TensorFlow package (the gpu one). If you have more than one, there is no guarantee that
import tensorflow as tf
will import the one you want ...
Solution 7:[7]
Using conda to install TensorFlow is always a better way to manage the multi versions of TensorFlow itself as well as CUDA and CUDNN. I recently create a new conda environment and prepare to install the newest TensorFlow too. I also encountered the issue you mentioned. I checked the dependency list from conda install tensorflow-gpu and found that the cudatoolkit and cudnn packages are missing. As the latest version of tensorflow-gpu at Anaconda is 2.3, I think the problem was already pointed out by @GZ0's answer at the GitHub issue.
Here I list the output below:
Using conda install tensorflow=2.3:
PS > conda install tensorflow-gpu=2.3
## Package Plan ##
environment location: C:\Anaconda3\envs\test_cuda_38
added / updated specs:
- tensorflow-gpu=2.3
The following packages will be downloaded:
package | build
---------------------------|-----------------
absl-py-0.12.0 | py38haa95532_0 176 KB
aiohttp-3.7.4 | py38h2bbff1b_1 513 KB
astunparse-1.6.3 | py_0 17 KB
async-timeout-3.0.1 | py38haa95532_0 14 KB
blas-1.0 | mkl 6 KB
blinker-1.4 | py38haa95532_0 23 KB
brotlipy-0.7.0 |py38h2bbff1b_1003 412 KB
cachetools-4.2.1 | pyhd3eb1b0_0 13 KB
cffi-1.14.5 | py38hcd4344a_0 224 KB
chardet-3.0.4 |py38haa95532_1003 194 KB
click-7.1.2 | pyhd3eb1b0_0 64 KB
coverage-5.5 | py38h2bbff1b_2 272 KB
cryptography-3.4.7 | py38h71e12ea_0 643 KB
cython-0.29.23 | py38hd77b12b_0 1.7 MB
gast-0.4.0 | py_0 15 KB
google-auth-1.29.0 | pyhd3eb1b0_0 76 KB
google-auth-oauthlib-0.4.4 | pyhd3eb1b0_0 18 KB
google-pasta-0.2.0 | py_0 46 KB
grpcio-1.36.1 | py38hc60d5dd_1 1.7 MB
h5py-2.10.0 | py38h5e291fa_0 841 KB
hdf5-1.10.4 | h7ebc959_0 7.9 MB
icc_rt-2019.0.0 | h0cc432a_1 6.0 MB
idna-2.10 | pyhd3eb1b0_0 52 KB
importlib-metadata-3.10.0 | py38haa95532_0 34 KB
intel-openmp-2021.2.0 | haa95532_616 1.8 MB
keras-applications-1.0.8 | py_1 29 KB
keras-preprocessing-1.1.2 | pyhd3eb1b0_0 35 KB
libprotobuf-3.14.0 | h23ce68f_0 1.9 MB
markdown-3.3.4 | py38haa95532_0 144 KB
mkl-2021.2.0 | haa95532_296 115.5 MB
mkl-service-2.3.0 | py38h2bbff1b_1 49 KB
mkl_fft-1.3.0 | py38h277e83a_2 137 KB
mkl_random-1.2.1 | py38hf11a4ad_2 223 KB
multidict-5.1.0 | py38h2bbff1b_2 61 KB
numpy-1.20.1 | py38h34a8a5c_0 23 KB
numpy-base-1.20.1 | py38haf7ebc8_0 4.2 MB
oauthlib-3.1.0 | py_0 91 KB
opt_einsum-3.1.0 | py_0 54 KB
protobuf-3.14.0 | py38hd77b12b_1 242 KB
pyasn1-0.4.8 | py_0 57 KB
pyasn1-modules-0.2.8 | py_0 72 KB
pycparser-2.20 | py_2 94 KB
pyjwt-1.7.1 | py38_0 48 KB
pyopenssl-20.0.1 | pyhd3eb1b0_1 49 KB
pyreadline-2.1 | py38_1 145 KB
pysocks-1.7.1 | py38haa95532_0 31 KB
requests-2.25.1 | pyhd3eb1b0_0 52 KB
requests-oauthlib-1.3.0 | py_0 23 KB
rsa-4.7.2 | pyhd3eb1b0_1 28 KB
scipy-1.6.2 | py38h66253e8_1 13.0 MB
tensorboard-plugin-wit-1.6.0| py_0 630 KB
tensorflow-2.3.0 |mkl_py38h8557ec7_0 6 KB
tensorflow-base-2.3.0 |eigen_py38h75a453f_0 49.5 MB
tensorflow-estimator-2.3.0 | pyheb71bc4_0 271 KB
termcolor-1.1.0 | py38haa95532_1 9 KB
typing-extensions-3.7.4.3 | hd3eb1b0_0 12 KB
typing_extensions-3.7.4.3 | pyh06a4308_0 28 KB
urllib3-1.26.4 | pyhd3eb1b0_0 105 KB
werkzeug-1.0.1 | pyhd3eb1b0_0 239 KB
win_inet_pton-1.1.0 | py38haa95532_0 35 KB
wrapt-1.12.1 | py38he774522_1 49 KB
yarl-1.6.3 | py38h2bbff1b_0 153 KB
------------------------------------------------------------
Total: 210.0 MB
Using conda install tensorflow=2.1:
PS > conda install tensorflow-gpu=2.1
## Package Plan ##
environment location: C:\Anaconda3\envs\test_cuda
added / updated specs:
- tensorflow-gpu=2.1
The following packages will be downloaded:
package | build
---------------------------|-----------------
_tflow_select-2.1.0 | gpu 3 KB
absl-py-0.12.0 | py37haa95532_0 175 KB
aiohttp-3.7.4 | py37h2bbff1b_1 507 KB
astor-0.8.1 | py37haa95532_0 47 KB
async-timeout-3.0.1 | py37haa95532_0 14 KB
blas-1.0 | mkl 6 KB
blinker-1.4 | py37haa95532_0 23 KB
brotlipy-0.7.0 |py37h2bbff1b_1003 337 KB
cachetools-4.2.1 | pyhd3eb1b0_0 13 KB
cffi-1.14.5 | py37hcd4344a_0 220 KB
chardet-3.0.4 |py37haa95532_1003 192 KB
click-7.1.2 | pyhd3eb1b0_0 64 KB
coverage-5.5 | py37h2bbff1b_2 273 KB
cryptography-3.4.7 | py37h71e12ea_0 641 KB
cudatoolkit-10.1.243 | h74a9793_0 300.3 MB
cudnn-7.6.5 | cuda10.1_0 179.1 MB
cython-0.29.23 | py37hd77b12b_0 1.7 MB
gast-0.2.2 | py37_0 155 KB
google-auth-1.29.0 | pyhd3eb1b0_0 76 KB
google-auth-oauthlib-0.4.4 | pyhd3eb1b0_0 18 KB
google-pasta-0.2.0 | py_0 46 KB
grpcio-1.36.1 | py37hc60d5dd_1 1.7 MB
h5py-2.10.0 | py37h5e291fa_0 808 KB
hdf5-1.10.4 | h7ebc959_0 7.9 MB
icc_rt-2019.0.0 | h0cc432a_1 6.0 MB
idna-2.10 | pyhd3eb1b0_0 52 KB
importlib-metadata-3.10.0 | py37haa95532_0 34 KB
intel-openmp-2021.2.0 | haa95532_616 1.8 MB
keras-applications-1.0.8 | py_1 29 KB
keras-preprocessing-1.1.2 | pyhd3eb1b0_0 35 KB
libprotobuf-3.14.0 | h23ce68f_0 1.9 MB
markdown-3.3.4 | py37haa95532_0 144 KB
mkl-2021.2.0 | haa95532_296 115.5 MB
mkl-service-2.3.0 | py37h2bbff1b_1 48 KB
mkl_fft-1.3.0 | py37h277e83a_2 133 KB
mkl_random-1.2.1 | py37hf11a4ad_2 214 KB
multidict-5.1.0 | py37h2bbff1b_2 85 KB
numpy-1.20.1 | py37h34a8a5c_0 23 KB
numpy-base-1.20.1 | py37haf7ebc8_0 4.1 MB
oauthlib-3.1.0 | py_0 91 KB
opt_einsum-3.1.0 | py_0 54 KB
protobuf-3.14.0 | py37hd77b12b_1 240 KB
pyasn1-0.4.8 | py_0 57 KB
pyasn1-modules-0.2.8 | py_0 72 KB
pycparser-2.20 | py_2 94 KB
pyjwt-1.7.1 | py37_0 49 KB
pyopenssl-20.0.1 | pyhd3eb1b0_1 49 KB
pyreadline-2.1 | py37_1 143 KB
pysocks-1.7.1 | py37_1 28 KB
requests-2.25.1 | pyhd3eb1b0_0 52 KB
requests-oauthlib-1.3.0 | py_0 23 KB
rsa-4.7.2 | pyhd3eb1b0_1 28 KB
scipy-1.6.2 | py37h66253e8_1 12.8 MB
six-1.15.0 | py37haa95532_0 51 KB
tensorboard-plugin-wit-1.6.0| py_0 630 KB
tensorflow-2.1.0 |gpu_py37h7db9008_0 4 KB
tensorflow-base-2.1.0 |gpu_py37h55f5790_0 105.3 MB
tensorflow-estimator-2.1.0 | pyhd54b08b_0 251 KB
tensorflow-gpu-2.1.0 | h0d30ee6_0 3 KB
termcolor-1.1.0 | py37haa95532_1 9 KB
typing-extensions-3.7.4.3 | hd3eb1b0_0 12 KB
typing_extensions-3.7.4.3 | pyh06a4308_0 28 KB
urllib3-1.26.4 | pyhd3eb1b0_0 105 KB
werkzeug-0.16.1 | py_0 258 KB
win_inet_pton-1.1.0 | py37haa95532_0 35 KB
wrapt-1.12.1 | py37he774522_1 49 KB
yarl-1.6.3 | py37h2bbff1b_0 151 KB
------------------------------------------------------------
Total: 745.0 MB
Therefore, you may install the latest version (v2.3) of tensorflow-gpu from Anaconda on Windows platform using the advises from @GZ0 and @geometrikal, or just using conda install tensorflow-gpu=2.1 to get the newest and right environment.
Notice that tensorflow-gpu v2.1 only support Python between 3.5-3.7.
Solution 8:[8]
As of August 2021, with TensorFlow 2.4.1, I believe it seems to install CUDA and CuDNN in a conda environment. Here's what I did to create a fresh conda env on an Ubuntu 18.04 machine:
conda create --name tftest python=3.7 -y && conda activate tftest
conda install ipython tensorflow-gpu==2.4.1 -y
The command above will list the following packages to be installed. For our purposes, note how cudatoolkit and cudnn are listed.
The following packages will be downloaded:
package | build
---------------------------|-----------------
_tflow_select-2.1.0 | gpu 2 KB
absl-py-0.13.0 | py37h06a4308_0 173 KB
aiohttp-3.7.4 | py37h27cfd23_1 536 KB
astor-0.8.1 | py37h06a4308_0 47 KB
astunparse-1.6.3 | py_0 17 KB
async-timeout-3.0.1 | py37h06a4308_0 13 KB
attrs-21.2.0 | pyhd3eb1b0_0 46 KB
backcall-0.2.0 | pyhd3eb1b0_0 13 KB
blas-1.0 | mkl 6 KB
blinker-1.4 | py37h06a4308_0 23 KB
brotlipy-0.7.0 |py37h27cfd23_1003 320 KB
c-ares-1.17.1 | h27cfd23_0 108 KB
cachetools-4.2.2 | pyhd3eb1b0_0 13 KB
cffi-1.14.6 | py37h400218f_0 223 KB
chardet-3.0.4 |py37h06a4308_1003 175 KB
charset-normalizer-2.0.4 | pyhd3eb1b0_0 35 KB
click-8.0.1 | pyhd3eb1b0_0 79 KB
coverage-5.5 | py37h27cfd23_2 254 KB
cryptography-3.4.7 | py37hd23ed53_0 904 KB
cudatoolkit-10.1.243 | h6bb024c_0 347.4 MB
cudnn-7.6.5 | cuda10.1_0 179.9 MB
cupti-10.1.168 | 0 1.4 MB
cython-0.29.24 | py37h295c915_0 1.9 MB
decorator-5.0.9 | pyhd3eb1b0_0 12 KB
gast-0.4.0 | pyhd3eb1b0_0 13 KB
google-auth-1.33.0 | pyhd3eb1b0_0 80 KB
google-auth-oauthlib-0.4.4 | pyhd3eb1b0_0 18 KB
google-pasta-0.2.0 | py_0 46 KB
grpcio-1.36.1 | py37h2157cd5_1 1.9 MB
h5py-2.10.0 | py37hd6299e0_1 902 KB
hdf5-1.10.6 | hb1b8bf9_0 3.7 MB
idna-3.2 | pyhd3eb1b0_0 48 KB
importlib-metadata-3.10.0 | py37h06a4308_0 33 KB
intel-openmp-2021.3.0 | h06a4308_3350 1.4 MB
ipython-7.26.0 | py37hb070fc8_0 1005 KB
ipython_genutils-0.2.0 | pyhd3eb1b0_1 27 KB
jedi-0.18.0 | py37h06a4308_1 911 KB
keras-preprocessing-1.1.2 | pyhd3eb1b0_0 35 KB
libgfortran-ng-7.5.0 | ha8ba4b0_17 22 KB
libgfortran4-7.5.0 | ha8ba4b0_17 995 KB
libprotobuf-3.17.2 | h4ff587b_1 2.0 MB
markdown-3.3.4 | py37h06a4308_0 127 KB
matplotlib-inline-0.1.2 | pyhd3eb1b0_2 12 KB
mkl-2021.3.0 | h06a4308_520 141.2 MB
mkl-service-2.4.0 | py37h7f8727e_0 56 KB
mkl_fft-1.3.0 | py37h42c9631_2 170 KB
mkl_random-1.2.2 | py37h51133e4_0 287 KB
multidict-5.1.0 | py37h27cfd23_2 66 KB
numpy-1.20.3 | py37hf144106_0 23 KB
numpy-base-1.20.3 | py37h74d4b33_0 4.5 MB
oauthlib-3.1.1 | pyhd3eb1b0_0 90 KB
opt_einsum-3.3.0 | pyhd3eb1b0_1 57 KB
parso-0.8.2 | pyhd3eb1b0_0 69 KB
pexpect-4.8.0 | pyhd3eb1b0_3 53 KB
pickleshare-0.7.5 | pyhd3eb1b0_1003 13 KB
prompt-toolkit-3.0.17 | pyh06a4308_0 256 KB
protobuf-3.17.2 | py37h295c915_0 319 KB
ptyprocess-0.7.0 | pyhd3eb1b0_2 17 KB
pyasn1-0.4.8 | py_0 57 KB
pyasn1-modules-0.2.8 | py_0 72 KB
pygments-2.10.0 | pyhd3eb1b0_0 725 KB
pyjwt-2.1.0 | py37h06a4308_0 32 KB
pyopenssl-20.0.1 | pyhd3eb1b0_1 49 KB
pysocks-1.7.1 | py37_1 27 KB
python-flatbuffers-1.12 | pyhd3eb1b0_0 24 KB
requests-2.26.0 | pyhd3eb1b0_0 59 KB
requests-oauthlib-1.3.0 | py_0 23 KB
rsa-4.7.2 | pyhd3eb1b0_1 28 KB
scipy-1.6.2 | py37had2a1c9_1 15.5 MB
six-1.16.0 | pyhd3eb1b0_0 18 KB
tensorboard-2.4.0 | pyhc547734_0 8.8 MB
tensorboard-plugin-wit-1.6.0| py_0 630 KB
tensorflow-2.4.1 |gpu_py37ha2e99fa_0 4 KB
tensorflow-base-2.4.1 |gpu_py37h29c2da4_0 195.2 MB
tensorflow-estimator-2.5.0 | pyh7b7c402_0 267 KB
tensorflow-gpu-2.4.1 | h30adc30_0 3 KB
termcolor-1.1.0 | py37h06a4308_1 9 KB
traitlets-5.0.5 | pyhd3eb1b0_0 81 KB
typing-extensions-3.10.0.0 | hd3eb1b0_0 8 KB
typing_extensions-3.10.0.0 | pyh06a4308_0 27 KB
urllib3-1.26.6 | pyhd3eb1b0_1 112 KB
wcwidth-0.2.5 | py_0 29 KB
werkzeug-1.0.1 | pyhd3eb1b0_0 239 KB
wrapt-1.12.1 | py37h7b6447c_1 49 KB
yarl-1.6.3 | py37h27cfd23_0 133 KB
zipp-3.5.0 | pyhd3eb1b0_0 13 KB
------------------------------------------------------------
Total: 915.9 MB
Next, run ipython and try:
In [1]: import tensorflow as tf
2021-08-29 12:26:36.582384: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
In [2]: tf.config.list_physical_devices('GPU')
2021-08-29 12:26:48.676151: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-08-29 12:26:48.679894: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-08-29 12:26:48.975002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:04:00.0 name: Tesla V100-PCIE-32GB computeCapability: 7.0
coreClock: 1.38GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-08-29 12:26:48.979341: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 1 with properties:
pciBusID: 0000:08:00.0 name: Tesla V100-PCIE-32GB computeCapability: 7.0
coreClock: 1.38GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-08-29 12:26:48.981747: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 2 with properties:
pciBusID: 0000:09:00.0 name: Tesla V100-PCIE-32GB computeCapability: 7.0
coreClock: 1.38GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-08-29 12:26:48.990002: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 3 with properties:
pciBusID: 0000:85:00.0 name: Tesla V100-PCIE-32GB computeCapability: 7.0
coreClock: 1.38GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-08-29 12:26:48.992488: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 4 with properties:
pciBusID: 0000:89:00.0 name: Tesla V100-PCIE-32GB computeCapability: 7.0
coreClock: 1.38GHz coreCount: 80 deviceMemorySize: 31.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-08-29 12:26:48.992523: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1
2021-08-29 12:26:49.312793: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10
2021-08-29 12:26:49.312907: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10
2021-08-29 12:26:49.388961: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-08-29 12:26:49.413946: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-08-29 12:26:49.535055: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-08-29 12:26:49.563142: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10
2021-08-29 12:26:50.009291: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7
2021-08-29 12:26:50.051301: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0, 1, 2, 3, 4
Out[2]:
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'),
PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU'),
PhysicalDevice(name='/physical_device:GPU:2', device_type='GPU'),
PhysicalDevice(name='/physical_device:GPU:3', device_type='GPU'),
PhysicalDevice(name='/physical_device:GPU:4', device_type='GPU')]
In [3]: tf.test.is_built_with_cuda()
Out[3]: True
This machine has 5 GPUs, so the above output is correct.
What I'm not actually sure about is why CUDA 10.1 and CuDNN 7.6.5 are installed, since it seems like from Google's TF compatibility chart that 2.4.0 (and presumably 2.4.1?) work with CUDA 11.0 and CuDNN 8. If anyone has insights on that, feel free to chime in...
Solution 9:[9]
In my case (In April 2022):
conda install tensorflow-gpu=2.3 tensorflow=2.3=mkl_py38h1fcfbd6_0 cudatoolkit cudnn keras matplotlib
Works perfectly!! it installed tensorflow-gpu=2.3 - cudatoolkit 10.1.243 and cudnn 7.6.5
Solution 10:[10]
Updated April 26, 2022, tensorflow 2.6.0 does the job, Python version 3.8.13
Recap - in Anaconda Jupyter, encountered the same "GPU no show" issue
'2.3.0'
Followed the procedure in this link, again "GPU no show".
Trial and error: workaround below does the job.
- Launch CMD window from Navigator (the same environment);
- conda install tensorflow=2.6.0 –-channel conda-forge -y;
- It will take some time (collecting package, resolving environment etc.);
- In this case, automatically update CUDA and cuDNN versions to 11.3 and 8.2, respectively.
- After it is done, exit Anaconda and restart the computer.
- Return to Jupyter, run the cell:
and check result:
2.6.0
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
Solution 11:[11]
Following Steps worked for me:
Do the same as in the video. https://www.youtube.com/watch?v=r31jnE7pR-g
Also install tensorflow estimator which is missing in the video. In picture you can see my environment which is working for me.
Maybe you have to change the versions to the same ones. Now go in Visual Code and run your code with the anaconda environment you created before. See picture below.
In my case it it tf_env, what i created and named.
Try to run your code. If Visual Code says something is missing try to install it with the anaconda terminal. Click the "play"-Button to start the terminal.
Also close and open Visual Code when you do changes, sometimes anaconda too. Now try this code below.
> print("Num GPU: ", len(tf.config.list_physical_devices("GPU")))
>
> print(tf.test.is_gpu_available()) print(tf.test.is_built_with_cuda())
> OUTPUT
> Num GPU: 1
> WARNING:tensorflow:From <ipython-input-2-8748de971110>:3:
> is_gpu_available (from tensorflow.python.framework.test_util) is
> deprecated and will be removed in a future version. Instructions for
> updating: Use `tf.config.list_physical_devices('GPU')` instead.
> True
> True
If your output is the same, everything went fine. Now when you train a model you should see your gpu running in your task manager.
Hopefully it helps you guys :)
Sources
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Source: Stack Overflow