15

I have a system with an NVIDIA GeForce GTX 980 Ti. I installed tensorflow, and look for the gpu device with tf.test.gpu_device_name(). It looks like it finds the gpu, but then says "Adding visible gpu devices: 0"

>>> import tensorflow as tf
>>> tf.test.gpu_device_name()
2019-01-08 10:01:12.589000: I tensorflow/core/platform/cpu_feature_guard.cc:141]
 Your CPU supports instructions that this TensorFlow binary was not compiled to
use: AVX2
2019-01-08 10:01:12.855000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1
432] Found device 0 with properties:
name: GeForce GTX 980 Ti major: 5 minor: 2 memoryClockRate(GHz): 1.228
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 5.67GiB
2019-01-08 10:01:12.862000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1
511] Adding visible gpu devices: 0
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3
  • 11
    The device is 0... and it is detected. Commented Jan 8, 2019 at 15:20
  • but it isn't being used. On another system (same gpu) it responds with "Created Tensorflow device (/device:GPU:0 with 75MB memory)-> physical GPU (device: 0, name GeForce GTX 980 Ti, pci bus id: 0000:01:00.0, compute capability: 5.2" Commented Jan 8, 2019 at 15:23
  • tensorflow.org/guide/using_gpu this has all you need to know to use gpu with tensorflow Commented May 14, 2019 at 8:54

4 Answers 4

Reset to default
32

Interestingly, the 0 you are concerned about is not the 0 you would use for counting. Precisely, its not "detected 0 devices" but " device 0 detected". "Adding visible device 0", 0 here is an identity for you GPU. Or you can say, the way of tensorflow to differentiate between multiple GPUs in the system. Here is the output of my system, and I'm pretty sure, I m using up my gpu for computation. So don't worry. You are good to go! 😉

Python 3.6.6 (v3.6.6:4cf1f54eb7, Jun 27 2018, 03:37:03) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.test.gpu_device_name()
2019-01-08 20:51:02.212125: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-01-08 20:51:03.199893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 0 with properties:
name: GeForce GTX 1060 with Max-Q Design major: 6 minor: 1 memoryClockRate(GHz): 1.3415
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-01-08 20:51:03.207308: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1490] Adding visible gpu devices: 0
2019-01-08 20:51:04.857881: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-08 20:51:04.861791: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977]      0
2019-01-08 20:51:04.863796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0:   N
2019-01-08 20:51:04.867507: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1103] Created TensorFlow device (/device:GPU:0 with 4722 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 6.1)
'/device:GPU:0'

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2 Comments

Also, to be sure about the GPU not working, I would suggest you to go to this link and import tensorflow and run a simple graph while keeping an eye on the gpu activity panel. If everything is fine, you would be able to see python.exe there.
In TensorFlow, the supported device types are CPU and GPU. They are represented as strings. For example: "/cpu:0": The CPU of your machine. "/device:GPU:0": The GPU of your machine, if you have one. "/device:GPU:1": The second GPU of your machine, etc.
1

Running the prompt as administrator solved the issue in my case

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1 Comment

This worked for me. Thanks!! TensorflowJS 3.6.1/ Cuda 11.0/RTX 2060 MAXQ. Though, I have to downgrade Cuda driver from 11.3 to 11.0 due to failing 'cusolver64_10.dll'.
0

You can try one of the following commands:

  1. device_lib.list_local_devices()
  2. print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

This will show you the gpu devices and their number.

My setup is as following to overcome the issue:

tensorflow 2.4.1

cuda 11.0.2

cudNN 8.1.0

So first you install tensorflow. Then you proceed with cuda (https://developer.nvidia.com/cuda-11.0-download-archive) and after you download the cudNN zip file from here -> https://developer.nvidia.com/rdp/cudnn-download, unzip and paste the cudnn64_8.dll file into C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0\bin. Then everything works like a charm.

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I was also facing the same problem and creating a conda environment with an environment.yml file solved the issue for me. The content of the .yml file are as follows: Please make sure to provide your system path in the last line of the code. eg. "/home/nikhilanand_1921cs24" should be replaced with your system path.

name: keras-gpu
channels:
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=4.5=1_gnu
  - _tflow_select=2.1.0=gpu
  - absl-py=0.13.0=py39h06a4308_0
  - aiohttp=3.8.1=py39h7f8727e_0
  - aiosignal=1.2.0=pyhd3eb1b0_0
  - astor=0.8.1=py39h06a4308_0
  - astunparse=1.6.3=py_0
  - async-timeout=4.0.1=pyhd3eb1b0_0
  - attrs=21.2.0=pyhd3eb1b0_0
  - blas=1.0=mkl
  - blinker=1.4=py39h06a4308_0
  - brotli=1.0.9=h7f98852_5
  - brotli-bin=1.0.9=h7f98852_5
  - brotlipy=0.7.0=py39h27cfd23_1003
  - c-ares=1.17.1=h27cfd23_0
  - ca-certificates=2021.10.8=ha878542_0
  - cachetools=4.2.2=pyhd3eb1b0_0
  - certifi=2021.10.8=py39hf3d152e_1
  - cffi=1.14.6=py39h400218f_0
  - charset-normalizer=2.0.4=pyhd3eb1b0_0
  - click=8.0.3=pyhd3eb1b0_0
  - cryptography=3.4.8=py39hd23ed53_0
  - cudatoolkit=10.1.243=h6bb024c_0
  - cudnn=7.6.5=cuda10.1_0
  - cupti=10.1.168=0
  - cycler=0.11.0=pyhd8ed1ab_0
  - dataclasses=0.8=pyh6d0b6a4_7
  - dbus=1.13.6=he372182_0
  - expat=2.2.10=h9c3ff4c_0
  - fontconfig=2.13.1=h6c09931_0
  - fonttools=4.25.0=pyhd3eb1b0_0
  - freetype=2.10.4=h0708190_1
  - frozenlist=1.2.0=py39h7f8727e_0
  - gast=0.4.0=pyhd3eb1b0_0
  - glib=2.69.1=h4ff587b_1
  - google-auth=1.33.0=pyhd3eb1b0_0
  - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
  - google-pasta=0.2.0=pyhd3eb1b0_0
  - grpcio=1.42.0=py39hce63b2e_0
  - gst-plugins-base=1.14.0=hbbd80ab_1
  - gstreamer=1.14.0=h28cd5cc_2
  - h5py=2.10.0=py39hec9cf62_0
  - hdf5=1.10.6=hb1b8bf9_0
  - icu=58.2=hf484d3e_1000
  - idna=3.3=pyhd3eb1b0_0
  - importlib-metadata=4.8.2=py39h06a4308_0
  - intel-openmp=2021.4.0=h06a4308_3561
  - jpeg=9d=h7f8727e_0
  - keras-preprocessing=1.1.2=pyhd3eb1b0_0
  - kiwisolver=1.3.1=py39h2531618_0
  - lcms2=2.12=hddcbb42_0
  - ld_impl_linux-64=2.35.1=h7274673_9
  - libbrotlicommon=1.0.9=h7f98852_5
  - libbrotlidec=1.0.9=h7f98852_5
  - libbrotlienc=1.0.9=h7f98852_5
  - libffi=3.3=he6710b0_2
  - libgcc-ng=9.3.0=h5101ec6_17
  - libgfortran-ng=7.5.0=ha8ba4b0_17
  - libgfortran4=7.5.0=ha8ba4b0_17
  - libgomp=9.3.0=h5101ec6_17
  - libpng=1.6.37=h21135ba_2
  - libprotobuf=3.17.2=h4ff587b_1
  - libstdcxx-ng=9.3.0=hd4cf53a_17
  - libtiff=4.2.0=h85742a9_0
  - libuuid=1.0.3=h7f8727e_2
  - libwebp-base=1.2.0=h27cfd23_0
  - libxcb=1.13=h7f98852_1003
  - libxml2=2.9.12=h03d6c58_0
  - lz4-c=1.9.3=h9c3ff4c_1
  - markdown=3.3.4=py39h06a4308_0
  - matplotlib=3.4.3=py39hf3d152e_2
  - matplotlib-base=3.4.3=py39hbbc1b5f_0
  - mkl=2021.4.0=h06a4308_640
  - mkl-service=2.4.0=py39h7f8727e_0
  - mkl_fft=1.3.1=py39hd3c417c_0
  - mkl_random=1.2.2=py39h51133e4_0
  - multidict=5.1.0=py39h27cfd23_2
  - munkres=1.1.4=pyh9f0ad1d_0
  - ncurses=6.3=h7f8727e_2
  - numpy=1.21.2=py39h20f2e39_0
  - numpy-base=1.21.2=py39h79a1101_0
  - oauthlib=3.1.1=pyhd3eb1b0_0
  - olefile=0.46=pyh9f0ad1d_1
  - openssl=1.1.1m=h7f8727e_0
  - opt_einsum=3.3.0=pyhd3eb1b0_1
  - pcre=8.45=h9c3ff4c_0
  - pip=21.2.4=py39h06a4308_0
  - protobuf=3.17.2=py39h295c915_0
  - pthread-stubs=0.4=h36c2ea0_1001
  - pyasn1=0.4.8=pyhd3eb1b0_0
  - pyasn1-modules=0.2.8=py_0
  - pycparser=2.21=pyhd3eb1b0_0
  - pyjwt=2.1.0=py39h06a4308_0
  - pyopenssl=21.0.0=pyhd3eb1b0_1
  - pyparsing=3.0.7=pyhd8ed1ab_0
  - pyqt=5.9.2=py39h2531618_6
  - pysocks=1.7.1=py39h06a4308_0
  - python=3.9.7=h12debd9_1
  - python-dateutil=2.8.2=pyhd8ed1ab_0
  - python-flatbuffers=2.0=pyhd3eb1b0_0
  - python_abi=3.9=2_cp39
  - qt=5.9.7=h5867ecd_1
  - readline=8.1=h27cfd23_0
  - requests=2.26.0=pyhd3eb1b0_0
  - requests-oauthlib=1.3.0=py_0
  - rsa=4.7.2=pyhd3eb1b0_1
  - scipy=1.7.1=py39h292c36d_2
  - setuptools=58.0.4=py39h06a4308_0
  - sip=4.19.13=py39h295c915_0
  - six=1.16.0=pyhd3eb1b0_0
  - sqlite=3.36.0=hc218d9a_0
  - tensorboard-plugin-wit=1.6.0=py_0
  - tensorflow-estimator=2.6.0=pyh7b7c402_0
  - termcolor=1.1.0=py39h06a4308_1
  - tk=8.6.11=h1ccaba5_0
  - tornado=6.1=py39h3811e60_1
  - typing-extensions=3.10.0.2=hd3eb1b0_0
  - typing_extensions=3.10.0.2=pyh06a4308_0
  - tzdata=2021e=hda174b7_0
  - urllib3=1.26.7=pyhd3eb1b0_0
  - werkzeug=2.0.2=pyhd3eb1b0_0
  - wheel=0.37.0=pyhd3eb1b0_1
  - wrapt=1.13.3=py39h7f8727e_2
  - xorg-libxau=1.0.9=h7f98852_0
  - xorg-libxdmcp=1.1.3=h7f98852_0
  - xz=5.2.5=h7b6447c_0
  - yarl=1.6.3=py39h27cfd23_0
  - zipp=3.6.0=pyhd3eb1b0_0
  - zlib=1.2.11=h7f8727e_4
  - zstd=1.4.9=ha95c52a_0
  - pip:
    - joblib==1.1.0
    - keras==2.8.0
    - keras-applications==1.0.8
    - libclang==13.0.0
    - opencv-python==4.5.5.62
    - pandas==1.4.0
    - pillow==9.0.1
    - pytz==2021.3
    - pyyaml==6.0
    - scikit-learn==1.0.2
    - tensorboard==2.8.0
    - tensorboard-data-server==0.6.1
    - tensorflow==2.8.0
    - tensorflow-gpu==2.8.0
    - tensorflow-io-gcs-filesystem==0.23.1
    - tf-estimator-nightly==2.8.0.dev2021122109
    - threadpoolctl==3.0.0
prefix: /home/nikhilanand_1921cs24/anaconda3/envs/keras-gpu

Create the environment by runningconda env create -f environment.yml

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