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I tried everything but tensorflow cant see my gpu. ı gonna show all versions to i have, can anyone has idea about this? 1- my nvidia enter image description here

2 Cuda :version

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  1. CuDNN version : cuDNN (7.6.5)
  2. my tf version

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I follow all this steps from there : https://medium.com/analytics-vidhya/installing-tensorflow-with-cuda-cudnn-gpu-support-on-ubuntu-20-04-f6f67745750a

After this steps i controlled tf ;

>>> tf.config.list_physical_devices('GPU') 2020-12-30 10:41:50.035846: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set 2020-12-30 10:41:50.047043: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1 2020-12-30 10:41:50.080921: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-12-30 10:41:50.081141: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce RTX 2060 SUPER computeCapability: 7.5 coreClock: 1.665GHz coreCount: 34 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s 2020-12-30 10:41:50.081155: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0 2020-12-30 10:41:50.107337: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11 2020-12-30 10:41:50.107387: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11 2020-12-30 10:41:50.126300: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10 2020-12-30 10:41:50.132954: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10 2020-12-30 10:41:50.204340: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10 2020-12-30 10:41:50.212418: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11 2020-12-30 10:41:50.212534: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/cuda/lib64: 2020-12-30 10:41:50.212543: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... [] >>> tf.config.list_physical_devices('GPU') `[]` >>> tf.test.is_built_with_cuda() true >>> tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None) WARNING:tensorflow:From <stdin>:1: 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. 2020-12-30 10:42:47.612041: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set 2020-12-30 10:42:47.612151: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-12-30 10:42:47.612381: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce RTX 2060 SUPER computeCapability: 7.5 coreClock: 1.665GHz coreCount: 34 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s 2020-12-30 10:42:47.612400: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0 2020-12-30 10:42:47.612421: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11 2020-12-30 10:42:47.612431: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11 2020-12-30 10:42:47.612441: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10 2020-12-30 10:42:47.612450: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10 2020-12-30 10:42:47.612459: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10 2020-12-30 10:42:47.612467: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11 2020-12-30 10:42:47.615079: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib/cuda/include:/usr/lib/cuda/lib64: 2020-12-30 10:42:47.615090: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1757] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... 2020-12-30 10:42:47.725208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-12-30 10:42:47.725230: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 2020-12-30 10:42:47.725235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N False
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    Please do not post errors and messages as images. They can't be searched for and will make it much harder for future visitors to find Commented Dec 30, 2020 at 8:10
  • Cuda dependencies are tricky so may be reading it wrong, but to me it looks like your tf version expects cudnn 8.x (libcudnn.so.8). You may want to take a look there. Commented Dec 30, 2020 at 8:12
  • @JoachimIsaksson may be you are right but, if you rewiev a tutorial, every version are same... really diffucult problems. thanks your advice. Commented Dec 30, 2020 at 8:20
  • They are not all the same ! Commented Dec 30, 2020 at 12:54
  • @MohanRadhakrishnan Thank you Mohan, i will try to change true versions. Commented Dec 30, 2020 at 13:50

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Copy the table here for anyone who has the same question .

Version Python version Compiler Build tools cuDNN CUDA
tensorflow-2.4.0 3.6-3.8 GCC 7.3.1 Bazel 3.1.0 8.0 11.0
tensorflow-2.3.0 3.5-3.8 GCC 7.3.1 Bazel 3.1.0 7.6 10.1
tensorflow-2.2.0 3.5-3.8 GCC 7.3.1 Bazel 2.0.0 7.6 10.1
tensorflow-2.1.0 2.7, 3.5-3.7 GCC 7.3.1 Bazel 0.27.1 7.6 10.1
tensorflow-2.0.0 2.7, 3.3-3.7 GCC 7.3.1 Bazel 0.26.1 7.4 10.0
tensorflow_gpu-1.15.0 2.7, 3.3-3.7 GCC 7.3.1 Bazel 0.26.1 7.4 10.0
tensorflow_gpu-1.14.0 2.7, 3.3-3.7 GCC 4.8 Bazel 0.24.1 7.4 10.0
tensorflow_gpu-1.13.1 2.7, 3.3-3.7 GCC 4.8 Bazel 0.19.2 7.4 10.0
tensorflow_gpu-1.12.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.11.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.10.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.15.0 7 9
tensorflow_gpu-1.9.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.11.0 7 9
tensorflow_gpu-1.8.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.10.0 7 9
tensorflow_gpu-1.7.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.9.0 7 9
tensorflow_gpu-1.6.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.9.0 7 9
tensorflow_gpu-1.5.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.8.0 7 9
tensorflow_gpu-1.4.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.5.4 6 8
tensorflow_gpu-1.3.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.4.5 6 8
tensorflow_gpu-1.2.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.4.5 5.1 8
tensorflow_gpu-1.1.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.4.2 5.1 8
tensorflow_gpu-1.0.0 2.7, 3.3-3.6 GCC 4.8 Bazel 0.4.2 5.1 8
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3 Comments

after downgrade tf = 2.2.0 its work! thank you
Is it possible to get an updated list for 2022? Or at least, how did you get this table, so that people in the future can find it out for themselves?
@ShepBryan this list can be found in https://www.tensorflow.org/install/source#gpu

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