I am trying to install Tensorflow using WSL2 on windows as outlined on the TF website Install TensorFlow with pip. I get to the point where I am getting confirmation the GPU is registering but I am getting quite a bit of feedback and when I try to train a simple CNN it errors out. I have tried to uninstall and reinstall things using instructions nvidia but still no success. When I try to use other methods to install it doe not recognize the gpu. The first things it spits out is
2023-04-15 22:21:52.990342: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:982] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
but this seems to just be a warning, the model compiles but when it starts to train
it finally errors out
2023-04-15 22:21:56.409879: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8600
Could not load library libcudnn_cnn_infer.so.8. Error: libcuda.so: cannot open shared object file: No such file or directory
Aborted
When I try to use other methods to install it doe not recognize the gpu.
Could you please elaborate the steps that you have followed to install tensorflow? Also provide System Configuration, GPU Card, cuDNN, CUDA, NVIDIA driver version and CUDA Compute Capability details to debug further?
Did you managed to install tensorflow with GPU on WSL2? I also have similar problems, everything I tried failed. On WSL2 I have tested pytorch with GPU and CUDA Samples so the problem is not with drivers, etc.
Nothing works and each time I end on WSL with Tensorflow installed without the GPU.
This is the full print:
Python 3.10.6 (main, Mar 10 2023, 10:55:28) [GCC 11.3.0] on linux
Type âhelpâ, âcopyrightâ, âcreditsâ or âlicenseâ for more information.
import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.list_physical_devices(âGPUâ)))
2023-04-18 23:43:25.274791: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-04-18 23:43:25.375614: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2023-04-18 23:43:25.379647: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibcudart.so.11.0â; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2023-04-18 23:43:25.379678: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2023-04-18 23:43:25.396866: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2023-04-18 23:43:25.830051: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibnvinfer.so.7â; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2023-04-18 23:43:25.830118: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibnvinfer_plugin.so.7â; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2023-04-18 23:43:25.830123: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
print("Num GPUs Available: ", len(tf.config.list_physical_devices(âGPUâ)))
2023-04-18 23:43:26.418880: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:966] could not open file to read NUMA node: /sys/bus/pci/devices/0000:01:00.0/numa_node
Your kernel may have been built without NUMA support.
2023-04-18 23:43:26.418995: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibcudart.so.11.0â; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2023-04-18 23:43:26.419042: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibcublas.so.11â; dlerror: libcublas.so.11: cannot open shared object file: No such file or directory
2023-04-18 23:43:26.419081: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibcublasLt.so.11â; dlerror: libcublasLt.so.11: cannot open shared object file: No such file or directory
2023-04-18 23:43:26.419120: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibcufft.so.10â; dlerror: libcufft.so.10: cannot open shared object file: No such file or directory
2023-04-18 23:43:26.447389: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibcusparse.so.11â; dlerror: libcusparse.so.11: cannot open shared object file: No such file or directory
2023-04-18 23:43:26.447475: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library âlibcudnn.so.8â; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory
2023-04-18 23:43:26.447494: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1934] 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 Install TensorFlow with pip for how to download and setup the required libraries for your platform.
Skipping registering GPU devicesâŚ
Num GPUs Available: 0
So I was able to get ii to work the following way
install everything as laid out in Install TensorFlow with pip for wsl2.
Then test if your GPU is being registered with the code they five
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
if this does not read something along the lines of
the GPU is not being mapped correctly. Additionally one thing that thru me off initially is that when you download cuDNN 8.6.0 you need to copy the bin, lib, and included files and then paste them into your CUDA 11.8 folder in program files.
once you have completed installation you have to do these extra steps
while on wsl
cd /usr/lib/wsl/lib/
then backup files "libcuda.so.1" and "libcuda.so" to something like libcuda.so.backup
then
rm -r libcuda.so.1
rm -r libcuda.so
finally
ln -s libcuda.so.1.1 libcuda.so.1
ln -s libcuda.so.1.1 libcuda.so
sudo ldconfig
once you have done that return to the install page and go to the linux part at the bottom it mentions a fix for ubuntu version 22.04 with the following code
conda install -c nvidia cuda-nvcc=11.3.58
# Configure the XLA cuda directory
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
printf 'export XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX/lib/\n' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
# Copy libdevice file to the required path
mkdir -p $CONDA_PREFIX/lib/nvvm/libdevice
cp $CONDA_PREFIX/lib/libdevice.10.bc $CONDA_PREFIX/lib/nvvm/libdevice/
'''
this is what worked for me but I used python 3.9, tensorflow 2.12, CUDA 11.8 and cuDNN 8.6.0
I was able to fix my problem. It turns out that tensorfow still depends from CUDA toolkit 11.8 but following the official link I have installed CUDA toolkit 12.
I donât know if this is the right way but for me the following tutorial worked:
I stopped at installing tensorflow because I already did this.
Now tensorflow recognizes the GPU, only complains about missing TensorRT.
Hello, I have the same issue with using CNN kayers, while with dense layers there are no issue.I checked your way to fix the problem, but the issue is that i do not have libcuda.so but libcuda.so.1 and libcuda.so.1.1 are existing.What would you suggest to fix problem?
Cudnn version is 8.6.0 and cuda version is 11.8, tensorflow 2.14.0 version.
