Hello.
I have installed the latest drivers, Cuda and Cudnn in the host machine and both Cuda and Cudnn inside the WSL.
If I use nvidia-smi I do see the GPU information, both outside and inside WSL:
sergio@DESKTOP-U0MALDT:~$ nvidia-smi
Thu Apr 25 18:24:10 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.14 Driver Version: 551.78 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4070 On | 00000000:07:00.0 On | N/A |
| 0% 43C P5 23W / 200W | 3082MiB / 12282MiB | 22% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 0 N/A N/A 28 G /Xwayland N/A |
+-----------------------------------------------------------------------------------------+
However, when getting the list of available of GPUs through Tensorflow, I get an empty array:
sergio@DESKTOP-U0MALDT:~$ python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2024-04-25 18:29:16.082040: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-04-25 18:29:16.671581: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-04-25 18:29:17.248700: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:984] could not open file to read NUMA node: /sys/bus/pci/devices/0000:07:00.0/numa_node
Your kernel may have been built without NUMA support.
2024-04-25 18:29:17.280233: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] 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...
[]
It tells me there are some missing libraries, however, I’ve installed everything listed in the tutorial and it is still giving me the very same output.
What should I do? How do I proceed from there?