Stuck with enabling GPUs for Tensorflow in WSL2 under Windows 10 - tensorflow

Question

I can't get Tensorflow 2 to use my GPUs under WSL2. I am aware of this question, but GPU support is now (supposedly) no longer experimental.
Windows is on the required 21H2 version, which should support the WSL2 GPU connection.
Windows 10 Pro, 21H2, build 19044.1706
The PC has two GPUs:
GPU 0: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-19c8549a-4b8d-5d70-456b-776ceece4b0f)
GPU 1: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-2a946756-0472-fb90-f1a4-b40cce1bba4f)
I had installed Ubuntu under WSL2 some time ago:
PS C:\Users\jem-m> wsl --status
Default Distribution: Ubuntu-20.04
Default Version: 2
...
Kernel version: 5.10.16
In the Windows PowerShell, I can run nvidia-smi.exe, which gives me
PS C:\Users\jem-m> nvidia-smi.exe
Mon May 16 18:13:27 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 512.77 Driver Version: 512.77 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... WDDM | 00000000:08:00.0 On | N/A |
| 23% 31C P8 10W / 250W | 753MiB / 11264MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA GeForce ... WDDM | 00000000:41:00.0 Off | N/A |
| 23% 31C P8 12W / 250W | 753MiB / 11264MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
while the nvidia-smi in the WSL2 Ubuntu shell gives
(testenv) jem-mosig:~/ $ nvidia-smi [17:48:30]
Mon May 16 17:49:53 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.68.02 Driver Version: 512.77 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| 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 ... On | 00000000:08:00.0 On | N/A |
| 23% 34C P8 10W / 250W | 784MiB / 11264MiB | 8% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA GeForce ... On | 00000000:41:00.0 Off | N/A |
| 23% 34C P8 13W / 250W | 784MiB / 11264MiB | 12% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
Note the same driver and CUDA version, but different NVIDIA-SMI version.
This seems to indicate that CUDA works under WSL2 as it is supposed to. But when I run
import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))
# 2022-05-17 12:13:05.016328: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
# []
in python inside WSL2 I get [], so no GPU is recognized by Tensorflow. This is Python 3.8.0 and Tensorflow 2.4.1 freshly installed in a new Miniconda environment inside Ubuntu WSL2. I don't know what is going wrong. Any suggestions?
Addendum
I don't get any error messages when importing Tensorflow. But some warnings are produced when working with it. E.g., when I run
import tensorflow as tf
print(tf.__version__)
model = tf.keras.Sequential([tf.keras.layers.Dense(3)])
model.compile(loss="mse")
print(model.predict([[0.]]))
I get
2.4.1
2022-05-17 10:38:28.792209: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2022-05-17 10:38:28.792411: 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: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-05-17 10:38:28.794356: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
2022-05-17 10:38:28.853557: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2022-05-17 10:38:28.860126: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 3792975000 Hz
[[0. 0. 0.]]
These don't seem to be GPU related, though.

Answer 1

Dr. Snoopy got me onto the right track: Despite the fact that the TF website says that
The TensorFlow pip package includes GPU support for CUDA®-enabled cards
, I still needed to run conda install tensorflow-gpu and it worked! Now
import tensorflow as tf
from tensorflow.python.client import device_lib
print("devices: ", [d.name for d in device_lib.list_local_devices()])
print("GPUs: ", tf.config.list_physical_devices('GPU'))
print("TF v.: ", tf.__version__)
gives lots of debug messages and
devices: ['/device:CPU:0', '/device:GPU:0', '/device:GPU:1']
GPUs: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'), PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')]
TF v.: 2.4.1