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Problem importing TensorFlow 2 in Python (running on WSL in Windows)

Problem: I followed Microsoft's instruction in order to properly install and run TensorFlow 2 in WSL with GPU acceleration, using DirectML (here's the document).
Following the installation, when I try and import tensorflow in Python I get the following output:
>>> import tensorflow
2022-11-22 15:52:33.090032: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/pietro/miniconda3/envs/testing/lib/python3.9/site-package
/tensorflow/__init__.py", line 440, in <module>
_ll.load_library(_plugin_dir)
File "/home/pietro/miniconda3/envs/testing/lib/python3.9/site-package
/tensorflow/python/framework/load_library.py", line 151, in load_library
py_tf.TF_LoadLibrary(lib)
tensorflow.python.framework.errors_impl.NotFoundError: /home/pietro
/miniconda3/envs/testing/lib/python3.9/site-packages/tensorflow-plugin
/libtfdml_plugin.so: undefined symbol:_ZN10tensorflow8internal15LogMessageFatalD1Ev, version tensorflow
I tried instead to follow the instructions for TensorFlow 1 and PyTorch (just in case something was wrong with my machine) and they both work perfectly, so I assume this issue only involves TensorFlow 2 somehow.
Did anyone encounter the same problem?
Thanks to everybody in advance :)
Pietro

Had the same problem, and downgrading TensorFlow from 2.11 fixed it. First remove the existing version:
pip uninstall tensorflow-cpu
Then re-install, this time with 2.10.0:
pip install tensorflow-cpu==2.10.0
After that, try importing it in Python. You should see something like the following (apologies for the messy output):
>>> import tensorflow as tf
2022-11-28 22:41:21.693757: 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 AVX512F AVX512_VNNI FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-11-28 22:41:21.806150: 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`.
2022-11-28 22:41:22.982148: I tensorflow/c/logging.cc:34] Successfully opened dynamic library libdirectml.d6f03b303ac3c4f2eeb8ca631688c9757b361310.so
2022-11-28 22:41:22.982289: I tensorflow/c/logging.cc:34] Successfully opened dynamic library libdxcore.so
2022-11-28 22:41:22.996385: I tensorflow/c/logging.cc:34] Successfully opened dynamic library libd3d12.so
2022-11-28 22:41:27.615851: I tensorflow/c/logging.cc:34] DirectML device enumeration: found 1 compatible adapters.
You can test that it works by adding two tensors. Running a command like the following:
print(tf.add([1.0, 2.0], [3.0, 4.0]))
And somewhere in the output, you should be able to verify that DirectML has found your GPU:
2022-11-28 22:43:42.632447: I tensorflow/c/logging.cc:34] DirectML: creating device on adapter 0 (NVIDIA GeForce RTX 3080)
Hope this helps!

Error about AVX (?) when running Tensorflow

I'm attempting to do text generation with Tensorflow but whenever I run it I get this:
2022-08-12 19:35:08.527356: 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: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-08-12 19:35:08.945360: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6007 MB memory: -> device: 0, name: NVIDIA GeForce RTX 2070, pci bus id: 0000:29:00.0, compute capability: 7.5
Process finished with exit code -1073740791 (0xC0000409)
what could be wrong?
EDIT 1
The code works if I only use the CPU, I've reinstalled CUDA but it didn't help.

How to remove TensorFlow CUDA messages? TensorFlow was installed for CPU only

I installed TensorFlow 2.8.0 in my Windows 10 PC with CPU only. No Nvidia/CUDA hardware/software installed at all. I run a simple Python image processing project with CNN and I got bunch of no sense messages:
2022-02-23 07:31:55.511878: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-02-23 07:31:55.512119: 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.
2022-02-23 07:32:16.820716: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2022-02-23 07:32:16.820934: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2022-02-23 07:32:16.823066: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-7OCT2M4
2022-02-23 07:32:16.823285: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-7OCT2M4
2022-02-23 07:32:16.823612: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Why I'm getting these CUDA messages? I never installed anything for CUDA. I did try the following like everyone recommended in Google:
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
And still getting these messages. WHY? I really don't want to see any CUDA message because I did not install nothing for it. This is very bad for the TensorFlow deployment team. Why no one test this before in Windows?
I'm waiting for any suggestion to fix this TensorFlow installation issues.

is the min log before the first import of tensorflow in the project? if i move the min log under the import tensorflow i will get the warnings you get
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # removes tf informative messages
import tensorflow as tf # noqa E402
print(tf.__version__)

Tensorflow python

I am trying to use tensorflow to do sentiment analysis using python and this error happens
2019-06-08 17:08:06.104566: IT:\src\github\tensorflow\tensorflow\core\platform\ cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow bi nary was not compiled to use: AVX2

Tensorflow not showing "Successfully opened so & so CUDA libraries locally"

I configured tensorflow to work with CUDA support on my GPU (GeForce 840M) but the programs are running quite slow in compare to what my CPU used to earlier. Also, I do not get any kind of message that the so and so CUDA library was successfully opened when I run the program. Instead, this is what I get in logs when I run any tensorflow program:
python Neuralnet.py
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting /tmp/data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
2017-03-28 07:53:57.979382: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE4.1 instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:57.979413: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE4.2 instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:57.979431: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:57.979438: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX2 instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:57.979447: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use FMA instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:58.233876: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901]
successful NUMA node read from SysFS had negative value (-1),
but there must be at least one NUMA node, so returning NUMA node zero
2017-03-28 07:53:58.234333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887]
Found device 0 with properties:
name: GeForce 840M
major: 5 minor: 0 memoryClockRate (GHz) 1.124
pciBusID 0000:08:00.0
Total memory: 1.96GiB
Free memory: 1.75GiB
2017-03-28 07:53:58.234362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2017-03-28 07:53:58.234372: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2017-03-28 07:53:58.234388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977]
Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 840M, pci bus id: 0000:08:00.0)
('Epoch', 0, 'completed out of', 15, 'loss:', 115374329.04653475)
And so on the program started runnning but it didn't ran any faster according to my expectations. I installed CUDA from the official documentation, but I did not reset the git master head since it was creating issues and I used the same optimization flags provided bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package when building through bazel.

Did you use nvidia-smi to tell whether you have the right cuda drivers installed and that your gpu is visible to the system?
In TF you can set the log_device_placement option to understand if any ops are being assigned to the GPU.