已解决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.
2023-03-31 16:58:07.971004: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDMN) 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

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Error report

A friend in the fan group ran into a problem and came to private message me, wanting to use tensorflow, but an error occurred (at that time, his heart was cold for a moment, he came to me for help, and then helped him solve it smoothly, and record it by the way I hope it can help more friends who encounter this bug and cannot solve it), the error code is as follows:

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A screenshot of the content of the error message is as follows :

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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.
2023-03-31 16:58:07.971004: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDMN) 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
False

error translation

The translation of the error message content is as follows :

WARNING: tensorflow:From(stdin)1:is_gpu_available(From tensorflow.python.framework.test_util) is deprecated and will be removed in a future release.
Update Note:
Use tf.config.list_physical_devices('GPU')~ instead.
2023-03-31 16:58:07.971004: I tensorflow/core/platform/cpu_feature_guard.cc:142] This tensorflow binary is optimized using the oneAPI Deep Neural Network library (oneDMN) to use the following cpus in performance critical operations Directives:
AVX-AVX2
To enable them in other ops, use appropriate compiler flags to regenerate TensorFlow
errors

Error reason

Reason for error :

This warning is usually caused by using a function or method that has been deprecated. With this warning, the is_gpu_available function is deprecated and will be removed in a future release.

Friends, follow the method below to solve it! ! !

Solution

This warning is usually caused by using a function or method that has been deprecated. In this warning, is_gpu_availablethe function is deprecated and will be removed in a future release. To fix this, you can try the following:

If you don't need to use the GPU for calculations, you can ignore this warning. You can use other functions or methods in your code to check whether your TensorFlow supports GPU, for example:
```
import tensorflow as tf
print(tf.test.is_built_with_cuda())
print(tf.test.is_gpu_available())
```
A function is used here is_built_with_cuda()to check whether TensorFlow is compiled with CUDA support, and is_gpu_available()a function is used to check whether the GPU is available.
If you need to use GPU for computing, you can try to upgrade your version of TensorFlow. In newer TensorFlow versions, is_gpu_available()functions have been replaced with tf.config.list_physical_devices('GPU')functions. You can use the following code to check if the GPU is available:
```
import tensorflow as tf
print(tf.test.is_built_with_cuda())
print(tf.config.list_physical_devices('GPU'))
```
If you have an older version of TensorFlow, you can try upgrading to a newer version to resolve this issue.

Correspondence between pytorch versions and cuda versions, official website link : https://pytorch.org/get-started/previous-versions/

Note : Does the lower version of pytorch support a higher version of cuda; a higher version of pytorch is generally compatible with a lower version of cuda. For example: you need pytorch 1.7.0, then cuda can only be 11.0 and below. The officially recommended cuda versions are 10.2 and 11.3, which support most pytorch versions.

PyTorch version	CUDA environment
0.4.1、1.2.0、1.4.0、1.5.0(1)、1.6.0、1.7.0(1)	9.2
1.2.0、1.1.0、1.0.0(1)	10.0
1.4.0、1.5.0(1)、1.6.0、1.7.0(1)	10.1
1.5.0(1)、1.6.0、1.7.0(1)、1.8.0(1)、1.9.0、1.10.0	10.2
1.7.0(1)	11.0
1.8.0(1)、1.9.0、1.10.0	11.1
1.8.0(1)、1.9.0、1.10.0	11.3

The above is the solution to the cause of this error. Welcome to leave a message in the comment area to discuss whether it can be solved.If it is useful, please like and collect the article. Thank you for your support. The blogger has the motivation to keep recording the problems encountered.！！！

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