通过调试信息，我指的是TensorFlow在我的终端中显示的关于加载的库和找到的设备等的信息，而不是Python错误。
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: Graphics Device
major: 5 minor: 2 memoryClockRate (GHz) 1.0885
pciBusID 0000:04:00.0
Total memory: 12.00GiB
Free memory: 11.83GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0: Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:717] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Graphics Device, pci bus id: 0000:04:00.0)
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.0KiB
...
我为此挣扎了一段时间，尝试了这里几乎所有的解决方案，但无法摆脱TF 1.14中的调试信息，我尝试了以下多个解决方案:
import os
import logging
import sys
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # FATAL
stderr = sys.stderr
sys.stderr = open(os.devnull, 'w')
import tensorflow as tf
tf.get_logger().setLevel(tf.compat.v1.logging.FATAL)
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
logging.getLogger('tensorflow').setLevel(tf.compat.v1.logging.FATAL)
sys.stderr = stderr
import absl.logging
logging.root.removeHandler(absl.logging._absl_handler)
absl.logging._warn_preinit_stderr = False
调试信息仍然显示，最终帮助我重新启动我的电脑(实际上重新启动内核应该工作)。所以如果有人有类似的问题，试着在你设置环境变量后重新启动内核，简单但可能不会想到。
在测试了各种建议之后，他们也可以沉默使用PyInstaller构建的可执行文件，我想出了这样的设置:
import logging
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
logging.getLogger('tensorflow').setLevel(logging.ERROR)
import tensorflow as tf
这条线
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
将关闭重建TensorFlow的警告:
我tensorflow / /平台/ cpu_feature_guard核心。这个TensorFlow二进制文件是用onapi深度神经网络库(oneDNN)优化的，可以在性能关键操作中使用以下CPU指令:AVX2 FMA。
要在其他操作中启用它们，请使用适当的编译器标志重新构建TensorFlow。
这条线
logging.getLogger('tensorflow').setLevel(logging.ERROR)
将取消对AutoGraph的警告:
警告:tensorflow:AutoGraph在此环境中不可用:函数缺少代码信息。这是一些典型的环境，如交互式Python shell。更多信息请参见https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/limitations.md#access-to-source-code。
关键是在导入tensorflow之前放置这两个文件——尽管Pylint警告过!
tensorflow 2.11.0
