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
...

当前回答

我使用的是Tensorflow 2.3.1版本,上面的解决方案都没有完全有效。 直到我找到这个包裹。

像这样安装:

水蟒,

python -m pip install silence-tensorflow

ide,

pip install silence-tensorflow

并在第一行代码中添加:

from silence_tensorflow import silence_tensorflow
silence_tensorflow()

就是这样!

其他回答

因为TF_CPP_MIN_LOG_LEVEL对我没用,你可以试试:

tf.logging.set_verbosity(tf.logging.WARN)

在tensorflow v1.6.0中为我工作过

我用这篇文章解决了无法删除所有警告#27045,解决方案是:

import logging
logging.getLogger('tensorflow').disabled = True

献给那些还在努力获得操作系统的人。environ解决方案的工作,检查这是放在你导入tensorflow在你的脚本,就像mwweb的答案:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # or any {'0', '1', '2'}
import tensorflow as tf

如果你只需要清除屏幕上的警告输出,你可能想在导入tensorflow后立即使用这个简单的命令清除控制台屏幕(根据我的经验,它比禁用所有调试日志更有效):

在windows中:

import os
os.system('cls')

在Linux或Mac中:

import os
os.system('clear')

我也遇到过这个问题(在tensorflow-0.10.0rc0上),但无法通过建议的答案修复过多的鼻子测试日志问题。

我设法通过直接探测张量流记录器来解决这个问题。不是最正确的修复,但工作很好,只污染直接或间接导入tensorflow的测试文件:

# Place this before directly or indirectly importing tensorflow
import logging
logging.getLogger("tensorflow").setLevel(logging.WARNING)