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
...
是的，我正在使用tf 2.0 beta，并想启用/禁用默认日志记录。tf1中的环境变量和方法。X似乎不再存在了。
我在PDB中发现了这个方法:
# close the TF2 logger
tf2logger = tf.get_logger()
tf2logger.error('Close TF2 logger handlers')
tf2logger.root.removeHandler(tf2logger.root.handlers[0])
然后添加自己的日志记录器API(在本例中是基于文件的)
logtf = logging.getLogger('DST')
logtf.setLevel(logging.DEBUG)
# file handler
logfile='/tmp/tf_s.log'
fh = logging.FileHandler(logfile)
fh.setFormatter( logging.Formatter('fh %(asctime)s %(name)s %(filename)s:%(lineno)d :%(message)s') )
logtf.addHandler(fh)
logtf.info('writing to %s', logfile)
是的，我正在使用tf 2.0 beta，并想启用/禁用默认日志记录。tf1中的环境变量和方法。X似乎不再存在了。
我在PDB中发现了这个方法:
# close the TF2 logger
tf2logger = tf.get_logger()
tf2logger.error('Close TF2 logger handlers')
tf2logger.root.removeHandler(tf2logger.root.handlers[0])
然后添加自己的日志记录器API(在本例中是基于文件的)
logtf = logging.getLogger('DST')
logtf.setLevel(logging.DEBUG)
# file handler
logfile='/tmp/tf_s.log'
fh = logging.FileHandler(logfile)
fh.setFormatter( logging.Formatter('fh %(asctime)s %(name)s %(filename)s:%(lineno)d :%(message)s') )
logtf.addHandler(fh)
logtf.info('writing to %s', logfile)
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。
