禁用Tensorflow调试信息

By debugging information I mean what TensorFlow shows in my terminal about loaded libraries and found devices etc. not Python errors.

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
...

You can disable all debugging logs using os.environ :

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 
import tensorflow as tf

Tested on tf 0.12 and 1.0

In details,

0 = all messages are logged (default behavior)
1 = INFO messages are not printed
2 = INFO and WARNING messages are not printed
3 = INFO, WARNING, and ERROR messages are not printed

2.0 Update (10/8/19) Setting TF_CPP_MIN_LOG_LEVEL should still work (see below in v0.12+ update), but there is currently an issue open (see issue #31870). If setting TF_CPP_MIN_LOG_LEVEL does not work for you (again, see below), try doing the following to set the log level:

import tensorflow as tf
tf.get_logger().setLevel('INFO')

In addition, please see the documentation on tf.autograph.set_verbosity which sets the verbosity of autograph log messages – for example:

# Can also be set using the AUTOGRAPH_VERBOSITY environment variable
tf.autograph.set_verbosity(1)

v0.12+ Update (5/20/17), Working through TF 2.0+:

In TensorFlow 0.12+, per this issue, you can now control logging via the environmental variable called TF_CPP_MIN_LOG_LEVEL; it defaults to 0 (all logs shown) but can be set to one of the following values under the Level column.

  Level | Level for Humans | Level Description                  
 -------|------------------|------------------------------------ 
  0     | DEBUG            | [Default] Print all messages       
  1     | INFO             | Filter out INFO messages           
  2     | WARNING          | Filter out INFO & WARNING messages 
  3     | ERROR            | Filter out all messages      

See the following generic OS example using Python:

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

To be thorough, you call also set the level for the Python tf_logging module, which is used in e.g. summary ops, tensorboard, various estimators, etc.

# append to lines above
tf.logging.set_verbosity(tf.logging.ERROR)  # or any {DEBUG, INFO, WARN, ERROR, FATAL}

For 1.14 you will receive warnings if you do not change to use the v1 API as follows:

# append to lines above
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)  # or any {DEBUG, INFO, WARN, ERROR, FATAL}

View the page below for information on TensorFlow logging; with the new update, you’re able to set the logging verbosity to either DEBUG, INFO, WARN, ERROR, or FATAL. For example:

tf.logging.set_verbosity(tf.logging.ERROR)

The page additionally goes over monitors which can be used with TF-Learn models. Here is the page.

This doesn’t block all logging, though (only TF-Learn). I have two solutions; one is a ‘technically correct’ solution (Linux) and the other involves rebuilding TensorFlow.

script -c 'python [FILENAME].py' | grep -v 'I tensorflow/'

For the other, please see this answer which involves modifying source and rebuilding TensorFlow.

I have had this problem as well (on tensorflow-0.10.0rc0), but could not fix the excessive nose tests logging problem via the suggested answers.

I managed to solve this by probing directly into the tensorflow logger. Not the most correct of fixes, but works great and only pollutes the test files which directly or indirectly import tensorflow:

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

For compatibility with Tensorflow 2.0, you can use tf.get_logger

import logging
tf.get_logger().setLevel(logging.ERROR)

As TF_CPP_MIN_LOG_LEVEL didn’t work for me you can try:

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

Worked for me in tensorflow v1.6.0

Usual python3 log manager works for me with tensorflow==1.11.0:

import logging
logging.getLogger('tensorflow').setLevel(logging.INFO)

I solved with this post Cannot remove all warnings #27045 , and the solution was:

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

To add some flexibility here, you can achieve more fine-grained control over the level of logging by writing a function that filters out messages however you like:

logging.getLogger('tensorflow').addFilter(my_filter_func)

where my_filter_func accepts a LogRecord object as input [LogRecord docs] and returns zero if you want the message thrown out; nonzero otherwise.

Here’s an example filter that only keeps every nth info message (Python 3 due to the use of nonlocal here):

def keep_every_nth_info(n):
    i = -1
    def filter_record(record):
        nonlocal i
        i += 1
        return int(record.levelname != 'INFO' or i % n == 0)
    return filter_record

# Example usage for TensorFlow:
logging.getLogger('tensorflow').addFilter(keep_every_nth_info(5))

All of the above has assumed that TensorFlow has set up its logging state already. You can ensure this without side effects by calling tf.logging.get_verbosity() before adding a filter.

Yeah, I’m using tf 2.0-beta and want to enable/disable the default logging. The environment variable and methods in tf1.X don’t seem to exist anymore.

I stepped around in PDB and found this to work:

# close the TF2 logger
tf2logger = tf.get_logger()
tf2logger.error('Close TF2 logger handlers')
tf2logger.root.removeHandler(tf2logger.root.handlers[0])

I then add my own logger API (in this case file-based)

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)

for tensorflow 2.1.0, following code works fine.

import tensorflow as tf
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)

If you only need to get rid of warning outputs on the screen, you might want to clear the console screen right after importing the tensorflow by using this simple command (Its more effective than disabling all debugging logs in my experience):

In windows:

import os
os.system('cls')

In Linux or Mac:

import os
os.system('clear')