Disable Tensorflow debugging information

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

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

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)

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

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

To anyone still struggling to get the os.environ solution to work as I was, check that this is placed before you import tensorflow in your script, just like mwweb's answer:

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

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

I am using Tensorflow version 2.3.1 and none of the solutions above have been fully effective.
Until, I find this package.

Install like this:

with Anaconda,

python -m pip install silence-tensorflow

with IDEs,

pip install silence-tensorflow

And add to the first line of code:

from silence_tensorflow import silence_tensorflow
silence_tensorflow()

That's It!

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

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

for tensorflow 2.1.0, following code works fine.

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

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)

I was struggling from this for a while, tried almost all the solutions here but could not get rid of debugging info in TF 1.14, I have tried following multiple solutions:

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

The debugging info still showed up, what finally helped was restarting my pc (actually restarting the kernel should work). So if somebody has similar problem, try restart kernel after you set your environment vars, simple but might not come in mind.

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')

None of the solutions above could solve my problem in Jupyter Notebook, so I use the following snippet code bellow from Cicoria, and issues solved.

import warnings  
with warnings.catch_warnings():  
    warnings.filterwarnings("ignore",category=FutureWarning)
    import tensorflow as tf
    from tensorflow import keras
    from tensorflow.keras.preprocessing.text import Tokenizer

print('Done')