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}
For Prior Versions of TensorFlow or TF-Learn Logging (v0.11.x or lower):
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.