Disable Tensorflow debugging information Disable Tensorflow debugging information python python

Disable Tensorflow debugging information


You can disable all debugging logs using os.environ :

import osos.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 printed2 = INFO and WARNING messages are not printed3 = 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 tftf.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 variabletf.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 osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # or any {'0', '1', '2'}import tensorflow as tf

You can set this environmental variable in the environment that you run your script in. For example, with bash this can be in the file ~/.bashrc, /etc/environment, /etc/profile, or in the actual shell as:

TF_CPP_MIN_LOG_LEVEL=2 python my_tf_script.py

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 abovetf.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 abovetf.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.


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

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


matomo