Introduction

The error module 'tensorflow' has no attribute 'logging' usually means the code was written for TensorFlow 1.x but is running against a modern TensorFlow 2 installation. In TensorFlow 2, the old tf.logging API is no longer part of normal public usage.

The fix is not to hunt for a hidden replacement with the same name. In current code, you normally use Python's standard logging module or TensorFlow's tf.get_logger() integration instead.

Why the Attribute Error Happens

Older TensorFlow examples often used code like this:

1import tensorflow as tf
2
3tf.logging.set_verbosity(tf.logging.INFO)
4tf.logging.info("starting training")

That worked in the TensorFlow 1 era. Once TensorFlow moved to the 2.x API surface, tf.logging stopped being the standard public interface, so the attribute lookup fails.

This means the problem is usually one of version mismatch, not installation failure. Your environment is fine; your example is old.

Modern Replacement: tf.get_logger()

If the goal is to control TensorFlow-related log output inside a current TensorFlow program, use tf.get_logger():

1import logging
2import tensorflow as tf
3
4logger = tf.get_logger()
5logger.setLevel(logging.INFO)
6logger.info("TensorFlow version: %s", tf.__version__)

This gives you a normal Python logger that integrates with TensorFlow's logging behavior. It is the closest conceptual replacement for many basic tf.logging use cases.

For reducing TensorFlow noise, changing the logger level is often enough:

1import logging
2import tensorflow as tf
3
4tf.get_logger().setLevel(logging.ERROR)

That is much clearer than searching for removed TF1 helper methods.

Use Standard Python Logging for Application Code

Most of the time, the better design is to use Python's built-in logging module for your own application events and reserve TensorFlow's logger only for framework-specific output.

1import logging
2import tensorflow as tf
3
4logging.basicConfig(
5    level=logging.INFO,
6    format="%(levelname)s %(name)s %(message)s"
7)
8
9logging.info("Loading model with TensorFlow %s", tf.__version__)

This keeps application logging consistent across the codebase. If the rest of the project uses standard logging, your TensorFlow code should not invent a separate logging strategy unless there is a strong reason to do so.

Migration Cases and tf.compat.v1

If you are in the middle of migrating a TensorFlow 1 codebase, you may still see compatibility APIs under tf.compat.v1. Those can act as a temporary bridge while the rest of the code is modernized.

That said, a compatibility namespace is not the long-term answer. If a project still depends on tf.logging, it often also depends on other TensorFlow 1 ideas such as:

• sessions
• placeholders
• graph-based execution assumptions
• older estimator-era examples

When you see the logging error, it is worth checking whether the rest of the script also needs migration.

Logging Control Versus Native Runtime Noise

A common source of confusion is that not every TensorFlow message is coming from the same layer. Some output is routed through Python logging, while other messages come from lower-level native components.

So if you replace tf.logging and still see startup warnings or runtime messages, that does not mean the fix failed. It may simply mean you are dealing with a different logging path.

The important distinction is:

• 'AttributeError comes from using an obsolete Python API'
• extra startup noise may come from runtime configuration, not from the missing attribute

Solve those as separate problems.

A Minimal Before-and-After Example

Old style:

import tensorflow as tf

tf.logging.info("starting")

Modern equivalent:

1import logging
2import tensorflow as tf
3
4logger = tf.get_logger()
5logger.setLevel(logging.INFO)
6logger.info("starting")

Or, if the message is about your application rather than TensorFlow internals:

1import logging
2
3logging.basicConfig(level=logging.INFO)
4logging.info("starting")

Common Pitfalls

The most common mistake is assuming the error means TensorFlow was installed incorrectly. In most cases, the installation is fine and the code sample is simply from the wrong TensorFlow generation.

Another mistake is trying to keep tf.compat.v1 around forever. Compatibility shims are useful during migration, but they should not become the permanent design for new code.

Developers also sometimes mix framework logging and application logging without a plan. Use standard logging for your own business events and use tf.get_logger() when you specifically want to manage TensorFlow's logger.

Finally, do not assume every noisy TensorFlow message is controlled by the same logger. The missing tf.logging API and native runtime verbosity are related topics, but they are not the same problem.

Summary

• 'tf.logging is a TensorFlow 1-era API and is not the normal interface in modern TensorFlow.'
• Use tf.get_logger() to manage TensorFlow-related logging in current code.
• Use Python's standard logging module for general application logs.
• Treat tf.compat.v1 as a migration bridge, not as the preferred long-term approach.
• If this error appears, check the rest of the code for other TensorFlow 1 assumptions.