Hacktoberfest 2026: the issues maintainers tagged for October, open and beginner-friendly. Browse Hacktoberfest issues

tensorflow-model-optimization fails to import in official tensorflow:2.15.0 Docker image due to TF_USE_LEGACY_KERAS=1 and missing tf_keras

Open
#1,272 0 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
48/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Quiet
Tech stack
docker, python, tensorflow

Research direction

Reproduce the import failure in the official tensorflow/tensorflow:2.15.0 image using tensorflow-model-optimization==0.8.0 and the shown Python command. Inspect how TF_USE_LEGACY_KERAS=1 and the tf_keras dependency interact; done means tfmot imports successfully without upgrading TensorFlow beyond 2.15.

Written by the indexing model from the issue text.

Description

bug

When using the official TensorFlow Docker image tensorflow/tensorflow:2.15.0, the environment variable TF_USE_LEGACY_KERAS is preset to 1 (True). This causes tensorflow-model-optimization (tfmot) to fail with an ImportError because it expects tf_keras to be installed.

Attempting to install tf_keras leads to an automatic upgrade of TensorFlow to 2.21+, which breaks compatibility with tfmot (which is designed for TensorFlow 2.15).

  1. Pull the official image:
    docker pull tensorflow/tensorflow:2.15.0
    docker run -it tensorflow/tensorflow:2.15.0 bash
    pip install tensorflow-model-optimization==0.8.0
    python -c "import tensorflow_model_optimization as tfmot"
    

The import fails with:
ImportError: Keras cannot be imported. Check that it is installed.

Dominant language
Python
Stars
1.6k
Forks
349
Avg merge
3d 2h
Merged PRs (30d)
1

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from tensorflow/model-optimization

All issues in tensorflow/model-optimization

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.