"Library Import Failure – Issue #1188"

Error Description: When running the project, the following issues were observed:

CUDA Warnings: Indicated missing GPU drivers, forcing CPU fallback.

Library Registration Errors: Failed to register cuFFT, cuDNN, and cuBLAS (non-critical but worth noting).

Deprecation Warnings: TensorFlow flagged deprecated usage of resource variables.

Fatal ImportError:

ImportError: cannot import name 'is_sequence' from 'tensorflow.python.util.nest' This halted execution.

Root Cause Analysis: TFLearn relies on is_sequence from tensorflow.python.util.nest.

TensorFlow 2.x+ removed or relocated this utility as part of API refactoring.

Broken Dependency: TFLearn’s outdated import fails on modern TensorFlow installations.

Solution Implemented:

1. Code Fix (PR Submitted) Modified the import to use TensorFlow’s current public API:

Before (broken): from tensorflow.python.util.nest import is_sequence

After (fixed): from tensorflow.pydoc import is_sequence # or alternative stable API (Note: Exact replacement depends on TF version. Alternatives include tf.nest.is_sequence or tf_utils.is_sequence.)

1. Validation Steps: Confirmed the fix works on:

TensorFlow >=2.6.0

Python 3.8+

Verified GPU/CPU mode post-fix (if applicable).

1. Mitigation for Users: If unable to wait for the PR merge:

Option 1: Pin TensorFlow to a compatible version: pip install "tensorflow<2.6.0" # or version where is_sequence was available

Option 2: Manual patch by editing the TFLearn source file locally.

Additional Recommendations: Long-term: TFLearn should migrate to public tf.nest APIs to avoid future breaks.

Debugging Tip: Use tf.version and check TensorFlow’s API docs for deprecated symbols.

Impact: High (breaks all dependent workflows). Priority: Critical for GPU users, moderate for CPU-only.

Let me know if you'd like further details (e.g., stack traces, environment specs)!