Fixing AttributeError: module 'numpy' has no attribute 'bool'

Problem Statement

When attempting to import libraries like gluoncv or mxnet with NumPy 1.24.0 or newer, you may encounter this error:

AttributeError: module 'numpy' has no attribute 'bool'

The error stems from major changes in NumPy's type aliases and typically occurs in environments with:

• Python 3.7+
• NumPy ≥ 1.24.x
• Libraries that use deprecated NumPy aliases (like MXNet/GluonCV in this case)
• Conda environments (but also affects pip installations)

Why This Happens

NumPy deprecated aliases for built-in Python types (like np.bool, np.int, etc.) in version 1.20. These aliases were completely removed in NumPy 1.24.0. Libraries still using np.bool syntax will fail with this error since the attribute no longer exists.

WARNING

The error originates in dependent libraries (like MXNet) that haven't updated their code to reflect NumPy's changes—not your own code.

Recommended Solutions

1. Downgrade NumPy (Most Reliable Fix)

This is the safest solution for most users until library maintainers update their code.

# Uninstall current numpy
pip uninstall numpy -y

# Install compatible version
pip install numpy==1.23.5

Why this works:
NumPy 1.23.x retains the deprecated np.bool alias for compatibility, avoiding the attribute error while maintaining NumPy functionality.

TIP

Check your dependent library requirements first. If you're using MXNet/GluonCV, these versions work best:

• mxnet-mkl==1.6.0
• gluoncv==0.10.5.post0

2. Runtime Patch (Temporary Workaround)

Add early in your main Python script:

import numpy as np
np.bool = bool  # or np.bool = np.bool_

Pros:

• Quick fix without environment changes
• Useful for testing

Cons:

• Dirty patch that may hide future issues
• Doesn't fix the root cause
• Might break other type-related operations

3. Modify Library Source Code (Advanced)

For cases where downgrading isn't possible:

1. Locate the problematic file
   (e.g., .../mxnet/numpy/utils.py from the error traceback)
2. Replace np.bool with bool or np.bool_:

   # Original (broken)
   bool = onp.bool
   
   # Fixed version
   bool = bool  # or np.bool_

3. Save changes and restart your environment

Warning

Editing library source code is error-prone:

• Changes break on package updates
• May violate library compatibility
• Only recommended as a last resort

Why Avoid Dirty Fixes?

• ⚠️ Version Conflicts: Runtime patches (np.bool=bool) often cause issues in production environments
• ⚠️ Future Failures: NumPy may remove more aliases in future updates
• ⚠️ Project Scalability: Source code modifications aren't maintainable in team settings

Long-Term Solution

Track these GitHub issues for permanent fixes:

• MXNet #20597
• NumPy 1.24 Release Notes

Best Practice

For new projects:

1. Verify library compatibility before choosing versions
2. Use a virtual environment to isolate dependencies
3. Pin key library versions in requirements.txt:

   numpy>=1.23,<1.24
   gluoncv==0.10.5

Verifying Your Fix

After applying a solution:

import numpy as np
try:
    print(np.bool)  # Shouldn't throw error if fixed
    from gluoncv import data  # Test import
    print("Success!")
except AttributeError:
    print("Still broken")