Fixing "SystemError: initialization of _internal failed without raising an exception"

This error typically occurs when Python packages conflict during initialization, commonly affecting libraries like numpy, numba, and packages depending on them (e.g., Top2Vec, scikit-image). Based on community insights, here are effective solutions:

🔄 1. Restart Kernel or Environment (Quick Check)

Sometimes temporary state conflicts cause this error:

# If using Jupyter/IPython:
from IPython import get_ipython
get_ipython().magic('reset -f')  # Reset environment

# Then reimport:
from top2vec import Top2Vec

⬇️ 2. Downgrade numpy (If Using v1.24+)

numpy==1.24.0+ often triggers incompatibility:

pip uninstall -y numpy
pip install numpy==1.23.5

⬆️ 3. Update numba

Outdated numba versions frequently cause this:

pip install -U numba

If you’ve installed libraries like umap or scikit-image, reinstall numba afterward to avoid conflicts:

pip install numba==0.56.4  # Explicit version if needed

🐍 4. Upgrade Python Version (If Stuck Below 3.10)

Older Python versions (≤3.9) struggle with dependencies:

# Use pyenv or conda to upgrade:
conda create -n py310 python=3.10
conda activate py310
pip install top2vec numpy numba

🔍 5. Resolve Dependency Conflicts

Isolate conflicting packages using:

pip check  # Identify broken dependencies
# Example: If umap/scikit-image conflicts with numba:
pip uninstall umap scikit-image
pip install numba==0.56.4
pip install umap-learn scikit-image

💡 Prevention Tips

• Use virtual environments (venv/conda)
• Freeze working dependency versions:

  pip freeze > requirements.txt

• Test packages incrementally before complex imports

Note on Environment

Cloud environments (AWS/Azure) often require a full Python restart after package changes due to persistent kernel states.

Following these steps typically resolves the _internal initialization error. Most frequently, downgrading numpy, updating numba, or upgrading Python addresses the core conflict.