PyPI (Python Package Index) is the official package repository for the Python programming language, hosting over 500,000 packages downloaded billions of times monthly. As the default source for 'pip install' commands, PyPI is essential infrastructure for virtually every Python project — from data science and machine learning to web development and DevOps tooling. The repository is operated by the Python Software Foundation and relies on donated infrastructure from sponsors including AWS and Fastly. PyPI serves as the Python ecosystem's equivalent of npm for JavaScript.
PyPI outages commonly involve CDN distribution failures affecting package downloads, storage backend issues preventing new package uploads, and XML-RPC API deprecation-related disruptions as the platform modernizes. Malware scanning infrastructure can cause delays in package availability after upload. The JSON API that pip uses for dependency resolution occasionally experiences performance degradation under high concurrent access. Because PyPI relies on donated infrastructure, capacity constraints during unexpected traffic spikes can cause broader availability issues than commercial package registries.
When PyPI goes down, Python package installation fails globally, breaking CI/CD pipelines for Python projects across every industry. Data science workflows that pip install dependencies cannot proceed. Docker builds with Python requirements files fail at the installation step. Development environments cannot be set up or reproduced. The impact is especially severe in the data science and machine learning communities where complex dependency trees mean even simple projects pull dozens of packages from PyPI.
Use this page to check PyPI availability. If 'pip install' commands fail with network errors, PyPI may be experiencing an outage. Check status.python.org for official incident reports. PyPI outages can be regional due to CDN distribution, so the registry may work in some locations but not others.
pip install failures can result from PyPI outages, network connectivity issues, package version conflicts, or corrupted pip cache. Test with 'pip install --verbose package_name' for detailed error information. If the error mentions connection timeouts or HTTP 503 errors, it is likely a PyPI availability issue rather than a local problem.
Data science and ML projects depend heavily on packages like numpy, pandas, scikit-learn, and PyTorch from PyPI. An outage prevents setting up new environments, running Jupyter notebooks that install dependencies, and building Docker images for ML pipelines. Pre-built virtual environments and Docker image caching provide the best protection.
Use a cached pip download directory with 'pip install --no-index --find-links=/path/to/cached/packages'. Consider setting up a private PyPI mirror using devpi or Artifactory for critical builds. If you have existing virtual environments, they continue working. Docker images with pre-installed dependencies bypass PyPI entirely.
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