Best PyTorch Packages

PatternStack analyzes 2+ real PyTorch projects on GitHub to determine which packages developers actually use in production. Unlike download counts or star counts alone, our rankings reflect co-occurrence patterns — which packages are used together in successful projects.

Each package is scored by adoption rate (how many PyTorch projects include it), confidence (how consistently it appears with other popular packages), and health (whether it has known security vulnerabilities or is deprecated).

Packages with unpatched CVEs are automatically excluded from rankings. Data refreshes continuously as new projects are synced, so you always see what developers are choosing now, not what was popular years ago.

The top PyTorch packages include nvidia-ml-py (81% adoption), pytest-xdist (81%), and pytest (79%). These form the core of most production PyTorch stacks.

When starting a new PyTorch project, the package choices you make early on shape your entire architecture. PatternStack helps you make informed decisions by showing what combinations work well together, based on real project data.

Instead of relying on blog posts or opinions, you can see exactly what percentage ofPyTorch projects use each package and what they pair it with. This is especially valuable for choosing between alternatives — like deciding between ORMs, authentication libraries, or testing frameworks.