I Solved PyTorch's Cross-Platform Nightmare (svana.name)

A developer has tackled the long-standing challenge of distributing PyTorch-based Python packages that work seamlessly across different hardware accelerators and operating systems. The problem stems from PyTorch’s platform-specific wheels and the limitations of packaging tools, which often force users to manually configure custom package indices or dependencies during installation—an experience that complicates deployment, especially for projects intended for wide distribution like the upcoming AI coding assistant, FileChat. The solution leverages PEP 508’s support for direct wheel URLs combined with Python version-specific dependency constraints in the package’s pyproject.toml. By defining optional dependency groups for CPU, CUDA, and other accelerators, each linked to precise PyTorch wheels matching both hardware and Python version, the developer enables a simplified install command (e.g., pip install filechat[xpu]) tailored to the user’s environment. While this approach requires maintaining updated URLs for new PyTorch versions or Python releases, it eliminates the need for users to manage complex installation setups or multiple package indices, streamlining cross-platform installs. This method represents a practical, elegant workaround to PyTorch’s distribution complexity, offering the AI/ML community a reproducible way to package accelerator-dependent projects without sacrificing usability. It also highlights current gaps in Python packaging infrastructure and points to emerging solutions like Astral’s PYX registry, which may further simplify such workflows in the future.