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Streamline CUDA-Accelerated Python Install and Packaging Workflows with Wheel Variants

If you’ve ever installed an NVIDIA GPU-accelerated Python package, you’ve likely encountered a familiar dance: navigating to pytorch.org, jax.dev,…

If you’ve ever installed an NVIDIA GPU-accelerated Python package, you’ve likely encountered a familiar dance: navigating to pytorch.org, jax.dev, rapids.ai, or a similar site to find the artifact built for your NVIDIA CUDA version. You then copy a custom pip, uv, or other installer command with a special index URL or special package name such as . This isn’t just an inconvenience…

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