gpu-kernel-output-contract

Package GPU kernel implementations by standardizing the final code into a self-contained generated_kernel.py module. This process strips debug code and prose, enforcing strict class structures and import constraints for automated performance evaluation.

Remove debugging artifacts from Triton kernels by using automated packaging to strip benchmarks and debug code. This standardization produces a clean, production-ready Python file that adheres to required class structures and avoids forbidden external dependencies.

A GPU kernel output contract standardizes optimized kernel implementations into a compliant format for hidden evaluation environments. You need it after completing performance optimization loops to ensure your CUDA or Triton code meets strict interface and dependency constraints.

Yes, the GPU kernel packaging process supports both CUDA and Triton implementations. It standardizes the final optimized code from either framework into the exact format required for submission to high-performance computing performance leaderboards.

GPU kernels fail automated evaluation when they contain forbidden external dependencies, debugging artifacts, or incorrect class signatures. Packaging the implementation into a compliant, self-contained Python module ensures it meets the strict structural requirements of the hidden evaluator.

No, you cannot include external dependencies in the generated kernel file. The packaging process enforces import constraints by validating that the implementation avoids forbidden dependencies, ensuring the final Python module is entirely self-contained for automated evaluation.