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Implement DeepSeek-R1 Code Analysis and Refactoring

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: PyTorch Optimization Agent

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Linked challenge
PyTorch Optimization Agent

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Original prompt text with formatting preserved for inspection.

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Using DeepSeek-R1, implement a core agent capability to analyze a provided PyTorch training loop and identify at least two common performance bottlenecks (e.g., inefficient data loading, CPU-GPU transfer issues, sub-optimal tensor operations). The agent should then propose concrete code changes to address these bottlenecks, providing a clear explanation for each. Focus on leveraging DeepSeek-R1's strengths in code understanding.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.