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pypipkg:pypi/cupy-cuda101@9.6.x
tile.json

tessl/pypi-cupy-cuda101

tessl install tessl/pypi-cupy-cuda101@9.6.0

CuPy: NumPy & SciPy for GPU (CUDA 10.1 version)

Agent Success

Agent success rate when using this tile

87%

Improvement

Agent success rate improvement when using this tile compared to baseline

1.19x

Baseline

Agent success rate without this tile

73%

rubric.jsonevals/scenario-6/

{
  "context": "This evaluation assesses how well the engineer uses CuPy's GPU-accelerated sorting capabilities, specifically lexicographic sorting with lexsort(), to implement a multi-key student ranking system. The focus is on correct usage of CuPy's sorting API and efficient GPU array operations.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Uses lexsort function",
      "description": "Uses cupy.lexsort() or cp.lexsort() to perform the multi-key lexicographic sorting operation",
      "max_score": 40
    },
    {
      "name": "Correct key ordering",
      "description": "Correctly constructs the keys tuple for lexsort() with keys in reverse priority order (lexsort sorts by the last key first), accounting for the priority_order parameter",
      "max_score": 25
    },
    {
      "name": "Descending sort handling",
      "description": "Implements descending order sorting correctly, either by negating scores before sorting or by reversing the result indices",
      "max_score": 15
    },
    {
      "name": "Array indexing for result",
      "description": "Uses the indices returned from lexsort() to correctly index student_ids and return the ranked list (e.g., student_ids[indices])",
      "max_score": 10
    },
    {
      "name": "GPU array operations",
      "description": "Uses CuPy arrays and GPU operations throughout the solution rather than falling back to NumPy or CPU-based operations unnecessarily",
      "max_score": 10
    }
  ]
}