gpu-review

name	gpu-review
description	Reviews deep learning code (.py files and Jupyter notebooks) to ensure GPU is used when available. Identifies anti-patterns like missing device detection, hardcoded CPU usage, tensors not on GPU, and performance issues. Supports PyTorch, TensorFlow/Keras, and JAX. Triggers: "review code for GPU usage", "check GPU utilization", "audit device placement", "ensure DL training runs on GPU".

GPU Code Review

Audits deep learning code for proper GPU utilization against framework-specific anti-patterns.

Inputs

• Target file(s) — .py or .ipynb
• Optional: severity scope (default: report Critical + Performance; runtime preflight uses Critical only)

References (load when relevant)

• kb/context/colab/gpu-review-patterns.md — anti-patterns by framework (PyTorch, TensorFlow/Keras, JAX, Colab notebook-specific)

Steps

1. Detect framework — scan imports for torch, tensorflow / keras, or jax.
2. Dispatch a subagent (reading-agent for read-only audit, or general-purpose if fixes are also expected) with the file path and the framework's section of kb/context/colab/gpu-review-patterns.md as reference. The subagent checks:
   • Device detection exists and uses dynamic selection (not hardcoded CPU).
   • Model is moved to the detected device.
   • All tensors / data are on the same device as the model before forward pass.
   • DataLoader / data pipeline is GPU-optimized (pin_memory, prefetch, etc.).
   • Eval / inference paths disable gradients.
   • Mixed precision is considered for training code.
   • Multi-GPU is considered when relevant.
   • For notebooks: an early cell verifies GPU availability (!nvidia-smi).
3. Receive findings as a structured list with severity (Critical / Performance), the problematic snippet, and a concrete fix.

Output

## GPU Review: <filename>

**Framework:** PyTorch | TensorFlow | JAX
**Device setup found:** Yes / No
**Overall:** Good / Issues found

### Findings

#### [Critical] <title>
- **Line(s):** ...
- **Issue:** ...
- **Fix:** ...

#### [Performance] <title>
- **Line(s):** ...
- **Issue:** ...
- **Fix:** ...

### Summary
- N critical issues, M performance suggestions
- <one-line overall recommendation>

Edge cases

• Code intentionally forces CPU (debugging, small models): note as informational, not Critical.
• No DL framework detected: report no GPU-relevant code found.
• Multi-framework files: audit each framework independently.

Notes for experiment runtime preflight

When invoked as part of an iterative experiment loop (experiment-runtime preflight), apply only Critical findings autonomously. Performance items can change training behavior (e.g. mixed precision, pin_memory) and are treated as operational downgrades per kb/context/experiments/experiment-policies.md.