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fn_registry/python/functions/ml/gpu_info.md
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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-13 00:50:34 +02:00

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Markdown

---
name: gpu_info
kind: function
lang: py
domain: ml
version: "1.0.0"
purity: impure
signature: "def gpu_info() -> list[dict]"
description: "Consulta nvidia-smi para obtener informacion de cada GPU NVIDIA: nombre, VRAM total y libre, version de driver y CUDA. Devuelve lista vacia si nvidia-smi no esta disponible, sin lanzar excepcion."
tags: [gpu, nvidia, cuda, vram, hardware, probe, ml, nvidia-smi]
uses_functions: []
uses_types: []
returns: []
returns_optional: false
error_type: "error_go_core"
imports: []
params: []
output: "lista de dicts por GPU con claves: index (int), name (str), vram_total_mb (int), vram_free_mb (int), driver_version (str), cuda_version (str). Lista vacia si nvidia-smi no esta disponible."
tested: true
tests:
- "sin nvidia-smi devuelve lista vacia"
- "formato CSV correcto devuelve lista con un dict por GPU"
- "fila malformada en CSV se ignora sin excepcion"
test_file_path: "python/functions/ml/tests/test_gpu_info.py"
file_path: "python/functions/ml/gpu_info.py"
---
## Ejemplo
```python
from ml.gpu_info import gpu_info
gpus = gpu_info()
# Sin nvidia-smi: []
# Con una GPU:
# [
# {
# "index": 0,
# "name": "NVIDIA GeForce RTX 4090",
# "vram_total_mb": 24564,
# "vram_free_mb": 22000,
# "driver_version": "535.183.01",
# "cuda_version": "8.9"
# }
# ]
for gpu in gpus:
pct = 100 * (1 - gpu["vram_free_mb"] / gpu["vram_total_mb"])
print(f"GPU {gpu['index']}: {gpu['name']} — VRAM {pct:.1f}% usada")
```
## Notas
- Usa `--query-gpu=compute_cap` como aproximacion de la version CUDA soportada. El campo `cuda_version` del output es la compute capability (ej. "8.9"), no la version CUDA del driver.
- Robusto a `FileNotFoundError` (nvidia-smi no instalado), `TimeoutExpired` (driver colgado), y `OSError`.
- Para datos de torch (no nvidia-smi), usar `cuda_available`.
- impure: consulta hardware y estado del sistema en tiempo de ejecucion.