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Fix multi-GPU detection, VRAM leak on disable, and orphaned GPU array handling

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_gpu_candidates was never populated (typo: _candidate_gpus), breaking
num_gpus()/available_gpu_ids() and the multi-GPU round-robin. available_gpu_ids
also indexed tuples with dict syntax (TypeError). disable_gpu() now frees the
memory pool before clearing refs (was leaking VRAM). to_cpu() and safe_gpu_call()
survive disable_gpu() via a persistent _cupy_ndarray type reference so orphaned
GPU arrays are recovered to CPU. log_gpu_status() now uses Device(0) instead of
the host index (CuPy renumbers to 0 after CUDA_VISIBLE_DEVICES).
This commit is contained in:
Antoine Jacquin
2026-07-23 20:06:14 +02:00
parent 4dafae6d02
commit c58ca3f477
1 changed files with 38 additions and 16 deletions
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54
lidar_pipeline/gpu.py
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@ -28,7 +28,7 @@ _gpu_reason = None
_restricted_gpu_ids: list[int] | None = None
# Discovered GPU candidates (populated by _pick_gpu)
_gpu_candidates: list[dict] = []
_gpu_candidates: list = []
def _pick_gpu() -> list:
@ -66,9 +66,8 @@ def _pick_gpu() -> list:
return []
_candidate_gpus: list = []
try:
_candidate_gpus = _pick_gpu() or []
_gpu_candidates = _pick_gpu() or []
except Exception:
pass
@ -78,6 +77,7 @@ except Exception:
_xp = np
_cp = None
_cp_ndimage = None
_cupy_ndarray = None # type ref qui survit à disable_gpu() pour que to_cpu() récupère les tableaux orphelins
_gpu_initialized = False
@ -112,12 +112,12 @@ def _init_gpu():
import CuPy directly (no subprocess test needed)
3. Otherwise: test each GPU in a subprocess, pick the first that works
"""
global _xp, _cp, _cp_ndimage, _gpu_initialized, HAS_GPU, _best_gpu_id, _gpu_name, _gpu_mem_gb
global _xp, _cp, _cp_ndimage, _cupy_ndarray, _gpu_initialized, HAS_GPU, _best_gpu_id, _gpu_name, _gpu_mem_gb
if _gpu_initialized:
return
_gpu_initialized = True
candidates = _filter_candidates(_candidate_gpus)
candidates = _filter_candidates(_gpu_candidates)
if not candidates:
logger.info("Pas de GPU utilisable — mode CPU uniquement")
_xp = np
@ -149,6 +149,7 @@ def _init_gpu():
_xp = _real_cupy
_cp = _real_cupy
_cp_ndimage = _real_cupy_ndimage
_cupy_ndarray = _real_cupy.ndarray
return
except Exception as e:
logger.warning(f"GPU indisponible (CUDA_VISIBLE_DEVICES={cuda_visible}): {e}")
@ -203,6 +204,7 @@ def _init_gpu():
_xp = _real_cupy
_cp = _real_cupy
_cp_ndimage = _real_cupy_ndimage
_cupy_ndarray = _real_cupy.ndarray
# ---------------------------------------------------------------------------
@ -211,7 +213,7 @@ def _init_gpu():
def num_gpus():
"""Return the number of available GPUs (after restrict_gpus filtering)."""
return len(_gpu_candidates)
return len(_filter_candidates(_gpu_candidates))
def available_gpu_ids():
@ -219,16 +221,20 @@ def available_gpu_ids():
Respects any prior restrict_gpus() call.
"""
return [c['id'] for c in _gpu_candidates]
return [c[0] for c in _filter_candidates(_gpu_candidates)]
def restrict_gpus(gpu_ids: list[int], set_env_var: bool = False):
"""Restrict GPU selection to specific host-level indices.
Stores the restriction to be applied during _init_gpu().
If set_env_var is True, also sets CUDA_VISIBLE_DEVICES immediately so
child processes inherit the restriction.
"""
global _restricted_gpu_ids
_restricted_gpu_ids = gpu_ids
if set_env_var and gpu_ids:
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(i) for i in gpu_ids)
def set_active_gpu(gpu_id):
@ -250,11 +256,12 @@ def log_gpu_status():
"""Log GPU detection result. Called after logging is configured."""
if _gpu_available():
try:
with _cp.cuda.Device(_best_gpu_id):
name = _cp.cuda.runtime.getDeviceProperties(_best_gpu_id)['name']
with _cp.cuda.Device(0):
props = _cp.cuda.runtime.getDeviceProperties(0)
name = props['name']
if isinstance(name, bytes):
name = name.decode()
mem_gb = _cp.cuda.runtime.getDeviceProperties(_best_gpu_id)['totalGlobalMem'] // (1024**3)
mem_gb = props['totalGlobalMem'] // (1024**3)
gpu_info = f"GPU: {name} ({mem_gb} Go VRAM) — ID {_best_gpu_id}"
except Exception:
gpu_info = f"GPU: {_gpu_name} ({_gpu_mem_gb} Go VRAM)"
@ -297,12 +304,20 @@ def to_gpu(arr):
def to_cpu(arr):
"""Bring array back to CPU (numpy). No-op if already on CPU."""
if _cp is not None and isinstance(arr, _cp.ndarray):
"""Bring array back to CPU (numpy). No-op if already on CPU.
Fonctionne même après disable_gpu() grâce à la réf de type _cupy_ndarray.
"""
if _cupy_ndarray is not None and isinstance(arr, _cupy_ndarray):
try:
return _cp.asnumpy(arr)
if _cp is not None:
return _cp.asnumpy(arr)
return arr.get()
except Exception:
pass
try:
return np.asarray(arr)
except Exception:
pass
return arr
@ -360,11 +375,16 @@ def gpu_cleanup():
def disable_gpu():
"""Disable GPU acceleration for the rest of this process."""
"""Disable GPU acceleration for the rest of this process.
Libère le memory pool GPU avant de nuller les références (anti-fuite VRAM).
Garde _cupy_ndarray vivant pour que to_cpu() récupère les tableaux orphelins.
"""
global HAS_GPU, _xp, _cp, _cp_ndimage
if not HAS_GPU:
return
logger.warning("GPU désactivé — passage en mode CPU pour la suite du processus")
gpu_cleanup()
HAS_GPU = False
_xp = np
_cp = None
@ -385,5 +405,7 @@ def safe_gpu_call(func, *args, **kwargs):
if _cp is not None and ('CUDA' in err_msg or 'cuda' in err_msg or 'GPU' in err_msg or 'Out of memory' in err_msg):
logger.warning(f"Erreur GPU ({e.__class__.__name__}), retry en CPU...")
disable_gpu()
return func(*args, **kwargs)
cpu_args = tuple(to_cpu(a) for a in args)
cpu_kwargs = {k: to_cpu(v) for k, v in kwargs.items()}
return func(*cpu_args, **cpu_kwargs)
raise