Unset CUBLAS_WORKSPACE_CONFIG for non-deterministic training and inference (#19138)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/engine/model.py

@@ -140,6 +140,7 @@ class Model(torch.nn.Module):
             return
 
         # Load or create new YOLO model
+        __import__("os").environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"  # to avoid deterministic warnings
         if Path(model).suffix in {".yaml", ".yml"}:
             self._new(model, task=task, verbose=verbose)
         else:

ultralytics/engine/trainer.py

@@ -52,6 +52,7 @@ from ultralytics.utils.torch_utils import (
     select_device,
     strip_optimizer,
     torch_distributed_zero_first,
+    unset_deterministic,
 )
 
 
@@ -471,6 +472,7 @@ class BaseTrainer:
                 self.plot_metrics()
             self.run_callbacks("on_train_end")
         self._clear_memory()
+        unset_deterministic()
         self.run_callbacks("teardown")
 
     def auto_batch(self, max_num_obj=0):

ultralytics/utils/__init__.py

@@ -128,7 +128,6 @@ torch.set_printoptions(linewidth=320, precision=4, profile="default")
 np.set_printoptions(linewidth=320, formatter={"float_kind": "{:11.5g}".format})  # format short g, %precision=5
 cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
 os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS)  # NumExpr max threads
-os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"  # for deterministic training to avoid CUDA warning
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"  # suppress verbose TF compiler warnings in Colab
 os.environ["TORCH_CPP_LOG_LEVEL"] = "ERROR"  # suppress "NNPACK.cpp could not initialize NNPACK" warnings
 os.environ["KINETO_LOG_LEVEL"] = "5"  # suppress verbose PyTorch profiler output when computing FLOPs

ultralytics/utils/torch_utils.py

@@ -488,8 +488,15 @@ def init_seeds(seed=0, deterministic=False):
         else:
             LOGGER.warning("WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.")
     else:
-        torch.use_deterministic_algorithms(False)
-        torch.backends.cudnn.deterministic = False
+        unset_deterministic()
+
+
+def unset_deterministic():
+    """Unsets all the configurations applied for deterministic training."""
+    torch.use_deterministic_algorithms(False)
+    torch.backends.cudnn.deterministic = False
+    os.environ.pop("CUBLAS_WORKSPACE_CONFIG", None)
+    os.environ.pop("PYTHONHASHSEED", None)
 
 
 class ModelEMA: