Upload entrypoint_setup.py with huggingface_hub

entrypoint_setup.py

@@ -1,22 +1,22 @@
-import torch
-import torch._inductor.config as inductor_config
-import torch._dynamo as dynamo
-
-# Enable TensorFloat32 tensor cores for float32 matmul (Ampere+ GPUs)
-# Provides significant speedup with minimal precision loss
-torch.set_float32_matmul_precision('high')
-
-# Enable TF32 for matrix multiplications and cuDNN operations
-torch.backends.cuda.matmul.allow_tf32 = True
-torch.backends.cudnn.allow_tf32 = True
-
-# Enable cuDNN autotuner - finds fastest algorithms for your hardware
-# Best when input sizes are consistent; may slow down first iterations
-torch.backends.cudnn.benchmark = True
-
-# Deterministic operations off for speed (set True if reproducibility needed)
-torch.backends.cudnn.deterministic = False
-inductor_config.max_autotune_gemm_backends = "ATEN,CUTLASS,FBGEMM"    
-
-dynamo.config.capture_scalar_outputs = True
-torch._dynamo.config.recompile_limit = 16
+import torch
+import torch._inductor.config as inductor_config
+import torch._dynamo as dynamo
+
+# Enable TensorFloat32 tensor cores for float32 matmul (Ampere+ GPUs)
+# Provides significant speedup with minimal precision loss
+torch.set_float32_matmul_precision('high')
+
+# Enable TF32 for matrix multiplications and cuDNN operations
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+
+# Enable cuDNN autotuner - finds fastest algorithms for your hardware
+# Best when input sizes are consistent; may slow down first iterations
+torch.backends.cudnn.benchmark = True
+
+# Deterministic operations off for speed (set True if reproducibility needed)
+torch.backends.cudnn.deterministic = False
+inductor_config.max_autotune_gemm_backends = "ATEN,CUTLASS,FBGEMM"    
+
+dynamo.config.capture_scalar_outputs = True
+torch._dynamo.config.recompile_limit = 16