Upload LLaVA-Next-3D/checkcuda.py with huggingface_hub

LLaVA-Next-3D/checkcuda.py

@@ -0,0 +1,34 @@
+import torch
+
+def check_cuda():
+    # 检查是否有可用的 CUDA 设备
+    if torch.cuda.is_available():
+        print("CUDA is available!")
+        
+        # 获取可用的 CUDA 设备数量
+        num_devices = torch.cuda.device_count()
+        print(f"Number of CUDA devices available: {num_devices}")
+        
+        # 输出每个设备的信息
+        for i in range(num_devices):
+            device_name = torch.cuda.get_device_name(i)
+            memory_allocated = torch.cuda.memory_allocated(i)
+            memory_reserved = torch.cuda.memory_reserved(i)
+            print(f"\nDevice {i}: {device_name}")
+            print(f"  Memory Allocated: {memory_allocated / 1024**2:.2f} MB")
+            print(f"  Memory Reserved: {memory_reserved / 1024**2:.2f} MB")
+            print(f"  Max Memory Allocated: {torch.cuda.max_memory_allocated(i) / 1024**2:.2f} MB")
+            print(f"  Max Memory Reserved: {torch.cuda.max_memory_reserved(i) / 1024**2:.2f} MB")
+            
+        # 获取当前默认的 CUDA 设备
+        current_device = torch.cuda.current_device()
+        print(f"\nCurrently using CUDA device: {torch.cuda.get_device_name(current_device)}")
+        
+        # 获取当前设备的详细信息
+        print(f"  Memory Allocated (Current): {torch.cuda.memory_allocated(current_device) / 1024**2:.2f} MB")
+        print(f"  Memory Reserved (Current): {torch.cuda.memory_reserved(current_device) / 1024**2:.2f} MB")
+    else:
+        print("CUDA is not available.")
+
+# 调用函数检查 CUDA 状态
+check_cuda()