Update GPU_test.py

GPU_test.py

@@ -1,36 +1,20 @@
 import paddle
 
-# Check if GPU is available
-if paddle.is_compiled_with_cuda():
-    # Define the device configuration
-    paddle.set_device('gpu')
-    print("CUDA is available. Using GPU for computations.")
-else:
-    paddle.set_device('cpu')
-    print("CUDA is not available. Using CPU for computations.")
-
-# Test code to verify GPU usage
-# Define a simple neural network example
-class SimpleNet(paddle.nn.Layer):
-    def __init__(self):
-        super(SimpleNet, self).__init__()
-        self.linear = paddle.nn.Linear(10, 1)
-
-    def forward(self, x):
-        return self.linear(x)
+# Define an array on the CPU
+array_on_cpu = paddle.to_tensor([1, 2, 3, 4, 5])
 
-# Create a random input tensor
-input_data = paddle.randn([1, 10])
-
-# Instantiate the network
-model = SimpleNet()
+# Check if GPU is available
+use_gpu = paddle.is_compiled_with_cuda() and paddle.device.is_compiled_with_cuda()
 
-# Move model to GPU explicitly if not set by default
-if paddle.is_compiled_with_cuda():
-    model = model.cuda()
+if use_gpu:
+    # Transfer the array to GPU
+    array_on_gpu = array_on_cpu.cuda()
 
-# Perform a forward pass
-output = model(input_data)
+    # Print the array on GPU
+    print("Array on GPU:")
+    print(array_on_gpu)
 
-# Print the output to verify GPU usage
-print("Output:", output)
+    # Verify the device of the tensor
+    print("Device of the tensor:", array_on_gpu.place)
+else:
+    print("CUDA is not available. Cannot load array onto GPU.")