Create GPU_test.py

GPU_test.py

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+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)
+
+# Create a random input tensor
+input_data = paddle.randn([1, 10])
+
+# Instantiate the network
+model = SimpleNet()
+
+# Move model to GPU explicitly if not set by default
+if paddle.is_compiled_with_cuda():
+    model = model.cuda()
+
+# Perform a forward pass
+output = model(input_data)
+
+# Print the output to verify GPU usage
+print("Output:", output)