debug gpu_compute and cpu_compute

app.py

@@ -1,4 +1,8 @@
 from app.logger_config import logger as logging
+from app.utils import (
+    debug_current_device,
+    get_current_device
+)
 import os
 import gradio as gr
 import spaces
@@ -9,53 +13,51 @@ import torch
 logging.info("-----------info------------")
 logging.debug("-----------debug------------")
 
-@spaces.GPU(progress=gr.Progress(track_tqdm=True))
-def greet(name):
-    logging.debug("=== Start of greet() ===")
-
-    # Check GPU availability
-    has_gpu = torch.cuda.is_available()
-    logging.debug(f"GPU available: {has_gpu}")
-
-    if has_gpu:
-        device = torch.device("cuda")
-        device_name = torch.cuda.get_device_name(0)
-        memory_allocated = torch.cuda.memory_allocated(0) / (1024 ** 2)
-        memory_reserved = torch.cuda.memory_reserved(0) / (1024 ** 2)
-        memory_total = torch.cuda.get_device_properties(0).total_memory / (1024 ** 2)
-        capability = torch.cuda.get_device_capability(0)
-        current_device = torch.cuda.current_device()
-
-        # Detailed GPU logs
-        logging.debug(f"GPU name          : {device_name}")
-        logging.debug(f"Current device ID : {current_device}")
-        logging.debug(f"CUDA capability   : {capability}")
-        logging.debug(f"Memory allocated  : {memory_allocated:.2f} MB")
-        logging.debug(f"Memory reserved   : {memory_reserved:.2f} MB")
-        logging.debug(f"Total memory      : {memory_total:.2f} MB")
-    else:
-        device = torch.device("cpu")
-        device_name = "CPU (no GPU detected)"
-        logging.debug("No GPU detected, using CPU")
-
-    # Create tensor
+@spaces.GPU
+def gpu_compute(name):
+    logging.debug("=== Start of gpu_compute() ===")
+    debug_current_device()
+    tensor,device_name = compute(name)
+    logging.debug("=== End of gpu_compute() ===")
+    return f"Tensor: {tensor.cpu().numpy()} | Device: {device_name}"
+
+def cpu_compute(name):
+    logging.debug("=== Start of cpu_compute() ===")
+    debug_current_device()
+
+    tensor,device_name = compute(name)
+
+    logging.debug("=== End of cpu_compute() ===")
+    return f"Tensor: {tensor.cpu().numpy()} | Device: {device_name}"
+
+def compute(name) :
+    # Get device info
+    device, device_name = get_current_device()
+    # Create a tensor
     tensor = torch.tensor([len(name)], dtype=torch.float32, device=device)
     logging.debug(f"Tensor created: {tensor}")
-
-    # Optional GPU cleanup
-    if has_gpu:
+    # Optional: free GPU memory
+    if torch.cuda.is_available():
         torch.cuda.empty_cache()
         logging.debug("GPU cache cleared")
+    return tensor, device_name
 
-    logging.debug("=== End of greet() ===")
-    return f"Tensor: {tensor.cpu().numpy()} | Device: {device_name}"
 
-demo = gr.Interface(
-    fn=greet,
-    inputs="text",
-    outputs="text",
-    title="GPU Info Demo",
-    description="Retourne un tenseur et le nom du GPU utilisé"
-)
+block = gr.Blocks()
+
+with block as demo:
+     with gr.Row():
+         input_text = gr.Text()
+         output_text = gr.Text()
+     with gr.Row():
+        gpu_button = gr.Button("GPU compute")
+        cpu_button = gr.Button("CPU compute")
+
+        gpu_button.click(fn=gpu_compute, inputs=[input_text],outputs=[output_text])
+        cpu_button.click(fn=cpu_compute, inputs=[input_text],outputs=[output_text])
+
+with gr.Blocks() as demo:
+    block.render()
 
-demo.launch()
+if __name__ == "__main__":
+    demo.queue(max_size=10, api_open=False).launch(show_api=False)

app/utils.py

@@ -0,0 +1,33 @@
+import torch
+from app.logger_config import logger as logging
+
+def debug_current_device():
+    """Logs detailed information about the current GPU or CPU device."""
+    logging.debug("=== Debugging current device ===")
+
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+        device_name = torch.cuda.get_device_name(0)
+        memory_allocated = torch.cuda.memory_allocated(0) / (1024 ** 2)
+        memory_reserved = torch.cuda.memory_reserved(0) / (1024 ** 2)
+        memory_total = torch.cuda.get_device_properties(0).total_memory / (1024 ** 2)
+        capability = torch.cuda.get_device_capability(0)
+        current_device = torch.cuda.current_device()
+
+        logging.debug(f"GPU name          : {device_name}")
+        logging.debug(f"Current device ID : {current_device}")
+        logging.debug(f"CUDA capability   : {capability}")
+        logging.debug(f"Memory allocated  : {memory_allocated:.2f} MB")
+        logging.debug(f"Memory reserved   : {memory_reserved:.2f} MB")
+        logging.debug(f"Total memory      : {memory_total:.2f} MB")
+
+    else:
+        logging.debug("No GPU detected, using CPU")
+    
+def get_current_device():
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+        device_name = torch.cuda.get_device_name(0)
+        return device, device_name
+    else:
+        return torch.device("cpu"), "CPU (no GPU detected)"