Engine.create_execution_context() Segmentation Fault on Nvidia Jetson Orin Nano

Issue Overview

Users are experiencing a segmentation fault when attempting to create an execution context for a TensorRT engine on the Nvidia Jetson Orin Nano. The issue occurs specifically when running a Python script to load and infer a TensorRT model. The error message indicates a CUDA runtime error, suggesting that the context is destroyed, followed by an invalid argument error and a segmentation fault.

Possible Causes

1. Incompatible TensorRT versions: The user’s TensorRT version (8.0.1.6) may be incompatible with the code or model being used.

2. Incorrect model serialization: The TensorRT engine file may not have been properly serialized or saved.

3. CUDA context issues: There might be problems with the CUDA context initialization or management.

4. Memory allocation errors: The segmentation fault could be due to improper memory allocation or deallocation.

5. API changes: The error messages suggest that some API functions have changed or been deprecated in the user’s TensorRT version.

Troubleshooting Steps, Solutions & Fixes

1. Verify TensorRT engine integrity:
   Use the trtexec tool to check if the engine can be loaded correctly:

   /usr/src/tensorrt/bin/trtexec --loadEngine=resnet_engine_pytorch.trt
   

   If this command works without errors, it confirms that the engine file is valid.

2. Update API calls:
   Replace deprecated or removed functions with their current equivalents:

   • Use get_binding_shape() instead of get_tensor_shape()
   • Use binding_is_input() instead of get_tensor_mode()

   Example:

   size = trt.volume(engine.get_binding_shape(binding)) * batch
   if engine.binding_is_input(binding):
       # Handle input tensor
   

3. Check TensorRT version compatibility:
   Ensure that your code is compatible with TensorRT 8.0.1.6. Consider updating to a more recent version if possible, as the sample code was verified with TensorRT 8.5.

4. Implement proper CUDA context management:
   Ensure that the CUDA context is properly initialized and managed throughout your script. Use pycuda.autoinit at the beginning of your script to handle CUDA context initialization automatically.

5. Implement error handling and logging:
   Add try-except blocks to catch and log specific exceptions, which can provide more information about the cause of the segmentation fault:

   try:
       context = engine.create_execution_context()
   except Exception as e:
       print(f"Error creating execution context: {e}")
       # Add additional logging or error handling as needed
   

6. Use a complete inference pipeline:
   Implement a full inference pipeline based on the working example provided in the forum. This includes proper image loading, preprocessing, and tensor management. Here’s a basic structure:

   import tensorrt as trt
   import pycuda.driver as cuda
   import pycuda.autoinit
   import numpy as np
   import cv2
   
   def load_engine(engine_file_path):
       with open(engine_file_path, "rb") as f:
           runtime = trt.Runtime(trt.Logger(trt.Logger.WARNING))
           return runtime.deserialize_cuda_engine(f.read())
   
   def allocate_buffers(engine, batch_size):
       inputs = []
       outputs = []
       bindings = []
       for binding in engine:
           size = trt.volume(engine.get_binding_shape(binding)) * batch_size
           dtype = trt.nptype(engine.get_binding_dtype(binding))
           host_mem = cuda.pagelocked_empty(size, dtype)
           device_mem = cuda.mem_alloc(host_mem.nbytes)
           bindings.append(int(device_mem))
           if engine.binding_is_input(binding):
               inputs.append({'host': host_mem, 'device': device_mem})
           else:
               outputs.append({'host': host_mem, 'device': device_mem})
       return inputs, outputs, bindings
   
   def infer(engine, context, inputs, outputs, bindings, batch_size):
       # Transfer input data to the GPU
       [cuda.memcpy_htod_async(inp['device'], inp['host'], stream) for inp in inputs]
       # Run inference
       context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
       # Transfer predictions back from the GPU
       [cuda.memcpy_dtoh_async(out['host'], out['device'], stream) for out in outputs]
       # Synchronize the stream
       stream.synchronize()
       # Return only the host outputs
       return [out['host'] for out in outputs]
   
   # Load the TensorRT engine
   engine = load_engine("resnet_engine_pytorch.trt")
   context = engine.create_execution_context()
   
   # Allocate buffers and create a CUDA stream
   inputs, outputs, bindings = allocate_buffers(engine, batch_size=1)
   stream = cuda.Stream()
   
   # Preprocess your input data (e.g., load and resize an image)
   input_image = cv2.imread("your_input_image.jpg")
   preprocessed_image = preprocess_image(input_image)  # Implement this function based on your model's requirements
   
   # Copy preprocessed data to input buffer
   np.copyto(inputs[0]['host'], preprocessed_image.ravel())
   
   # Run inference
   trt_outputs = infer(engine, context, inputs, outputs, bindings, batch_size=1)
   
   # Process the output as needed
   # ...
   
   # Clean up
   for inp in inputs:
       inp['device'].free()
   for out in outputs:
       out['device'].free()
   

   Adjust the preprocessing and postprocessing steps according to your specific model requirements.

7. Monitor system resources:
   Keep an eye on GPU memory usage and CPU load while running your script. Excessive memory usage or CPU load could indicate underlying issues.

8. Check for CUDA driver and toolkit compatibility:
   Ensure that your CUDA driver and toolkit versions are compatible with the TensorRT version you’re using.

By implementing these steps and fixes, you should be able to resolve the segmentation fault issue and successfully run inference using your TensorRT engine on the Nvidia Jetson Orin Nano.