tensorrt-cu12

import tensorrt as trt import os # A basic example: creating a TensorRT builder and network # Note: A real application would involve loading an ONNX/UFF model and building an engine. # Create a logger to track verbose output and errors TRT_LOGGER = trt.Logger(trt.Logger.WARNING) try: # Create a builder builder = trt.Builder(TRT_LOGGER) print(f"TensorRT Builder created successfully. Max batch size: {builder.max_batch_size}") # Create an empty network definition. EXPLICIT_BATCH is crucial for modern TensorRT. network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) print("Network created with EXPLICIT_BATCH flag.") # Example: Add an input layer (simplified, a real model would have specific shapes) input_tensor = network.add_input(name='input_tensor', dtype=trt.float32, shape=(1, 3, 224, 224)) print(f"Added input tensor with shape {input_tensor.shape}") # In a real scenario, you'd parse a model, e.g., using trt.OnnxParser(network, TRT_LOGGER) # and then configure the builder for engine creation and serialization. except Exception as e: print(f"An error occurred: {e}") print("Please ensure you have a compatible NVIDIA GPU and the correct CUDA/cuDNN installations.") # Clean up resources (important for complex applications) # Note: In a production script, `del` might not be strictly necessary if objects go out of scope, # but it's good practice for clarity or long-running processes. # Also, ensure network and builder are valid objects before attempting to delete. if 'network' in locals() and network is not None: del network if 'builder' in locals() and builder is not None: del builder if 'TRT_LOGGER' in locals() and TRT_LOGGER is not None: del TRT_LOGGER