I have updated @Oguz Vuruskaner's answer and article to support newer version of TensorRT

import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit  # Note: required! to initialize pycuda
import tensorrt as trt

class TensorRTInference:
    def __init__(self, engine_path):
        # initialize
        self.logger = trt.Logger(trt.Logger.ERROR)
        self.runtime = trt.Runtime(self.logger)

        # setup
        self.engine = self.load_engine(engine_path)
        self.context = self.engine.create_execution_context()

        # allocate buffers
        self.inputs, self.outputs, self.bindings, self.stream = self.allocate_buffers(
            self.engine
        )

    def load_engine(self, engine_path):
        # loads the model from given filepath
        with open(engine_path, "rb") as f:
            engine = self.runtime.deserialize_cuda_engine(f.read())
        return engine

    class HostDeviceMem:
        def __init__(self, host_mem, device_mem, shape):
            # keeping track of addresses
            self.host = host_mem
            self.device = device_mem
            # keeping track of shape to un-flatten it later
            self.shape = shape

    def allocate_buffers(self, engine):
        inputs, outputs, bindings = [], [], []
        stream = cuda.Stream()

        for i in range(engine.num_io_tensors):
            tensor_name = engine.get_tensor_name(i)
            shape = engine.get_tensor_shape(tensor_name)
            size = trt.volume(shape)
            dtype = trt.nptype(engine.get_tensor_dtype(tensor_name))

            # allocate host and device buffers
            host_mem = cuda.pagelocked_empty(size, dtype)
            device_mem = cuda.mem_alloc(host_mem.nbytes)

            # append the device buffer address to device bindings
            bindings.append(int(device_mem))

            # append to the appropiate input/output list
            if engine.get_tensor_mode(tensor_name) == trt.TensorIOMode.INPUT:
                inputs.append(self.HostDeviceMem(host_mem, device_mem, shape))
            else:
                outputs.append(self.HostDeviceMem(host_mem, device_mem, shape))

        return inputs, outputs, bindings, stream

    def infer(self, input_data):
        # transfer input data to device
        np.copyto(self.inputs[0].host, input_data.ravel())
        cuda.memcpy_htod_async(self.inputs[0].device, self.inputs[0].host, self.stream)

        # set tensor address
        for i in range(self.engine.num_io_tensors):
            self.context.set_tensor_address(
                self.engine.get_tensor_name(i), self.bindings[i]
            )

        # run inference
        self.context.execute_async_v3(stream_handle=self.stream.handle)

        # transfer predictions back
        for i in range(len(self.outputs)):
            cuda.memcpy_dtoh_async(
                self.outputs[i].host, self.outputs[i].device, self.stream
            )

        # synchronize the stream
        self.stream.synchronize()

        # un-flatten the outputs
        outputs = []
        for i in range(len(self.outputs)):
            output = self.outputs[i].host
            output = output.reshape(self.outputs[i].shape)
            outputs.append(output)

        return outputs

Note: above snippet is pretty much same as the article that I have referenced but with some small tweaks.

If you are also using plugins in your model you will have to add following before calling the self.load_engine(engine_path)

 # loading plugins
# Note: default namespace is ""
# https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#register-plugin-create
trt.init_libnvinfer_plugins(self.logger, namespace="")