针对你遇到的 pycuda._driver.logicerror: cumemcpyhtodasync failed: invalid argument 错误,这里有一些可能的解决步骤和考虑因素,帮助你定位和解决问题: 确认cumemcpyhtodasync函数调用的参数是否正确: cumemcpyhtodasync 函数用于将主机(Host)内存中的数据异步复制到设备(
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] File "/opt/github/yolov3-tiny-onnx-TensorRT/common.py", line 145, in <listcomp> [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] pycuda._driver.LogicError: cuMemcpyHtoDAsync failed...
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] File "/opt/github/yolov3-tiny-onnx-TensorRT/common.py", line 145, in <listcomp> [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs] pycuda._driver.LogicError: cuMemcpyHtoDAsync failed...
问memcpy_htod和to_gpu在Pycuda中的差异？EN例如，将self的内容转换为数组或新分配的numpy.ndarray。如...
pycuda._driver.LogicError: cuMemcpyHtoD failed: invalid device context whats the problem? Environment TensorRT Version: 8.0.3 GPU Type: RTX 2080 Ti Nvidia Driver Version: 470.57.02 CUDA Version: 11.3 CUDNN Version: – Operating System + Version: Ubuntu 18.0...
主机到设备的拷贝:使用cuda.memcpy_htod。 设备到主机的拷贝:使用cuda.memcpy_dtoh。 四、PyCUDA 进阶功能 1. 使用共享内存加速计算 共享内存是 GPU 内核中一块高速缓存,可显著提升内核的计算性能。 示例:使用共享内存实现数组求和 kernel_code="""
void py_memcpy_dtoh_async(py::object dest, CUdeviceptr src, py::object stream_py) { py_buffer_wrapper buf_wrapper; buf_wrapper.get(dest.ptr(), PyBUF_ANY_CONTIGUOUS | PyBUF_WRITABLE); PYCUDA_PARSE_STREAM_PY; CUDAPP_CALL_GUARDED_THREADED(cuMemcpyDtoHAsync, (buf_wrapper.m_buf.buf, ...