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megcore_cuda.h 1.8 kB

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  1. /**
  2. * \file dnn/include/megcore_cuda.h
  3. * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
  4. *
  5. * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
  6. *
  7. * Unless required by applicable law or agreed to in writing,
  8. * software distributed under the License is distributed on an
  9. * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  10. */
  11. #pragma once
  12. #include "./megcore.h"
  13. #include <cuda_runtime_api.h>
  14. #include "megdnn/internal/visibility_prologue.h"
  15. namespace megcore {
  16. struct CudaContext {
  17. cudaStream_t stream = nullptr;
  18. //! device pointer to buffer for error reporting from kernels
  19. AsyncErrorInfo* error_info = nullptr;
  20. CudaContext() = default;
  21. CudaContext(cudaStream_t s, AsyncErrorInfo* e) : stream{s}, error_info{e} {}
  22. };
  23. megcoreStatus_t createComputingHandleWithCUDAContext(
  24. megcoreComputingHandle_t* compHandle, megcoreDeviceHandle_t devHandle,
  25. unsigned int flags, const CudaContext& ctx);
  26. megcoreStatus_t getCUDAContext(megcoreComputingHandle_t handle, CudaContext* ctx);
  27. } // namespace megcore
  28. static inline megcoreStatus_t megcoreCreateComputingHandleWithCUDAStream(
  29. megcoreComputingHandle_t* compHandle, megcoreDeviceHandle_t devHandle,
  30. unsigned int flags, cudaStream_t stream) {
  31. megcore::CudaContext ctx;
  32. ctx.stream = stream;
  33. return megcore::createComputingHandleWithCUDAContext(
  34. compHandle, devHandle, flags, ctx);
  35. }
  36. static inline megcoreStatus_t megcoreGetCUDAStream(
  37. megcoreComputingHandle_t handle, cudaStream_t* stream) {
  38. megcore::CudaContext ctx;
  39. auto ret = megcore::getCUDAContext(handle, &ctx);
  40. *stream = ctx.stream;
  41. return ret;
  42. }
  43. #include "megdnn/internal/visibility_epilogue.h"
  44. // vim: syntax=cpp.doxygen

MegEngine 安装包中集成了使用 GPU 运行代码所需的 CUDA 环境,不用区分 CPU 和 GPU 版。 如果想要运行 GPU 程序,请确保机器本身配有 GPU 硬件设备并安装好驱动。 如果你想体验在云端 GPU 算力平台进行深度学习开发的感觉,欢迎访问 MegStudio 平台