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add mingw and cuda on github actions, restored cuda 10.2 on appveyor as a test

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pull/5417/head
Stefano Sinigardi 5 years ago
parent f2786ba19d
commit 24ad413a79
11 changed files with 239 additions and 84 deletions
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  1. 223
      .github/workflows/ccpp.yml
  2. 4
      CMakeLists.txt
  3. 49
      README.md
  4. 18
      appveyor.yml
  5. 8
      cmake/vcpkg_linux_cuda.diff
  6. 8
      cmake/vcpkg_windows_cuda.diff
  7. 5
      src/gemm.c
  8. 2
      src/getopt.c
  9. 2
      src/getopt.h
  10. 2
      src/gettimeofday.c
  11. 2
      src/gettimeofday.h

223
.github/workflows/ccpp.yml
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@ -8,28 +8,21 @@ jobs:
steps:
- uses: actions/checkout@v2
# Install system dependencies
- name: Update apt
run: sudo apt update
- name: Install dependencies
run: sudo apt install yasm
# Install latest CMake.
- uses: lukka/get-cmake@latest
# Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run.
- name: Restore from cache and run vcpkg
env:
vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_linux.diff
uses: lukka/run-vcpkg@v2
with:
# Response file stored in source control, it provides the list of ports and triplet(s).
vcpkgArguments: '@${{ env.vcpkgResponseFile }}'
# Location of the vcpkg as submodule of the repository.
vcpkgDirectory: '${{ github.workspace }}/vcpkg'
vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1'
# Since the cache must be invalidated when content of the response file changes, let's
# compute its hash and append this to the computed cache's key.
appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }}
- name: 'Build with CMake and Ninja'
@ -66,13 +59,11 @@ jobs:
steps:
- uses: actions/checkout@v2
# Install system dependencies
- name: Update apt
run: sudo apt update
- name: Install dependencies
run: sudo apt install libopencv-dev
# Install latest CMake.
- uses: lukka/get-cmake@latest
- name: 'Build with CMake and Ninja'
@ -104,12 +95,76 @@ jobs:
path: ${{ github.workspace }}/uselib*
linux-build-with-cuda:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- name: Update apt
run: sudo apt update
- name: Install dependencies
run: sudo apt install libopencv-dev
- uses: lukka/get-cmake@latest
- name: 'Install CUDA'
env:
INSTALL_CUDA_VERSION: "10.2"
CUDACXX: "/usr/local/cuda-10.2/bin/nvcc"
CUDA_PATH: "/usr/local/cuda-10.2"
CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2"
LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH"
run: |
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.2.89-1_amd64.deb
sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
sudo dpkg -i cuda-repo-ubuntu1804_10.2.89-1_amd64.deb
wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
sudo dpkg -i nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
sudo apt update
sudo apt-get install -y --no-install-recommends cuda-compiler-10-2 cuda-libraries-dev-10-2 cuda-driver-dev-10-2 cuda-cudart-dev-10-2 cuda-curand-dev-10-2
sudo apt-get install -y --no-install-recommends libcudnn7-dev
sudo ln -s /usr/local/cuda-${{ env.INSTALL_CUDA_VERSION }}/lib64/stubs/libcuda.so /usr/local/cuda-${{ env.INSTALL_CUDA_VERSION }}/lib64/stubs/libcuda.so.1
- name: 'Build with CMake and Ninja'
env:
INSTALL_CUDA_VERSION: "10.2"
CUDACXX: "/usr/local/cuda-10.2/bin/nvcc"
CUDA_PATH: "/usr/local/cuda-10.2"
CUDA_TOOLKIT_ROOT_DIR: "/usr/local/cuda-10.2"
LD_LIBRARY_PATH: "/usr/local/cuda-10.2/lib64:/usr/local/cuda-10.2/lib64/stubs:$LD_LIBRARY_PATH"
uses: lukka/run-cmake@v2
with:
cmakeListsOrSettingsJson: CMakeListsTxtAdvanced
cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt'
useVcpkgToolchainFile: true
buildDirectory: '${{ runner.workspace }}/buildDirectory'
cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release"
cmakeBuildType: 'Release'
buildWithCMakeArgs: '--target install'
- uses: actions/upload-artifact@v2
with:
name: darknet-${{ runner.os }}
path: cfg
- uses: actions/upload-artifact@v2
with:
name: darknet-${{ runner.os }}
path: data
- uses: actions/upload-artifact@v2
with:
name: darknet-${{ runner.os }}
path: ${{ github.workspace }}/*dark*
- uses: actions/upload-artifact@v2
with:
name: darknet-${{ runner.os }}
path: ${{ github.workspace }}/uselib*
linux-build-no-opencv-cpp-mode:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
# Install latest CMake.
- uses: lukka/get-cmake@latest
- name: 'Build with CMake and Ninja'
@ -129,26 +184,19 @@ jobs:
steps:
- uses: actions/checkout@v2
# Install system dependencies
- name: Install dependencies
run: brew install libomp yasm
# Install latest CMake.
- uses: lukka/get-cmake@latest
# Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run.
- name: Restore from cache and run vcpkg
env:
vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_osx.diff
uses: lukka/run-vcpkg@v2
with:
# Response file stored in source control, it provides the list of ports and triplet(s).
vcpkgArguments: '@${{ env.vcpkgResponseFile }}'
# Location of the vcpkg as submodule of the repository.
vcpkgDirectory: '${{ github.workspace }}/vcpkg'
vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1'
# Since the cache must be invalidated when content of the response file changes, let's
# compute its hash and append this to the computed cache's key.
appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }}
- name: 'Build with CMake and Ninja'
@ -185,11 +233,9 @@ jobs:
steps:
- uses: actions/checkout@v2
# Install system dependencies
- name: Install dependencies
run: brew install opencv libomp
# Install latest CMake.
- uses: lukka/get-cmake@latest
- name: 'Build with CMake and Ninja'
@ -226,7 +272,6 @@ jobs:
steps:
- uses: actions/checkout@v2
# Install latest CMake.
- uses: lukka/get-cmake@latest
- name: 'Build with CMake and Ninja'
@ -246,22 +291,16 @@ jobs:
steps:
- uses: actions/checkout@v2
# Install latest CMake.
- uses: lukka/get-cmake@latest
# Restore from cache the previously built ports. If "cache miss", then provision vcpkg, install desired ports, finally cache everything for the next run.
- name: Restore from cache and run vcpkg
env:
vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_windows.diff
uses: lukka/run-vcpkg@v2
with:
# Response file stored in source control, it provides the list of ports and triplet(s).
vcpkgArguments: '@${{ env.vcpkgResponseFile }}'
# Location of the vcpkg as submodule of the repository.
vcpkgDirectory: '${{ github.workspace }}/vcpkg'
vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1'
# Since the cache must be invalidated when content of the response file changes, let's
# compute its hash and append this to the computed cache's key.
appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }}
- name: 'Build with CMake and Ninja'
@ -273,7 +312,75 @@ jobs:
buildDirectory: '${{ runner.workspace }}/buildDirectory'
cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release"
cmakeBuildType: 'Release'
buildWithCMakeArgs: '--target install'
buildWithCMakeArgs: '--config Release --target install'
- uses: actions/upload-artifact@v2
with:
name: darknet-vcpkg-${{ runner.os }}
path: cfg
- uses: actions/upload-artifact@v2
with:
name: darknet-vcpkg-${{ runner.os }}
path: data
- uses: actions/upload-artifact@v2
with:
name: darknet-vcpkg-${{ runner.os }}
path: ${{ github.workspace }}/*dark*
- uses: actions/upload-artifact@v2
with:
name: darknet-vcpkg-${{ runner.os }}
path: ${{ github.workspace }}/buildDirectory/*.dll
- uses: actions/upload-artifact@v2
with:
name: darknet-vcpkg-${{ runner.os }}
path: ${{ github.workspace }}/uselib*
windows-build-vcpkg-with-cuda:
runs-on: windows-latest
steps:
- uses: actions/checkout@v2
- name: 'Install CUDA'
run: |
choco install cuda -y
$env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.."
Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1"
refreshenv
- uses: lukka/get-cmake@latest
- name: Restore from cache and run vcpkg
env:
vcpkgResponseFile: ${{ github.workspace }}/cmake/vcpkg_windows_cuda.diff
INSTALL_CUDA_VERSION: "10.2"
CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe"
uses: lukka/run-vcpkg@v2
with:
vcpkgArguments: '@${{ env.vcpkgResponseFile }}'
vcpkgDirectory: '${{ github.workspace }}/vcpkg'
vcpkgGitCommitId: '2bc6cd714f931c32d3299a137d5abe0f86f803e1'
appendedCacheKey: ${{ hashFiles(env.vcpkgResponseFile) }}
- name: 'Build with CMake and Ninja'
env:
INSTALL_CUDA_VERSION: "10.2"
CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe"
uses: lukka/run-cmake@v2
with:
cmakeListsOrSettingsJson: CMakeListsTxtAdvanced
cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt'
useVcpkgToolchainFile: true
buildDirectory: '${{ runner.workspace }}/buildDirectory'
cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release"
cmakeBuildType: 'Release'
buildWithCMakeArgs: '--config Release --target install'
- uses: actions/upload-artifact@v2
with:
@ -302,7 +409,6 @@ jobs:
steps:
- uses: actions/checkout@v2
# Install latest CMake.
- uses: lukka/get-cmake@latest
- name: 'Build with CMake and Ninja'
@ -314,7 +420,7 @@ jobs:
buildDirectory: '${{ runner.workspace }}/buildDirectory'
cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release"
cmakeBuildType: 'Release'
buildWithCMakeArgs: '--target install'
buildWithCMakeArgs: '--config Release --target install'
- uses: actions/upload-artifact@v2
with:
@ -337,12 +443,12 @@ jobs:
name: darknet-vcpkg-${{ runner.os }}
path: ${{ github.workspace }}/uselib*
windows-build-integrated-libs-cpp-mode:
runs-on: windows-latest
steps:
- uses: actions/checkout@v2
# Install latest CMake.
- uses: lukka/get-cmake@latest
- name: 'Build with CMake and Ninja'
@ -354,4 +460,59 @@ jobs:
buildDirectory: '${{ runner.workspace }}/buildDirectory'
cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release -DBUILD_AS_CPP:BOOL=TRUE"
cmakeBuildType: 'Release'
buildWithCMakeArgs: '--target install'
buildWithCMakeArgs: '--config Release --target install'
windows-build-integrated-libs-with-cuda:
runs-on: windows-latest
steps:
- uses: actions/checkout@v2
- name: 'Install CUDA'
run: |
choco install cuda -y
$env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.."
Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1"
refreshenv
- uses: lukka/get-cmake@latest
- name: 'Build with CMake and Ninja'
env:
INSTALL_CUDA_VERSION: "10.2"
CUDA_PATH: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDA_PATH_V10_2: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDA_TOOLKIT_ROOT_DIR: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2"
CUDACXX: "C:\\Program\ Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v10.2\\bin\\nvcc.exe"
uses: lukka/run-cmake@v2
with:
cmakeListsOrSettingsJson: CMakeListsTxtAdvanced
cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt'
useVcpkgToolchainFile: true
buildDirectory: '${{ runner.workspace }}/buildDirectory'
cmakeAppendedArgs: "-DCMAKE_BUILD_TYPE=Release"
cmakeBuildType: 'Release'
buildWithCMakeArgs: '--config Release --target install'
windows-mingw:
runs-on: windows-latest
steps:
- uses: actions/checkout@v2
- name: fixPath
run: |
set PATH=%PATH:C:\Program Files\Git\bin;=%
set PATH=%PATH:C:\Program Files\Git\usr\bin;=%
shell: cmd
- uses: lukka/get-cmake@latest
- name: 'Build with CMake and Ninja'
uses: lukka/run-cmake@v2
with:
cmakeListsOrSettingsJson: CMakeListsTxtAdvanced
cmakeListsTxtPath: '${{ github.workspace }}/CMakeLists.txt'
useVcpkgToolchainFile: true
buildDirectory: '${{ runner.workspace }}/buildDirectory'
cmakeAppendedArgs: "-G\"MinGW Makefiles\" -DCMAKE_BUILD_TYPE=Release"
cmakeBuildType: 'Release'
buildWithCMakeArgs: '--config Release --target install'

4
CMakeLists.txt
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@ -256,7 +256,7 @@ list(APPEND headers
${CMAKE_CURRENT_LIST_DIR}/include/darknet.h
)
#remove windows only files
if(NOT WIN32)
if(NOT MSVC)
list(REMOVE_ITEM headers
${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.h
${CMAKE_CURRENT_LIST_DIR}/src/getopt.h
@ -276,7 +276,7 @@ list(REMOVE_ITEM sources
${CMAKE_CURRENT_LIST_DIR}/src/darknet.c
)
#remove windows only files
if(NOT WIN32)
if(NOT MSVC)
list(REMOVE_ITEM sources
${CMAKE_CURRENT_LIST_DIR}/src/gettimeofday.c
${CMAKE_CURRENT_LIST_DIR}/src/getopt.c

49
README.md
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@ -242,14 +242,10 @@ The `CMakeLists.txt` will attempt to find installed optional dependencies like
CUDA, cudnn, ZED and build against those. It will also create a shared object
library file to use `darknet` for code development.
Do inside the cloned repository:
Open a bash terminal inside the cloned repository and launch:
```
mkdir build-release
cd build-release
cmake ..
make
make install
```bash
./build.sh
```
### How to compile on Linux (using `make`)
@ -270,51 +266,34 @@ Before make, you can set such options in the `Makefile`: [link](https://github.c
To run Darknet on Linux use examples from this article, just use `./darknet` instead of `darknet.exe`, i.e. use this command: `./darknet detector test ./cfg/coco.data ./cfg/yolov4.cfg ./yolov4.weights`
### How to compile on Windows (using `CMake-GUI`)
### How to compile on Windows (using `CMake`)
This is the recommended approach to build Darknet on Windows if you have already
installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, and
OpenCV > 2.4.
Use `CMake-GUI` as shown here on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png):
Open a Powershell terminal inside the cloned repository and launch:
1. Configure
2. Optional platform for generator (Set: x64)
3. Finish
4. Generate
5. Open Project
6. Set: x64 & Release
7. Build
8. Build solution
```PowerShell
.\build.ps1
```
### How to compile on Windows (using `vcpkg`)
If you have already installed Visual Studio 2015/2017/2019, CUDA > 10.0,
cuDNN > 7.0, OpenCV > 2.4, then to compile Darknet it is recommended to use
[CMake-GUI](#how-to-compile-on-windows-using-cmake-gui).
Otherwise, follow these steps:
1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio Community](http://visualstudio.com)
2. Install CUDA and cuDNN
3. Install `git` and `cmake`. Make sure they are on the Path at least for the current account
4. Install [vcpkg](https://github.com/Microsoft/vcpkg) and try to install a test library to make sure everything is working, for example `vcpkg install opengl`
5. Define an environment variables, `VCPKG_ROOT`, pointing to the install path of `vcpkg`
2. Install CUDA
6. Define another environment variable, with name `VCPKG_DEFAULT_TRIPLET` and value `x64-windows`
3. Install [vcpkg](https://github.com/Microsoft/vcpkg) and try to install a test library to make sure everything is working, for example `vcpkg install opengl`
7. Open Powershell and type these commands:
4. Open Powershell and type these commands:
```PowerShell
PS \> cd $env:VCPKG_ROOT
PS Code\vcpkg> .\vcpkg install pthreads opencv[ffmpeg] #replace with opencv[cuda,ffmpeg] in case you want to use cuda-accelerated openCV
PS \> cd vcpkg
PS Code\vcpkg> .\vcpkg install darknet[full]:x64-windows #replace with darknet[opencv-base,weights]:x64-windows for a quicker install; use --head if you want to build latest commit on master branch and not latest release
```
8. Open Powershell, go to the `darknet` folder and build with the command `.\build.ps1`. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in `build_win_debug` and the other in `build_win_release`, containing all the appropriate config flags for your system.
5. You will find darknet inside the vcpkg\installed\x64-windows\tools\darknet folder, together with all the necessary weight and cfg files
### How to compile on Windows (legacy way)

18
appveyor.yml
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@ -24,13 +24,13 @@ environment:
# configuration: Release
# VCPKG_ROOT: C:\Tools\vcpkg
# VCPKG_DEFAULT_TRIPLET: x64-windows
#- platform: Win64
# USE_CUDA: yes
# INSTALL_CUDA_VERSION: 10.2
# COMPILER: vs
# configuration: Release
# VCPKG_ROOT: C:\Tools\vcpkg
# VCPKG_DEFAULT_TRIPLET: x64-windows
- platform: Win64
USE_CUDA: yes
INSTALL_CUDA_VERSION: 10.2
COMPILER: vs
configuration: Release
VCPKG_ROOT: C:\Tools\vcpkg
VCPKG_DEFAULT_TRIPLET: x64-windows
#- platform: Win64
# USE_CUDA: no
# COMPILER: vs
@ -58,8 +58,8 @@ install:
- cd %WORKSPACE%\
- if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.0] curl -L https://developer.nvidia.com/compute/cuda/10.0/Prod/local_installers/cuda_10.0.130_411.31_win10 -o setup.exe
- if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.1] curl -L https://developer.nvidia.com/compute/cuda/10.1/Prod/local_installers/cuda_10.1.105_418.96_win10.exe -o setup.exe
- if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.2] curl -L http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_441.22_win10.exe -o setup.exe
- if [%USE_CUDA%]==[yes] .\setup.exe -s nvcc_%INSTALL_CUDA_VERSION% cuobjdump_%INSTALL_CUDA_VERSION% nvprune_%INSTALL_CUDA_VERSION% cupti_%INSTALL_CUDA_VERSION% gpu_library_advisor_%INSTALL_CUDA_VERSION% memcheck_%INSTALL_CUDA_VERSION% nvdisasm_%INSTALL_CUDA_VERSION% nvprof_%INSTALL_CUDA_VERSION% visual_profiler_%INSTALL_CUDA_VERSION% visual_studio_integration_%INSTALL_CUDA_VERSION% cublas_%INSTALL_CUDA_VERSION% cublas_dev_%INSTALL_CUDA_VERSION% cudart_%INSTALL_CUDA_VERSION% cufft_%INSTALL_CUDA_VERSION% cufft_dev_%INSTALL_CUDA_VERSION% curand_%INSTALL_CUDA_VERSION% curand_dev_%INSTALL_CUDA_VERSION% cusolver_%INSTALL_CUDA_VERSION% cusolver_dev_%INSTALL_CUDA_VERSION% cusparse_%INSTALL_CUDA_VERSION% cusparse_dev_%INSTALL_CUDA_VERSION% nvgraph_%INSTALL_CUDA_VERSION% nvgraph_dev_%INSTALL_CUDA_VERSION% npp_%INSTALL_CUDA_VERSION% npp_dev_%INSTALL_CUDA_VERSION% nvrtc_%INSTALL_CUDA_VERSION% nvrtc_dev_%INSTALL_CUDA_VERSION% nvml_dev_%INSTALL_CUDA_VERSION% occupancy_calculator_%INSTALL_CUDA_VERSION% fortran_examples_%INSTALL_CUDA_VERSION%
- if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.2] choco install cuda -y
- if [%USE_CUDA%]==[yes] if NOT [%INSTALL_CUDA_VERSION%]==[10.2] .\setup.exe -s nvcc_%INSTALL_CUDA_VERSION% cuobjdump_%INSTALL_CUDA_VERSION% nvprune_%INSTALL_CUDA_VERSION% cupti_%INSTALL_CUDA_VERSION% gpu_library_advisor_%INSTALL_CUDA_VERSION% memcheck_%INSTALL_CUDA_VERSION% nvdisasm_%INSTALL_CUDA_VERSION% nvprof_%INSTALL_CUDA_VERSION% visual_profiler_%INSTALL_CUDA_VERSION% visual_studio_integration_%INSTALL_CUDA_VERSION% cublas_%INSTALL_CUDA_VERSION% cublas_dev_%INSTALL_CUDA_VERSION% cudart_%INSTALL_CUDA_VERSION% cufft_%INSTALL_CUDA_VERSION% cufft_dev_%INSTALL_CUDA_VERSION% curand_%INSTALL_CUDA_VERSION% curand_dev_%INSTALL_CUDA_VERSION% cusolver_%INSTALL_CUDA_VERSION% cusolver_dev_%INSTALL_CUDA_VERSION% cusparse_%INSTALL_CUDA_VERSION% cusparse_dev_%INSTALL_CUDA_VERSION% nvgraph_%INSTALL_CUDA_VERSION% nvgraph_dev_%INSTALL_CUDA_VERSION% npp_%INSTALL_CUDA_VERSION% npp_dev_%INSTALL_CUDA_VERSION% nvrtc_%INSTALL_CUDA_VERSION% nvrtc_dev_%INSTALL_CUDA_VERSION% nvml_dev_%INSTALL_CUDA_VERSION% occupancy_calculator_%INSTALL_CUDA_VERSION% fortran_examples_%INSTALL_CUDA_VERSION%
- if [%USE_CUDA%]==[yes] set CUDA_PATH=%ProgramFiles%\NVIDIA GPU Computing Toolkit\CUDA\v%INSTALL_CUDA_VERSION%
- if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.0] set CUDA_PATH_V10_0=%CUDA_PATH%
- if [%USE_CUDA%]==[yes] if [%INSTALL_CUDA_VERSION%]==[10.1] set CUDA_PATH_V10_1=%CUDA_PATH%

8
cmake/vcpkg_linux_cuda.diff
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@ -0,0 +1,8 @@
cuda
cudnn
stb
pthreads
ffmpeg
opencv[ffmpeg]
--triplet
x64-linux

8
cmake/vcpkg_windows_cuda.diff
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@ -0,0 +1,8 @@
cuda
cudnn
stb
pthreads
ffmpeg
opencv[ffmpeg]
--triplet
x64-windows

5
src/gemm.c
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@ -513,10 +513,9 @@ static inline int popcnt_32(uint32_t val32) {
}
//----------------------------
#if (defined(__AVX__) && defined(__x86_64__)) || (defined(_WIN64) && !defined(__MINGW32__))
#ifdef _WIN64
#if (defined(_WIN64) && !defined(__MINGW64__))
#include <intrin.h>
#include <ammintrin.h>
#include <immintrin.h>
@ -2863,4 +2862,4 @@ int test_gpu_blas()
void init_cpu() {
is_avx();
is_fma_avx2();
}
}

2
src/getopt.c
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@ -1,4 +1,4 @@
#ifdef _WIN32
#ifdef _MSC_VER
#include "getopt.h"
#ifdef __cplusplus

2
src/getopt.h
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@ -1,4 +1,4 @@
#ifdef _WIN32
#ifdef _MSC_VER
#ifndef __GETOPT_H__
/**
* DISCLAIMER

2
src/gettimeofday.c
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@ -1,4 +1,4 @@
#ifdef _WIN32
#ifdef _MSC_VER
#include "gettimeofday.h"
int gettimeofday(struct timeval* tp, struct timezone* tzp)

2
src/gettimeofday.h
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@ -1,4 +1,4 @@
#ifdef _WIN32
#ifdef _MSC_VER
#define WIN32_LEAN_AND_MEAN
#include <windows.h>
#include <winsock2.h>

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