Updated logistic regression model
This commit is contained in:
Alejandro Saucedo
parent
157b6592dd
commit
5822850ef2
3 changed files with 37 additions and 61 deletions
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@ -3,6 +3,7 @@ project(kompute_linear_reg VERSION 0.1.0)
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set(CMAKE_CXX_STANDARD 14)
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option(KOMPUTE_ARR_OPT_INSTALLED_KOMPUTE "Enable if you prefer to use your installed Kompute library" 0)
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option(KOMPUTE_OPT_ENABLE_SPDLOG "Extra compile flags for Kompute, see docs for full list" 0)
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set(KOMPUTE_EXTRA_CXX_FLAGS "" CACHE STRING "Extra compile flags for Kompute, see docs for full list")
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@ -14,12 +15,16 @@ endif()
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set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -DDEBUG=1 ${KOMPUTE_EXTRA_CXX_FLAGS}")
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set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -DRELEASE=1 ${KOMPUTE_EXTRA_CXX_FLAGS}")
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find_package(kompute REQUIRED)
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if(KOMPUTE_ARR_OPT_INSTALLED_KOMPUTE)
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find_package(kompute REQUIRED)
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else()
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add_subdirectory(../../ ${CMAKE_CURRENT_BINARY_DIR}/kompute_build)
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endif()
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find_package(Vulkan REQUIRED)
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if(KOMPUTE_OPT_ENABLE_SPDLOG)
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find_package(spdlog REQUIRED)
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find_package(fmt REQUIRED)
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endif()
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add_executable(kompute_linear_reg
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@ -30,11 +35,11 @@ target_link_libraries(kompute_linear_reg
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Vulkan::Vulkan
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)
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include_directories(
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../../single_include/)
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if(KOMPUTE_OPT_ENABLE_SPDLOG)
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target_link_libraries(kompute_linear_reg
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kompute::kompute
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fmt::fmt
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spdlog::spdlog
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)
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target_link_libraries(kompute_array_mult
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spdlog::spdlog)
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endif()
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@ -6,54 +6,12 @@ This example is structured such that you will be able to extend it for your proj
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It contains a cmake build configuration that can be used in your production applications.
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## Pre-requisites
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In order to run this example, you will need the following dependencies:
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* REQUIRED
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+ Vulkan Kompute library must be accessible
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+ The Vulkan SDK must be installed
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* OPTIONAL
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+ SPDLOG - for logging
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+ FMT - for text formatting
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We will cover how you can install Vulkan Kompute in the next section.
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For the Vulkan SDK, the simplest way to install it is through [their website](https://vulkan.lunarg.com/sdk/home). You just have to follow the instructions for the relevant platform.
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For the other libraries, because they are optional you can just make sure you build and install Kompute with these disabled (this will be covered in more detail below).
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Alternatively you can use package managers such as vcpkg to help you install them, although to simplify things you can start without the dependencies first.
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## Set Up Vulkan Kompute Dependency
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You have multiple options to set up Vulkan Kompute. The easiest is to perform a local installation.
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For this, you will want to go to the main repo and run the following cmake command, which will configure it without SPDLOG by default.
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```
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cmake \
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-Bbuild
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```
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You can pass the following optional parameters based on your desired configuration:
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* If you wish to install with spdlog support you just have to pass `-DKOMPUTE_ENABLE_SPDLOG=1`.
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* If you wish to perform the installation on the local folder instead of in your system you can use `-DCMAKE_INSTALL_PREFIX="build/src/CMakeFiles/Export/"` which will basically ensure that the final files are created in the local directory.
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* If you are using a package manager such as `vcpkg` make sure you pass the `-DCMAKE_TOOLCHAIN_FILE=` parameter
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Then you can proceed to run the installation:
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* For Windows / Visual Studio you just have to build `INSTALL.vcxproj`
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* For Linux you can just run the `install` target via `make -C build install`
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You also have the option to build as `Release` or `Debug` - just make sure that you build your example with the same build/debug flags as required.
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## Building the example
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Now that you've set up the dependencies / installation of Vulkan Kompute you can build this example.
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You will notice that it's a standalone project, so you can re-use it for your application.
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This project has the option to either import the Kompute dependency relative to the project or use your existing installation of Kompute.
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To build you just need to run the cmake command in this folder as follows:
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```
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@ -61,14 +19,19 @@ cmake \
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-Bbuild
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```
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Make sure to pass the required flags depending on the configuration above:
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* If you built with Debug make sure you build your example with Debug as well
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* If you installed in the local folder, make sure you pass the CMAKE_PREFIX_PATH pointing to the respective folder (e.g. `-DCMAKE_PREFIX_PATH=../../build/src/CMakeFiles/Export/lib/cmake/kompute/` if parent folder is main repo).
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* If you built Vulkan Kompute with spdlog enabled, make sure to pass `-DKOMPUTE_OPT_ENABLE_SPDLOG=1`
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You can pass the following optional parameters based on your desired configuration:
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* If you wish to install with spdlog support you just have to pass `-DKOMPUTE_OPT_ENABLE_SPDLOG=1`.
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* If you are using a package manager such as `vcpkg` make sure you pass the `-DCMAKE_TOOLCHAIN_FILE=` parameter
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* If you wish to load shader from raw glsl string instead of spirv bytes you can use `-DKOMPUTE_ANDROID_SHADER_FROM_STRING`
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Now you just have to build your application as above:
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## Pre-requisites
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* For Windows / Visual Studio you just have to build and run `kompute_linear_reg.vcxproj`
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* For Linux you can just run the `kompute_linear_reg` target via `make -C build kompute_linear_reg`
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In order to run this example, you will need the following dependencies:
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* REQUIRED
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+ The Vulkan SDK must be installed
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* OPTIONAL
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+ Vulkan Kompute library must be accessible (by default it uses the source directory)
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+ SPDLOG - for logging
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+ FMT - for text formatting
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@ -36,22 +36,30 @@ int main()
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kp::Manager mgr;
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std::shared_ptr<kp::Sequence> sqTensor =
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mgr.createManagedSequence().lock();
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mgr.createManagedSequence();
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sqTensor->begin();
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sqTensor->record<kp::OpTensorCreate>(params);
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sqTensor->end();
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sqTensor->eval();
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std::shared_ptr<kp::Sequence> sq = mgr.createManagedSequence().lock();
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std::shared_ptr<kp::Sequence> sq = mgr.createManagedSequence();
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// Record op algo base
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sq->begin();
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sq->record<kp::OpTensorSyncDevice>({ wIn, bIn });
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sq->record<kp::OpAlgoBase<>>(
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#ifdef KOMPUTE_ANDROID_SHADER_FROM_STRING
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sq->record<kp::OpAlgoBase>(
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params, "shaders/glsl/logistic_regression.comp");
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#else
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sq->record<kp::OpAlgoBase>(
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params, std::vector<char>(
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kp::shader_data::shaders_glsl_logisticregression_comp_spv,
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kp::shader_data::shaders_glsl_logisticregression_comp_spv
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+ kp::shader_data::shaders_glsl_logisticregression_comp_spv_len));
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#endif
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sq->record<kp::OpTensorSyncLocal>({ wOutI, wOutJ, bOut, lOut });
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