* Include onnxruntime binary when not using pacakge referene or uap app.
* Remove the lib\uap10.0 build from the nuget package - causing conflicts
* Add UWP test
* remove build files
* remove local change
* reset mimalloc and onnx-tensorrt
* change username to Microsoft
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* Merge CPU/GPU nuget pipeline
* Include TensorRT EP libraries into existing GPU nuget package pipeline
* modify to use correct YAML
* Modify for test
* modify for test
* Add depedance
* Add depedance (cont.)
* modify for test
* Add create TensorRT nuget package
* modify for test
* modify for test
* Merge CPU/GPU nuget pipeline
* Include TensorRT EP libraries into existing GPU nuget package pipeline
* modify to use correct YAML
* Modify for test
* modify for test
* Add depedance
* Add depedance (cont.)
* modify for test
* Add create TensorRT nuget package
* modify for test
* fix merge bug
* code refactor
* code refactor
* modify for test
* modify for test
* modify for test
* modify for test
* modify for test
* modify for test
* cleanup
* modify for test
* fix bug
* modify for test
* refactor
* fix bug and test
* Modify for test
* Modify for test
* Modify for test
* Modify for test
* Prepare for PR
* Prepare for PR
* code refacotr from review
* Remove naming 'Microsoft.ML.OnnxRuntime.TensorRT' to avoid confusion
* Add linux TensorRT libraries
* Remove redundant variable in YMAL
* revert file
* undo revert file
* Modify regular expression so that it can capture the correct file
* Remove newline at end of file
* small fix
* Revert to CUDA11.1 on Windows
* Add unit tests for nuget package on Linux
Co-authored-by: Changming Sun <chasun@microsoft.com>
* initial update from 11.1 to 11.4
* change 11.4.1 to 11.4.0
* adjusting to match nvidia/cuda image tags
* adjusting to match nvidia/cuda image tags centos7
* correction to 11.4.0
* correction to 11.4.0
* update to cuda 11.4
* change training back to 11.1
* change training back to 11.1
* point to correct nvcr.io/nvidia/cuda 11.4.1 image
* change centos8 to centos7
* correct cudnn path
* Update linux-gpu-ci-pipeline.yml for Azure Pipelines
* Update c-api-noopenmp-packaging-pipelines.yml
* need to resolve centos images but remove space and change to 11.4
* Update linux-gpu-ci-pipeline.yml
* add cudnn to docker image
* bump devtoolset to 10
* revert cuda 11.4 change to setup_env_trt
* orttraining back to 11.1
* use nvcr.io
* Fix previous change back to cuda 11.1
* update cudnn path
* use cudnn image (revert if failure)
Merge CPU/GPU nuget pipeline. The old GPU nuget pipeline will be only for DML.
TODO: the result GPU package contains PDB files for some of the DLLs, but not all. It is due to the refactoring of CUDA EP to pluggable DLLs. At that time we forgot to copy the PDB files. However, I can't add them in now. Because currently the package is already 220MB large. If the missed PDB files were added, then it will be oversize. nuget.org doesn't accept >250MB packages.
1. Update SDLNativeRules from v2 to v3. The new one allows us setting excluded paths.
2. Update TSAUpload from v1 to v2. And add a config file ".gdn/.gdntsa" for it.
3. Fix some parentheses warnings
4. Update cmake to the latest.
5. Remove "--x86" build option from pipeline yaml files. Now we can auto-detect cpu architecture from python. So we don't need to ask user to specify it.
1. Remove some unused code and simplify tools/ci_build/github/linux/run_dockerbuild.sh.
2. Enable Nuget CUDA tests. The original design was we could leverage Directory.Build.props and let cmake generate the required properties(USE_CUDA/...) there. However, in nuget packaging pipeline we test the package on a different host that doesn't run cmake command and doesn't have the auto-generated Directory.Build.props file.
1. Update manylinux build scripts. This will add [PEP600](https://www.python.org/dev/peps/pep-0600/)(manylinux2 tags) support. numpy has adopted this new feature, we should do the same. The old build script files were copied from https://github.com/pypa/manylinux, but they has been deleted and replaced in the upstream repo. The manylinux repo doesn't have a manylinux2014 branch anymore. So I'm removing the obsolete code, sync the files with the latest master.
2. Update GPU CUDA version from 11.0 to 11.1(after a discussion with PMs).
3. Delete tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda10_2. (Merged the content to tools/ci_build/github/linux/docker/Dockerfile.manylinux2014_cuda11)
4. Modernize the cmake code of how to locate python devel files. It was suggested in https://github.com/onnx/onnx/pull/1631 .
5. Remove `onnxruntime_MSVC_STATIC_RUNTIME` and `onnxruntime_GCC_STATIC_CPP_RUNTIME` build options. Now cmake has builtin support for it. Starting from cmake 3.15, we can use `CMAKE_MSVC_RUNTIME_LIBRARY` cmake variable to choose which MSVC runtime library we want to use.
6. Update Ubuntu docker images that used in our CI build from Ubuntu 18.04 to Ubuntu 20.04.
7. Update GCC version in CUDA 11.1 pipelines from 8.x to 9.3.1
8. Split Linux GPU CI pipeline to two jobs: build the code on a CPU machine then run the tests on another GPU machines. In the past we didn't test our python packages. We only tested the pre-packed files. So we didn't catch the rpath issue in CI build.
9. Add a CentOS machine pool and test our Linux GPU build on real CentOS machines.
10. Rework ARM64 Linux GPU python packaging pipeline. Previously it uses cross-compiling therefore we must static link to C Runtime. But now have pluggable EP API and it doesn't support static link. So I changed to use qemu emulation instead. Now the build is 10x slower than before. But it is more extensible.
1. Remove openmp related packaging pipelines and build jobs.
2. Set continueOnError to true for the TSAUpload tasks. Their service is unstable recently.
3. Update Ubuntu 16 docker images to Ubuntu 18, in prepare for getting C++17 support
4. Cherry-pick the changes in 1.7.1 to the master: updating CFLAGS/CXXFLAGS to strip out debug symbols
* Change msbuild condition for UAP
* update .netcore target as well
* create nuget packages with _native path
* validate path under _native directory for windowsai package
* pep8
* add diagnostic error message
* pep8
* use baseame
* lib\uap10.0
* uap10
* build\\uap10.0
* Manually binplace winmds into appx when PackageReference is used.
* always binplace winmd regardless of packagereference since c# should work with packages.config also
* resolve all paths to full paths to avoid some reference warnings
* move winmds out of lib folder to prevent automatic component registration
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
Add python 3.8/3.9 support for Windows GPU and Linux ARM64
Delete jemalloc from cgmanifest.json.
Add onnx node test to Nuphar pipeline.
Change $ANDROID_HOME/ndk-bundle to $ANDROID_NDK_HOME. The later one is more accurate.
Delete Java GPU packaging pipeline
Remove test data download step in Nuget Mac OS pipeline. Because these machines are out of control and out of our network, it's hard to make it reliable and the data secure.
Fix a doc problem in c-api-artifacts-package-and-publish-steps-windows.yml. It shouldn't copy C_API.md, because the file has been moved into a different branch.
Delete the CI build docker file for Ubuntu cuda 9.x and Ubuntu x86 32 bits
And, due to some internal restrictions, I need to rename some of the agent pools
Update gpu packaging pipelines to CUDA11
In the next release we will use CUDA 11. And our CUDA 11 build suddenly became broken because recently CentOS 7 posted an update of glibc. The version of glibc was changed from 2.17-317.el7 to 2.17-322.el7_9. But the newer one isn't compatible with CUDA 11. We have to downgrade it.
1. Merge Nuget CPU pipeline, Java CPU pipeline, C-API pipeline into a single one.
2. Enable compile warnings for cuda files(*.cu) on Windows.
3. Enable static code analyze for the Windows builds in these jobs. For example, this is our first time scanning the JNI code.
4. Fix some warnings in the training code.
5. Enable code sign for Java. Previously we forgot it.
6. Update TPN.txt to remove Jemalloc.
* build for .net5
* only reference cswinrt for .net5
* remove netstandard2.0 references
* upgrade language version
* net5
* remove extra comment closure
* add targetframework
* set target framework
* remove net*
* pep8 errors
* make test project build with .net windows SDK projection
* disable c# builds for non-x64 builds
* fix pep8 errors
* disable for store build
* fix tests
* remove cswinrt and sdk references from package
* bump cswinrt down to 1.0.1
* fix bin path
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
Transitions from the ORT-only DML NuGet (hosted on the onnxruntime_public feed) to the new unified DirectML NuGet (Microsoft.AI.DirectML) on nuget.org. In addition, the Microsoft.AI.MachineLearning (WinML) and Microsoft.ML.OnnxRuntime.DirectML packages now take a dependency on the Microsoft.AI.DirectML package. This means we can remove the extra copy of DML binaries in these packages since they will be installed by the DML package.
* create new nuget packaging pipeline without openmp
* rename package
* update image name
* rename package name
* rename managed package
* reset project attribute
* merge master
* set package name
* set NoOpenMP as cpu build
* shorten line length
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
