* implement cuda provider
* define profiler common
* call start after register
* add memcpy event
* add cuda correlation
* format code
* add cupti to test path
* switch to CUpti_ActivityKernel3
* reset cupti path
* fix test case
* fix trt pipeline
* add namespace
* format code
* exclude training from testing
* remove mutex
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.
* cancel night build on pyop
* setup win cuda11 pipeline
* add debug build
* test base gpu settings
* setup pipelines to test cuda 10.2 and 11
* rename linux docker images
* rename docker image tag and add clean up job
* fix typo in cuda 11 config
* set cuda11 env
* update linux cuda 11 pipeline
* reset docker image name
* disable uninitialized warning from linux build
* change the way to silence uninitialized warning
* add flags to linux gpu pipeline
* switch docker image for linux cuda 10.2
* switch linuc cuda 10.2 image
* test cuda11 with devtool8
* try latest built images
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
