1. Fix training e2e pipeline. The failure was caused by my recent change #7632. The fix is adding "--cmake_extra_defines CMAKE_CUDA_ARCHITECTURES=70" to the build parameters because the machines are with V100 GPUs.
2. Simplify Nuphar pipeline. It doesn't need to install a separated ONNX version(1.5.0)
3. Fix a problem that run_dockerbuild.sh ignored OS version parameter. Now because it starts to take effect, I also set python version to the system default one(3.8 for ubuntu 20.04)
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.
* Add podspec template for ios package
* minor formatting update
* Add spec.source_files for header files
* Update spec.public_header_files to spec.source_files
* minor update
* Update the operator documentation generation
- Make layout a little nicer
- Update to latest supported operators including training
- Fix some links that are broken when the docs content is copied to github-pages
- Fix incorrect usage of 'onnx.ai.ml' as the default domain
- ML ops are now separated from the real default domain of 'onnx.ai'
- Include CPU, CUDA and training kernels
- exclude DNNL as it's not an EP we own
* There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library.
* Enable validation of both contrib ops and operator kernels in build
Filter generation so it's deterministic
Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs.
Remove workarounds for github-pages as that will now link to the github docs which display correctly
* fixed bugs in packed mode and enable pack mode tests in ci
* removed unnecessary space
* pr comments
* pr comments
* disable an average pool test
* try disabling another avg pool
* disable more avg pool tests
* disable maxpool tests
* First iteration of making cuda a shared provider.
Separated out shared OpKernel change, so doing this to merge with that change.
* More cuda shared library refactoring
* More cuda shared library refactoring
* More build options tested, converted the training ops over.
* Fix merge breaks
* Fix submodules
* Fix submodules
* Fix submodules
* Fix python
* Fix compile errors
* Duplicate symbol fix
* Test fix for ROCM provider
* Another ROCM test workaround
* ROCM Build Test
* ROCM build fix
* ROCM
* ROCM
* ROCM
* ROCM
* ROCM
* ROCM test
* Reduce header dependencies
* Remove redundant namespace
* Test fix for linux
* Fix linux build
* Fix Eigen build error
* Fix unused parameter warning
* Test link error
* Another linker test
* Linker test
* Linker test
* Another test
* Another build test
* Fix linux link error
* Build test
* Fix control flow ops to use common base class with core code
* Remove extra qualifiers
* Fix template syntax for linux
* Fix cuda memory leak
* Fix pybind
* Test disabling cast
* Cleanup
* Restore cuda in test
* Remove more header dependencies
* Test not adding cuda provider to session
* Make GetProviderInfo_CUDA throw
* No-op cuda provider creation
* Fix some setup issues
* Fix memory cleanup on unload
* Diagnostics
* Don't unload library
* Add diagnostics
* Fix deleting registry at right time.
* Test disabling profiler
* Fix merge break
* Revert profiler change
* Move unloading of shared providers into Environment
* Free more global allocations before library unloads
* Add more diagnostics
* Move unloading back to the OrtEnv as there are multiple Environments created during a session.
Remove some library dependencies for tests.
* Fix more cmake files
* ERROR -> WARNING
* Fix python shutdown
* Test not using dml in pipeline
* Change python version and disable dml
* Update python version
* Test adding unload method for shared providers
* Disable DLL test
* Python test
* Revert "Python test"
This reverts commit c7ec2cfe98.
* Revert "Disable DLL test"
This reverts commit e901cb93aa.
* Revert "Test adding unload method for shared providers"
This reverts commit c427b78799.
* Point to RyanWinGPU
* Revert python version
* Fix id_to_allocator_map
* Another python exit test
* Remove extra debug messages
Try a more clean python shutdown through DllMain
* Revert DllMain idea, it didn't work
* Merge conflicts
* Fix merge with master issues.
* Comments
* Undo edit to file
* Cleanup + new training ops
* Revert yml changes
* Fix another merge error
* ROCM fix
* ROCM fix v2
* Put back Linux hack, it is necessary
* Stupid fixes
* Fix submodule out of sync
* ROCM fix 3
* ROCM 4
* Test java fix
* Fix typos
* Java test on my VM
* Fix build error
* Spotless fix
* Leave temp file around to load properly
* Fix cleanup on exit
* Fix break
* Java comments
* Remove LongformerAttentionBase workaround
* Spotless fix
* Switch yml back to regular build pool
* Revert "Switch yml back to regular build pool"
This reverts commit be35fc2a5a.
* Code review feedback
* Fix errors due to merge
* Spotless fix
* Fix minimal build
* Java fix for non cuda case
* Java fix for CPU build
* Fix Nuphar?
* Fix nuphar 2
* Fix formatting
* Revert "Remove LongformerAttentionBase workaround"
This reverts commit 648679b370.
* Training fix
* Another java fix
* Formatting
* Formatting
* For orttraining
* Last orttraining build fix...
* training fixes
* Fix test provider error
* Missing pass command
* Removed in wrong spot
* Python typo
* Python typos
* Python crash on exit, possibly due to unloading of libraries.
* Remove test_execution_provider from training build
Only enable python atexit on windows
Remove assert on provider library exit
* Still can't unload providers in python, alas.
* Disable Nvtx temporarily
* MPI Kernels for Training
* MPI Kernels part 2
* Patch through INcclService
* Oops, wrong CMakeLists
* Missing namespace
* Fix missing ()
* Move INcclService::GetInstance around to link nicer
* Missing }
* Missing MPI libraries for Cuda
* Add extra GetType functions used by MPI
* Missing Nccl library
* Remove LOGS statements as a test
* Add in a couple more missing GetType methods
* Update comments
* Missed a logging reference in mpi_context.h
* Convert aten_op to shared (due to marge with master)
* Test moving DistributedRunContext instance into shared provider layer
(with purpose error to verify it's being built properly)
* Test passed, now with fix
* Missing static
* Oops, scope DistributedRunContext to just NCCL
* Merge related issues and code review feedback.
* Merge error
* Bump to rel-1.9.1 (#7684)
* Formatting
* Code review feedback for Java build on non Windows
* Remove cupti library dependency from core library
* Test Java pipeline fix
* Linux build fix
* Revert "Linux build fix"
This reverts commit a73a811516.
* Revert "Remove cupti library dependency from core library"
This reverts commit 6a889ee8bf.
* Packaging pipeline fixes to copy cuda shared provider for tensorrt & standard packages
* Add cuda to Tensorrt nuget package
* onnxruntime_common still has a cuda header dependency
Co-authored-by: ashbhandare <ash.bhandare@gmail.com>
1. Move the multi GPU pipeline to CUDA 11.0
2. Exclude the keras2coreml_SimpleRNN_ImageNet model test.
3. Add a test for NV6+CUDA 11.0
BTW, it's known our code doesn’t build with CUDA10.2 + Nvidia T4.
I saw a test timeout in our nodejs packaging pipeline. I'm not sure if it is because it ran slower than before or it's a deadlock issue. Increasing the timeout will be helpful for investigating such issues.
