* update trt 8.4ga
* trt 8.4 linux ci pipeline
* fix cmake
* placeholder_builder
* trt 8.4 windows pipeline
* gpu package pipeline
* trt 8.4.1.5 , packaging pipeline updates
* python packaging
* ctest timeout
* python packaging test
* bump timeout
* python format
* format
* revert
* newline
* enable trt python tests
* typo
* python format
* disable on windows
* update base image from 11.4.0 to 11.4.2
* update Linux TRT GPU pipeline to TRT 8.2
* update onnx-tensorrt to 8.2-GA
* disable failing TensorRT 8.2 tests.
* update pad test.
* fix
* update win trt ci pipeline to trt 8.2
* test run with cuda 11.4 and cudnn 8.2
* increase timeout
* revert
* revert
* update packaging pipelines to use trt 8.2
* fix typo
* update trt gpu perf pipeline to trt 8.2
* increase timeout
* delete deprecated ci-perf-pipeline.yml
* bump timeout
* adjust timeout packaging
* update onnx-tensorrt parser to master
* disable unsupported tests
* add cuda sm 75 for T4
* update tensorrt pipeline
* update trt pipelines
* update trt pipelines
* Update linux-gpu-tensorrt-ci-pipeline.yml
* update trt cid pipeline
* Update linux-gpu-tensorrt-ci-pipeline.yml
* Update Tensorrt Windows build pool and TensorRT/CUDA/CuDNN version
* update to cuda11.4 in trt ci pipeline
* update base image to cuda11.4
* update packaging pipeline to cuda11.4
* clean up
* remove cuda11.1 and cuda11.3 docker file
* disable unsupported tensorrt tests at runtime
* Update linux-multi-gpu-tensorrt-ci-pipeline.yml
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 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
This PR adds infrastructure to automatically cache docker images used in CI builds in a container registry.
Currently, build images are pulled from a container registry for some builds and built every time for others. The container registry requires maintenance to keep the images up to date and building images every time wastes build agent resources.
With this change, a given build image can be looked up in a cache container registry and if present, pulled, and otherwise, built and pushed. The uniqueness of a build image is determined by a hash digest of the dockerfile, docker build context directory, and certain "docker build" options. This digest is part of the image tag in the cache container repository.
The cache container registry will need to be cleaned up periodically. This is not automated yet.
* enable rejecting models based on onnx opset
* enable unreleased opsets in linux and mac CI
* test fixes and more updates
* enable unreleased opsets in CI builds
* enable released opsets in linux cis
* try fix windows ci yml
* yml fixes
* update yml
* yml updates post master merge
* review comments
* bug fix
* update onnx-tensorrt submodule to trt7 branch
* add fp16 option for TRT7
* switch to master branch of onnx tensorrt
* update submodule
* update to TensorRT7.0.0.11
* update to onnx-tensorrt for TensorRT7.0
* switch to private branch due to issues in master branch
* remove trt_onnxify
* disable warnings c4804 for TensorRT parser
* disable warnings c4702 for TensorRT parser
* add back sanity check of shape tensort input in the parser
* disable some warnings for TensorRT7
* change fp16 threshold for TensorRT
* update onn-tensorrt parser
* fix cycle issue in faster-rcnn and add cycle detection in GetCapability
* Update TensorRT container to v20.01
* Update TensorRT image name
* Update linux-multi-gpu-tensorrt-ci-pipeline.yml
* Update linux-gpu-tensorrt-ci-pipeline.yml
* disable rnn tests for TensorRT
* disable rnn tests for TensorRT
* disabled some unit test for TensorRT
* update onnx-tensorrt submodule
* update build scripts for TensorRT
* formating the code
* Update TensorRT-ExecutionProvider.md
* Update BUILD.md
* Update tensorrt_execution_provider.h
* Update tensorrt_execution_provider.cc
* Update win-gpu-tensorrt-ci-pipeline.yml
* use GetEnvironmentVar function to get env virables and switch to Win-GPU-2019 agent pool for win CI build
* change tensorrt path
* change tensorrt path
* fix win ci build issue
* update code based on the reviews
* fix build issue
* roll back to cuda10.0
* add RemoveCycleTest for TensorRT
* fix windows ci build issues
* fix ci build issues
* fix file permission
* fix out of range issue for max_workspace_size_env
* Finer control over when Python tests are run
* add --build_wheel to linux pipeline, instead of run_build.sh
* add --build_wheel to all ci configurations
* update per review comments
* Test protobuf-lite
* Test protobuf-lite
* Test protobuf-lite
* Optimize protobuf usage for LITE_RUNTIME to reduce the binary size of
onnxruntime.dll. More details can be found here https://developers.google.com/protocol-buffers/docs/proto.
The reduction is significant. For commit id: 4873b452151bafe49da332aaeab639ef0318fc1ca28d728, the size
reduced by ~700K; from 4873728 to 4172800.
* Add LITE_RUNTIME flag in in.proto files
* Fix merge conflict.
* Address PR comments
* Forgot to add 2 files + fix linux and gpu build errors.
* Fix build errors + test failures
* Fix cuda tests
* Fix tensor rt build
* Use full protobuf for trt
* Address PR comments
* Print tensor shape proto as text string for easier debugging
* updated cmake files for trt
* added trt execution provider
* added trt basic test
* removed trt_path action attribute
* Add files via upload
* Update build.py
* Update trt_allocator.h
* fixed issues found by reviewers
* changed cast operator
* added comment for custom kernel implementation
* changed auto to auto&
* changed to function compile APIs for TRT execution provider
* changed to function compile APIs for TRT execution provider
* added new DType DInt64
* adapted to the changes of onnxruntime_c_api
* removed trt kernel (use function compile instead)
* updated onnx-tensorrt submodule
* set default memory type to TRT fused kernel
* resolve merge conflict
* fixed the issue that USE_CUDA conflicts with USE_TRT
* construct graph by adding nodes in topological order
* made changes for Windows
* change buffers type
* bypass HasImplementationOf check for TRT XP because TRT kernel is not registered
* added domain to version info in rebuilt model proto
* added trt to test option list
* added DomainToVersionMap() to GraphViewer
* removed Copy()
* fixed broken code
* format the code to clang format
* used local reference to the frequently used values
* fixed a couple of issues according to reviewers feedback
* fixed a couple of issues according to reviewers feedback
* added python binding for TRT and enable use_cuda when use_trt is on
* fixed a redefinition issue
* changed shared_ptr to unique_ptr on trt engines, and made a few changes required by reviewers
* enabled trtexecution provider for unit tests
* renamed trt to tensorrt
* added tesorrt to python binding
* update submodule onnx and onnx-tensorrt
* made a couple of minor changes based on reviewer's feedback
* added CUDA_CHECK
* removed test code
* fixed broken code after merge
* updated onnx-tensorrt submodule
* added post processing to align trt inputs/outputs with graph inputs/outputs
* updated onnx submodule
* added CUDA fallback for TensorRT and fixed TensorRT cmake issue
* added ci pipeline for tensorrt and removed some redundent code from trt xp
* fixed syntax issue
* updated onnx-tensorrt submodule
* fix trt build problem by: (#602)
1. Add additional /wd for debug build
2. Add io.h for additional targets
3. Bring back mb version of getopt
* Update install_ubuntu.sh
* Update linux-gpu-tensorrt-ci-pipeline.yml
* Update linux-gpu-tensorrt-ci-pipeline.yml
* Update run_build.sh
* Update run_build.sh
* Update run_build.sh
* Update run_build.sh
* fixed the issue that GetKernelRegistry returns nullptr
* merged master to this branch
* moved some data types to private
* fixed tensorrt CI pipeline issue
* customized test data for TensorRT pipeline
* added onnx-tensorrt in json file and fixed an issue in ci script
* added comments
