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.
* first attempt rocm training wheel
* modifications needed to python packaging pipeline for Rocm 4.1
* changges to not conflict with cuda
missed stage1 changes
remove package push
add option r to getopt
try again without python install
try again without python install
try again without python install
split pipelines and add back push to remote storage
try on cuda gpu pool
try again
try again
try running without az subscription set
try again on original pipeline
change pool
passing AMD Rocm whl on AMD-GPU pool
split rocm pipeline from cuda pipeline
remove comments
* try adding Rocm tests as well
* try with tests in place
* fix trailing ws
* add training data
* try again as root for tests
* use python3
* typo
* try to map video, render group into container
* try again
* try again
* try to avoid yum error code
* make UID 1001
* try without yum downgrade
* define rocm_version=None
* remove CUDA related comments for Rocm Dockerfile
* Dont pin nightly torch torchvision torchtext versions as they expire (for now nightly is required for Rocm 4.1)
* missed requirements-rocm.txt from last commit
* fix whitespace
* working on re-organizing js code for ortweb
* remove dup files
* move folder
* fix common references
* fix common es5
* add webpack to common
* split interfact/impl
* use cjs for node
* add npmignore for common
* update sourcemap config for common
* update node
* adjust folder/path in CI and build
* update folder
* nit: readme
* add bundle for dev
* correct nodejs paths
* enable ORT_API_MANUAL_INIT
* set name for umd library
* correct name for commonjs export
* add priority into registerBackend()
* fix npm ci pwd
* update eslintrc
* revise code
* revert package-lock lockfileVersion 2->1
* update prebuild
* resolve comments
* update document
* revise eslint config
* update eslint for typescript rules
* revert changes by mistake in backend.ts
* add env
* resolve comments
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
* rename pipelines
* resync and rename
* resync master
* rename package id
* remove OrtPackageId which is for nuget
Co-authored-by: Randy Shuai <rashuai@microsoft.com>
1. For previous openmp build, remove --use_openmp, so thread pool will become default;
2. For previous non-openmp build, add --use_openmp and rename the package to indicate the inclusion.
3. Add a mac build with openmp enabled.
* model building
* fix build
* winml adapter model building api
* model building
* make build
* make build again
* add model building with audio op
* inplace and inorder fft
* add ifft
* works!
* cleanup
* add comments
* switch to iterative rather than recursive and use parallelization
* batched parallelization
* fft->dft
* cleanup
* window functions
* add melweightmatrix op
* updates to make spectrogram test work
* push latest
* add onesided
* cleanup
* Clean up building apis and fix mel
* cleanup
* cleanup
* naive stft
* fix test output
* middle c complete
* 3 tones
* cleanup
* signal def new line
* Add save functionality
* Perf improvements, 10x improvement
* cleanup
* use bitreverse lookup table for performance
* implement constant initializers for tensors
* small changes
* add matmul tests
* merge issues
* support add attribute
* add tests for double data type windowfunctions and minor cleanup
* stft onesided/and not tests
* cleanup
* cleanup
* clean up
* cleanup
* remove threading attribute
* forward declare orttypeinfo
* warnings
* fwd declare
* fix warnings
* 1 more warning
* remove saving to e drive...
* cleanup and fix stft test
* add opset picker
* small additions
* add onnxruntime tests
* add signed/unsigned
* fix warning
* fix warning
* finish onnxruntime tests
* make windows namespace build succeed
* add experimental flag
* add experimental api into nuget package
* add experimental api build flag and add to windows ai nuget package
* turn experimental for tests
* add minimum opset version to new experimental domain
* api cleanup
* disable ms experimental ops test when --ms_experimental is not enabled
* add macro behind flag
* remove unused x
* pr feedback
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.
1. Update the ProtoSrc path. The old one is not used anymore.
2. Regenerate OnnxMl.cs
3. Delete some unused code in tools/ci_build/build.py
4. Avoid set intra_op_param.thread_pool_size in ModelTests in OpenMP build.
5. Fix a typo in the C API pipeline.
* 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>
Fix a typo in tools/ci_build/github/azure-pipelines/templates/get-docker-image-steps.yml.
Add logging to tools/ci_build/get_docker_image.py for easier debugging.
Update training Python packaging build to use get_docker_image.py.
Remove BUILD_EXTR_PAR docker build argument.
Update get_docker_image.py to check again for the image in the cache after building and before pushing to reduce the chance of a redundant push.
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.
- Update docker image release build to use build commit.
- Use valid default in component governance detection step.
- Use smaller docker build context.
* Nuget store packaging
* Move DNNL workaround to EP
* Fix warning as error
* Disable store tests
* Skip store tests
* msbuild target
* Cross compile protoc in Store
* Disable DML in store
* Move store builds to CPU queue
* Copy uap10 to final nuget
* Fix pip8 error
* Remove extra dml copies
* Fix argparse
* pep8
* Forward IsStoreBuild
* Apply is_store_build to duplicate generate_nuspec
* runtimes
* Refactor uap10
* Store .NET
* uap
* PR feedback
* 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
1. Publish the image ACR, instead of building it every time for every PR
2. Make USE_MKLML and USE_OPENMP be able to co-exist. Currently both of them are enabled in our Linux CI build but indeed only one of them is taking effect.
3. Split nuphar and DNNL to separated pipelines.
4. Fix two warnings in onnxruntime/core/optimizer/matmul_scale_fusion.cc and onnxruntime/test/tvm/tvm_basic_test.cc.
5. Update the manylinux2010_x86_64 image to the latest.
1. Avoid building ONNX of every history ONNX versions in our CI, it is costly and easy to fail.
2. Run docker command without sudo. Previously the user is not in docker group, now Azure DevOps Service have added it in.
1. Increase job timeout, while we are investigating why the tests take much longer
2. Upgrade the linux docker image to manylinux2010, by request from Tianlei. (We had an offline discussion with Pranav and Tracy)
3. Remove the installation of "devtoolset-7" in the CUDA image. It was added for CUDA 10.0, it is not needed for CUDA 10.1. We have moved to CUDA 10.1.
Modify gradle build so artifactID has _gpu for GPU builds.
Pass USE_CUDA flag on CUDA build
Adjust publishing pipelines to extract POM from a correct path.
Co-Authored-By: @Craigacp
* try mac pipeline
* fix path separator
* copy prebuilds folder
* split esrp yaml for win/mac
* disable mac signing temporarily
* add linux
* fix indent
* add nodetool in linux
* add nodetool in win-ci-2019
* replace linux build by custom docker scripts
* use manylinux as node 12.16 not working on centos6
* try ubuntu
* loosen timeout for test case - multiple runs calls
- Add support for ENABLE_LANGUAGE_INTEROP_OPS in training build which is enabled for nightly builds
- Fix passing of environment variables to `sudo docker run` in build definitions
- Fix setup.py package naming logic
* add windowsai.yml for new Microsoft.AI.MachineLearning nuget
* temporarily add windowsai.yml to gpu.yml
* pass in build arch
* remove install onnx task
* no dml for arm or arm64
* refactor nuget pipeline defs
* update package creation
* pass in build and sources path
* missing hyphens
* copy license file
* fix parameter variable
* disable arm builds for now
* remove commented script block
* download pipeline atifcat name update
* set working dir
* Add bundling nuget script
* path combine
* null path
* combine needs parentheses
* binplace microsoft.* dlls in new nuget package
* update artifact name
* move merged nuget to artifacts directory
* move to merged subfolder in artifacts staging dir
* forward slash to back
* enable arm
* vcvarsall needs x64 vars setup
* Run Tests
* fix tests
* move global variables
* update yml to not have global variable in template
* removed parameters
* fixes
* Add build arch as an env variable
* ne not neq
* %Var% for batch script
* dont pass argument for x64
* disable arm tests
* skip csharp/cxx tests for microsoft nuget package
* remove test-win as it tests only c# cxx and capi
* test build for store apps
* dont build for store
* tools/nuget/generate_nuspec_for_native_nuget.py
* remove args.
* add new props and targets for microsoft.ai
* make windowsai props/targets static
* add dependency
* dont ship dot net props
* Remove c# fom windowsai nuget
* copy license file
* native packages must have win10 as the platform, not win
* cuda header in wrong if branch
* no dml for arm builds
* only build dml for x64/ x86
* User/sheilk/props update (#3616)
* prelim store work
* props
* Fix desktop nuget props/targets
* clean up targets and make store apps work
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* update windowsai.yml with latest
* remove extra dloadhelpers
* Add abi headers to abi dir, and reference native includes
* update windowsai.yml
* minor update
* remove parameters
* add doesrp param
* hard code esrp to true
* add directml for x86/x64
* revert gpu yml changes
* add store builds
* add store builds
* add checks again in old way
* dup job names for store and desktop builds
* move all of the runtime binaries to win10 folder
* only set safeseh on x86
* disable the store builds for now... missing msvcprt.lib
* copy paste deletion...
* switch back to win- (#3646)
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* use stahlworks
* & not supported in ado
* add cuda to cpu nuget(???) and EnableDelayedExpansion to enable x86 dml package
* revert nocontribops
* add underscore...
* extra win/win10 change
* merged nuget... still not being bundled...
* files in merged directory
* missing parens causing dml to be included in cpu package
* more diagnostic info
* switch dir to get-childitem
* wait for compression to complete
* add winml_adapter to mkml and gpu packages
* enable_wcos
* add mklml binaries
* props and targets missing from mklml
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
1. Fix static analysis warnings found by VC++
2. Add a new pipeline for static analysis
3. Merge all the windows CI build into one single yaml file.(Easier to queue them all).
4. Make DNNL build faster by disabling building the tests and examples.
5. Enable custom op unitest.
* Fix WCOS/Win32 linking bugs
* Remove unused NODEFAULTLIB flags
* Avoid plain target_link_libraries signature
* Avoid plain target_link_libraries signature
* Fix library list escaping
* Use library list instead of string
* Remove duplicate link to windowsapp.lib
* Remove Win32 build workarounds
* Specify CMake policies before initializing language
* Expose Win32 header definitions during build
* Force set API family
* Enable Win32 APIs in featurizer
* Use MT dynamic CRT
* Expose Win32 specific functions
* Disable app container globally
* Disable default wide functions in featurizers
* Add featurizers to test include path
* Workaround https://gitlab.kitware.com/cmake/cmake/issues/19428
* Revert pipeline debugging hacks
* Skip /FI in CUDA sources
* Default to Win32 builds
* Enable WCOS when using WinML
* Use generator expression to apply CMAKE_MSVC_RUNTIME_LIBRARY to C++ only
* add dml gpu pipelines
* add x86 to the gpu dml dev build pipeline
* Enable DML x86 builds
* Fix uint64_t -> size_t warning
* fix warnings
* enable dml on x86 ci builds
* operatorHelper 773 error uint32_t vs uint64_t
* operatorHelper 773 error uint32_t vs uint64_t
* make x86 pipeline use the gpu pool
* more warnings
* fix x86 directml path
* make dml nuget package
* disable tf_pnasnet_large
* disable zfnet512
* make validation use wildcards
* disable x86 dml gpu tests
* add args.
* update gpu.yml
* change nupkg wildcard
* add debug statements
* package x86 dml nupkg
* dont drop managed nuget again from dml pipeline build
* Add DML EULA
* directml license should be renamed to not clobber the existing license
* casing on dml package....
* {} to ()
* fix license name
* disable dml from x86 ci
* typo and cr feedback
* remove featurizers
* ship the dml pdb as well
* WIP: Re-enable x86 .NET testing in Release pipelines
Enabling x86 testing will make sure that ORT packages doesn’t break x86 projects of customers
* Remove setting some env variables
* Comment out a test failing on x86 builds
* More changes
* Minor fix
* More changes
* More changes
* s
* s
* s
* Revert minor change
* More changes
* More changes
* More changes 2
* explicitly set platform target
* Delete bin and obj folders
* Clean output dirs
* Add back TargetFramwork
* Disable x86 .net framework tests
* Skip x86 tests in MKLML pipeline
1. Add LTCG back. It was set to default OFF in my previous PR to speed up Windows build. It is only needed in release pipelines.
2. Remove --use_featurizers from all the packaging pipelines
3. Make sure all the packages have openmp
Use CUDA 10.1 for Linux build
(Windows change is already in)
Please note, cublas 10.2.1.243 is for CUDA SDK 10.1.243, not CUDA 10.2.x. CUDA 10.2.89 need cublas 10.2.2.89. They match on the last part of the digits.
libcublas10-10.1.0.105 won't work!!!
The cuda docker image by viswamy is already using 10.1, no need to change.
* Enable ARM64 release builds
* Add ARM release
* Skip C# dll signing in ARM
* Copy ARM binaries to Nuget
* Restore nuget packages before ARM packaging
* wip
* Use host protoc at C# build
* Set ProtocDirectory on cross-compiled builds
* wip
* Fix typo
* enabme telemetry
* enable telemetry
* set enable telemetry as default
* for debugging
* remove log and set disable telemetry as default back
* delete private file while testing
* resolve comment: mainly add license header, rename macro and update docs
* rewording in privacy.md
* add SAS token to download internal test data for nuget pipeline
* update azure endpoint
* fix keyvault download step
* fix variable declaration for secret group
* fix indentation
* fix yaml syntax for variables
* fix setting secrets for script
* fix env synctax
* Fix macos pipeline
* attempt to add secrets to windows download data
* fix mac and win data download
* fix windows data download
* update test data set url and location
1. refactor the pipeline, remove some duplicated code
2. Move Windows_py_GPU_Wheels job to Win-GPU-CUDA10. We'll deprecated the "Win-GPU" pool
3. Delete cpu-nocontribops-esrp-pipeline.yml and cpu-nocontribops-pipeline.yml
4. In Linux nuget jobs, run "make install" before creating the package. So that extra RPAH info will be removed
This change adds a new execution provider powered by [DirectML](https://aka.ms/DirectML).
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning on Windows. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers.
The DirectML execution provider is capable of greatly improving evaluation time of models using commodity GPU hardware, without sacrificing broad hardware support or requiring vendor-specific extensions to be installed.
**Note** that the DML EP code was moved verbatim from the existing WindowsAI project, which is why it doesn't yet conform to the onnxruntime coding style. This is something that can be fixed later; we would like to keep formatting/whitespace changes to a minimum for the time being to make it easier to port fixes from WindowsAI to ORT during this transition.
Summary of changes:
* Initial commit of DML EP files under onnxruntime/core/providers/dml
* Add cmake entries for building the DML EP and for pulling down the DirectML redist using nuget
* Add a submodule dependency on the Windows Implementation Library (WIL)
* Add docs under docs/execution_providers/DirectML-ExecutionProvider.md
* Add support for DML EP to provider tests and perf tests
* Add support for DML EP to fns_candy_style_transfer sample
* Add entries to the C ABI for instantiating the DML EP
1. remove sudo from the cleanup step for Linux so that we don't need the sudo access for vstsagent build user
2. a minor fix in the install_ubuntu.sh to make the image smaller for openvino
1. Add openvino GPU nightly build pipeline, this test is running on Intel Up square Edge device. The device are host locally not from Azure VM. We persist a smaller model test data on Edge device.
2. Update the build condition for openvino GPU so it works for GPU_FP32, GPU_FP16
3. add option to install_ubuntu.sh to exclude the package used for nuphar, so that we can save some disk space as the Edge device usually have limited disk space.
Enable Nuphar EP docker build
Revert back to LLVM 6.0.1
Reinstate disabled Softmax tests caused by LLVM 8.0.1
Reinstate Nuphar Python test due to stale sympy version
Increase build timeout of Linux CI
Python script and necessary changes in the azure-pipelines yaml file to post the binary size data from NuGet package build. Currently only posted from CPU pipeline. GPU and other pipelines may be added as necessary.
* Add arm64 nocontribops pipeline
* minor fix
* Added new template for arm build -- disable all tests
* fix build command
* add arm64 flag for msbuild
* add arm leg as upstream dependency
* update platform to arm64 for msbuild
* remove test task from arm build
* remove ESRP signing of C# dlls in arm build
* Updated to work for both --arm and --arm64
* Make the cross compiling cmake flags symmetric
* Add dynamic check for /Wno-error flag, instead of extra build option
* remove extra full-stop
* Simplify linux gpu pipeline
* Refactor win-gpu-ci-pipeline.yml
* Set cuda environment variables for testing and version
* Remove variables from starter script
* minor fix
* Add GPU Nuget pipeline
* Set DisableContribOps environment variable for Linux package tests
* Add ESRP tasks
* Add ESRP signing templates
* Test out hardcode value of ERSP
* Test out hardcode value of ERSP
* Test out hardcode value of ERSP
* Test out hardcode value of ERSP
* test variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* test out variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* test variable expansion
* update cpu pipeline to conditionally esrp sign
* Set C# GPU tests to run only if env var is set
* Refactor for easy parameter passing
* refactored esrp templates
* remove variables from template
* Add packaging variables back to pipelines
* update C# for cuda 10
* Merge vars ana parameters for gpu pipeline
* remove vars from mklml pipeline
* display envvars on terminal
* Clean up C# cuda tests, and upgrade to Cuda10
* Introduce CUDNN_PATH pipeline varaible
* YAML variable are always uppercased (not true with classic)
* Update C# GPU test to be more meaningful
* remove macos from gpu tests
* remove debugging info for DisableContribOps option
* Remove DisableContrib ops parameters -- use variables only
* Fix typo from = to -
* remove debug steps
* fix typo
* remove unused variable TESTONGPU from some templates
* clean up CUDA env setup scripts
* Remove CUDNN_PATH from setup_env_cuda.bat
- Added Python script to post the code coverage data to the MySQL table used for dashboard
- Added a build job to run a windows cpu debug build on every merge on master, and run the script
- Removed the code coverage step from the CI build
* Initial check in
* Add win x86
* minor update to x86
* update win-ci
* update win-ci
* update win-x86ci
* add linux and mac templates
* add nuget pipelines and test templates
* remove buildConfig
* add compliance template
* fix minor typos
* update pool for macos
* update mac agent pool
* update macos pool
* update agent pools for tests
* turn off debug build for testing
* some modifications to packaging scripts
* change ordering of compliance tasks
* Add mklml pipeline
* Add packagename variable to mklml pipeline
* remove unrequired dependent jobs from mklml pipeline
* Update build command for macOS legs in mklml and cpu pipeline
* Set vcvars to true
* Add no contrib ops pipeline
* Add no-contrib-ops pipeline
* set vcvars to true for package tests
* remove repetition in nuget templates
* get buildarch correct
* get name of test template correct
* remove steps from test_all_os.yml
* add parameters to test_all_os.yml
* Need jobs, not steps
* set envars for disablecontrib ops
* add cleanup tasks and CG to package tests
* fix path to cleanup script for macos
* remove buildDirectory -- not needed
* remove fp16tiny_yolov2 model from nocontribops tests
* remove debugging info
* fix individual linux pipelines to use correct template
* remove unneeded bak_latest2
* increase timeout to 120 to allow for variance
* turn off code coverage report
* added the runcoverage powershell script
* updated the run coverage script. added installation to the windows CI for trying
* exclude other parts of win ci
* fix in the download script
* fix in the download script
* fix in the download script
* fix in the download script
* fix in the download script
* fix in the download script
* fix in the download script
* fix in the download script
* fix in the download script
* added the runtestcoverage script to the pipeline
* some typo fix
* formatting
* re-commenting previously commented block
* cleaned up the powershell script
* fix path in pipeline
* fix path in pipeline
* fixed model path
* some fixes
* excluded long running tests
* add the publish job
* uncomment other tasks
* fixed excluded tests
* some format correction
* stopped running the test debug
* try placing the tes-all at the beginning
* try running the failing test only
* edit run_coverage
* some fix
* skip onnx_model_test
* Added memory size log in powershell script
* try running the onnxruntime_test_all.exe separately from codecov
* enable error reporting, and double memory size in powershell
* corrected the set-item
* remove memory resize, since we are already at max 2 GB
* fixed the tvm.dll issue
* added back the onnx tests in codecov. added back the regular test run
* cleanup
* remove * from the the module path
* add junction target resolution for modules dir
* remove junction-resolution
* reduced tests
* added target extraction for the junction paths in build machine
* added the appropriate change in win ci pipeline to call the updated ps script
* fix typo
* added back all the tests that were disabled
* try fixing the source root
* cleanup and enable all tests
* increase timeout for windows CPU CI due to codecoverage
* templatized the code coverage steps. Conitnue on error with any codecoverage step
* change quote marks
* Ensure Linux binaries are built with debug info. Extract debug info out of the main binaries. Strip the main binaries.
* add binutils
* add uname
* add binutils
* remove linux portion
* add variables for version number and git commit hash
* fix typo
* fix typo
* some logging
* some logging
* some logging
* some logging
* some logging
* some logging
* some logging
* some logging
* some more edits to see generic scripts can print
* working
* fixing windows git hash
* try quoted echo
* fix git rev-parse
* echo without quotes
* removed commit hash from artifact filename, added long commit hash as a file inside
* added the missing commit id parameter
* fix windows pipeline
* keep only win 64, others disabled
* remove disabling conditions
* added linux packaging template and pipeline
* Update linux-packaging-pipeline.yml for Azure Pipelines
* fix path seperator
* update copy command for linux
* fixed linux gpu artifact name, added mac build
* fixed linux gpu artifact name, added mac build
* fixed vmImage syntax
* use 1 model at a time for macos
* added onnx test on Mac CI
* some refactor of the pipeline scripts
* try fixing the tensorproto for x86 build
* try __cdecl
* try C-style cast
* use ORTAPICALL
* put the deleter under the namespace
* added packaging pipeline
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* put the c-api header file at root instead of under core/session
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* Update win-ci-pipeline.yml for Azure Pipelines
* parameterize the windows build script
* Update win-package-pipeline.yml for Azure Pipelines
* fixed indenting
* fixed indenting
* fix parameter reference syntax
* try using arch = amd64 for the vcvarsall
* remove duplicate tasks
* use vcvarsall
* some more refactor
* fix typo
* fix typo
* factored out the packaging step into a template
* add x86 build to package pipeline
* use amd64 for vcvars arg
* added gpu pipeline. added msbuild platform param
* fix the msbuild platform
* use amd64 host for x86 build
* use buildarch=x86 for vcvarsall
* remove vcvars from setup steps
* add some logging for PNG lib, and disable fns_candy demo for win32
* set allocator alignment to 32 bit for win32 compiler
* disable parallel execution test for x86
* use 64 bit toolchain for x86 build
* add missing -T flag for toolset
* fix string delimietr in workingdirectory name for package build test step
* fix gpu pipeline
* make io_types test conditional
* use cuda 10 instead of cuda 9.1, similar to the ci build
* try some workaround on the io test
* undo inadvertent local change in build.py, also reenable the io test
* make all test run single threaded
* blacklist few failing tests for x86
* added some log in build.py
* edit build.py to disable parallel test
* add the failed tests into the blacklist for win32
* add tf_pasnet_large to blacklist
* change control flow for build.py onnx tests
* add README, license and TPN to the package
* updated build.py test sequence for parallel executor
* updated onnx test flow as per review comment
* add type checking log in the compare_mlvalue
* fix type cast
* blacklist some failed test as of now
* one more blacklisted test
* Added test data arguments to build.py, modified win-ci-pipeline build.
* Updated CI builds to use template tasks, added test data args, removed AZURE_BLOB_KEY uses.
* Fixed up set test data step template.
