* add frontend minst test
* to use torch nightly with torchvision
* remove incorrect comment per reviewer's comment
* experiment torchvision import failure
* experiment install_deps.sh
* more experiment install_deps.sh
* experiment install_deps.sh with --upgrade
* Experiment with install_deps.sh.
* Experiment with install_ubuntu.sh.
* Use Ubuntu 18.04 and Python 3.6 for CI.
* Update cmake version for CI.
* Install MPI on Ubuntu 18.04 for CI.
* Increase tolerance for MNIST test.
* Go back to Ubuntu 16.04 for CI, fix installing from deadsnakes ppa.
* Clean-up.
* Update ort_trainer.py from ort_training.
* Get default Ubuntu Python ver back to 3.5.
* Add underscore to opset_version parameter name in ORTTrainer constructor.
* Move loss/model wrap before the call for sample output.
* Update expected values for MNIST test.
Co-authored-by: liqun <liqun@OrtTrainingDev4.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
Co-authored-by: Sergii Dymchenko <sedymche@microsoft.com>
* Migrate winml to Microsoft Namespace (packaging changes are pending)
* add ns_prefix toggle
* fix packaging
* Users/sheilk/add missing raw header (#3484)
* add dualapipartition
* wrong variable for repo root
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
* remove existence check to force failures
* extra paren
* dualapipartition needs to be referenced from the source
* add microsoft.ai.machinelearning.dll to the output dir
* rename the idl file so that assembly info is correctly added into the winmd
* fix namespaces
* update namespaces
* default to microsoft, and add namespace override as build argument
* update cmakesetings.json as well
* remove from cmakelists.txt
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
Co-authored-by: Changming Sun <chasun@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.
warn that initializers are in graph input
provide a tool to move initializer out of graph input
Motivation and Context
ONNX model from IR_VERSION 4 only treats initializers that appear in graph input as non-constant. This may fail some of the graph optimizations, like const folding, operator fusion and etc. Warn the case and provide a tool.
* added support for ios crosscompilation under linux
* reverted cmake generator change
* if --ios is added protoc can be compiled for host system
* accidently reverted change to compile protoc for host system for ios if protoc exe is not set
* wdata is now used
* accidentally pasted CMAKE_OSX_ARCHITECTURES into CmakeLists.txt, also made bad merge on build.py previously
* removed print
* fixed typeo, deleted commented statements for earlier debugging
* reverted accidental delete
* added asmmacro.h for aarch64 asm
now MlasSgemmKernel**** gets underscore added if needed
no need anymote to differentiate between iOS arm64 and normal amr64 build
onnxruntime.cmake: added check if iOSCross is set to properly set RPATH
* removed 2 spaces
* fix: logcial error fixed, now protoc gets compiled if not supplied with --path_to_protoc_exe
* removed unecessarily added spaces
* removed some more spaces
* 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 support for sessions to share a global threadpool.
* Fix build issues
* Add tests, fix build issues.
* Added some documentation
* Fix centos issue when threadpools become nullptr due to 1 core.
* Fix mac and x86 build issues
* Address some PR comments
* Disabled test for android, added few more tests and addressed more PR comments.
* const_cast
1. Fix onnxruntime server docker file build failure. Tested with the notebook in ONNX tutorial, it works well.
2. Delete the docker files for the other EPs, because currently they don't work and I don't have enough time to update them.
We want to implement SoftmaxCrossentropy and NegativeLossLikelihoodLoss forward training ops for opset-12 but that requires ONNX submodule to point to the latest commit to have the latest and greatest ONNX spec!
- Reverse integrate changes from *.in.proto files in github ONNX repo.
- Regenerate csharp/test/Microsoft.ML.OnnxRuntime.Tests/OnnxMl.cs
- Disable ONNX tests that don't have op implementation for the latest opset.
Discussed with Faith, because the data size is very small and changes are gradual, there is no need to delete the old data. We want to keep all the history.
