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.
* Remove nGraph Execution Provider
Pursuant to nGraph deprecation notice: https://github.com/microsoft/onnxruntime/blob/master/docs/execution_providers/nGraph-ExecutionProvider.md#deprecation-notice
**Deprecation Notice**
| | |
| --- | --- |
| Deprecation Begins | June 1, 2020 |
| Removal Date | December 1, 2020 |
Starting with the OpenVINO™ toolkit 2020.2 release, all of the features
previously available through nGraph have been merged into the OpenVINO™
toolkit. As a result, all the features previously available through
ONNX RT Execution Provider for nGraph have been merged with ONNX RT
Execution Provider for OpenVINO™ toolkit.
Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated
starting June 1, 2020 and will be completely removed on December 1,
2020. Users are recommended to migrate to the ONNX RT Execution Provider
for OpenVINO™ toolkit as the unified solution for all AI inferencing on
Intel® hardware.
* Remove nGraph Licence info from ThirdPartyNotices.txt
* Use simple Test.Run() for tests without EP exclusions
To be consistent with rest of test code.
* Remove nGraph EP functions from Java code
* Add iOS test pipeline and a sample app.
* clean up the unused code.
* clean up.
* revert the unknown change
* disable the shared library for iOS.
* add open source notice text.
* ignore the skipped test.
* extract the common ortenv setup
* Add minimal build option to build.py
Group some of the build settings so binary size reduction options are all together
Make some cmake variable naming more consistent
Replace usage of std::hash with murmurhash3 for kernel. std::hash is implementation dependent so can't be used.
Add initial doco and ONNX to ORT model conversion script
Misc cleanups of minimal build breaks.
* add training dockerfile tested for examples repo
* forgot pytorch patch for build from source
* make apt-get update -y adjacent apt-get install -y due to Docker caching rules
* comment for mellanox libraries
* mpi4py comment as I forgot where it came from
* apparently curl not included anymore
* grr.. nvidia change nccl location
* dont need findnccl.patch after nvidia changed nccl location
* pr comment /opt/ompi4 => /opt/openmpi-xxx
* switch to pip install pytorch
* use Release instead of RelWithDebInfo
* comment wording
* wordin
* missed RelWithDebInfo => Release
* replace Mellanox with libibverbs
* stale comment
* ordering
* no more ninja
* add / at end of copy
* update cgmanifest.json
* pr comments
Co-authored-by: suffian khan <sukha@OrtTrainingDev1.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net>
* Move nnapi dnnlib to subfolder
* dnnlib compile settings
* add nnapi buildin build.py
* add onnxruntime_USE_NNAPI_BUILTIN
* compile using onnxruntime_USE_NNAPI_BUILTIN
* remove dnnlib from built in code
* Group onnxruntime_USE_NNAPI_BUILTIN sources
* add file stubs
* java 32bit compile error
* built in nnapi support 5-26
* init working version
* initializer support
* fix crash on free execution
* add dynamic input support
* bug fixes for dynamic input shape, add mul support, working on conv and batchnorm
* Add batchnormalization, add overflow check for int64 attributes
* add global average/max pool and reshape
* minor changes
* minor changes
* add skip relu and options to use different type of memory
* small bug fix for in operator relu
* bug fix for nnapi
* add transpose support, minor bug fix
* Add transpose support
* minor bug fixes, depthwise conv weight fix
* fixed the bug where the onnx model input has mismatch order than the nnapi model input
* add helper to add scalar operand
* add separated opbuilder to handle single operator
* add cast operator
* fixed reshape, moved some logs to verbose
* Add softmax and identity support, change shaper calling signature, and add support for int32 output
* changed the way to execute the NNAPI
* move NNMemory and InputOutputInfo into Model class
* add limited support for input dynamic shape
* add gemm support, fixed crash when allocating big array on stack
* add abs/exp/floor/log/sigmoid/neg/sin/sqrt/tanh support
* better dynamic input shape support;
* add more check for IsOpSupportedImpl, refactored some code
* some code style fix, switch to safeint
* Move opbuilders to a map with single instance, minor bug fixes
* add GetUniqueName for new temp tensors
* change from throw std to ort_throw
* build settings change and 3rd party notice update
* add readme for nnapi_lib, move to ort log, add comments to public functions, clean the code
* add android log sink and more logging changes, add new string for NnApiErrorDescription
* add nnapi execution options/fp16 relax
* fix a dnnlibrary build break
* addressed review comments
* address review comments, changed adding output for subgraph in NnapiExecutionProvider::GetCapability, minor issue fixes
* formatting in build.py
* more formatting fix in build.py, return fail status instead of throw in compute_func
* moved android_log_sink to platform folder, minor coding style changes
* addressed review comments
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
* Mention OrtCreateSessionFromArray in C API doc
* Add make_unique implementation for use with C++11
* Add cgmanifest and TPN files as well
* Add annotation to cgmanifest to identify the component that uses the dependency
* Fixed typos in docs for 'onnx_test_runner'
* TensorRT Execution Provider (preview) release
Updated build instructions and component governence and third party notices for TensorRT execution provider release.
* test runner option for tensorrt
updated to add option for tensorrt.
* Introduction to TensorRT Execution Provider
Intro README for TensorRT Execution Provider.
* Update BUILD.md
* Update TensorRT-ExecutionProvicer.md
* corrected typo in the filename
* corrected typos
* updated with corrections.
* removed conflicting edits.
* Update BUILD.md
* Updated TPN
* Update batch_norm_op_test.cc
* Update ThirdPartyNotices.txt
* Update ThirdPartyNotices.txt
* Update readme with package links
* Update README.md
* Update README.md
* Update README.md
* Merged Ryan and TPN changes into single PR
* minor fix
* added mkldnn to GPU pipeline. Required by C# library as it is the default execution provider
