Previously, we put the "bin" folder of all the CUDA verions in the system PATH. And 10.2 is in the front. It's a mess.
So I've removed all of them from the system PATH env. But I need to add one of them back through build scripts.
(The problem only affect the C# test, not the C/C++ tests that forked from build.py).
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.
1. Move Win GPU pipeline to VS2019
2. Move C API pipeline to VS 2019
3. Move nuget mklml pipeline to VS 2019
4. Move windows no contrib ops pipeline to VS 2019
Advance commit to 4df80d5865a9d4e97f6d0b9304d4316115a04d9e
Add generated code for the commit before editing.
Import more featurizers.
Rename Automl ops domain to mlfeaturizers.
Rename conditional compilation macro.
Move and rename files getting rid of automl
Rename --use_automl build switch to --use_featurizers
Rename CMake option accordingly. Rename automl CMake targets.
Adjust CI and packaging pipeline switches.
Rename namespace automl to featurizers.
* 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
* remove memory copy between CUDA and TRT
* add info to RegisterExecutionProvider input
* use new IDeviceAllocator for trt allocator
* remove SetDefaultInputsMemoryType from TRT EP
* remove onnx-tensorrt 5.0
* add submodule onnx-tensorrt branch 5.1
* remove redundancy
* Update transformer_memcpy.cc
* Update tensorrt_execution_provider.cc
* switch to TensorRT 5.1.5.0
* update python binding
* disable failed test case on TensorRT
* Update activation_op_test.cc
* upgrade to TensorRT container 19.06
* update according to feedback
* add comments
* remove tensorrt allocator and use cuda(gpu) allocator
* update onnx-tensorrt submodule
* change ci build cuda directory name
* Update cuda for python wheels
* Update cuda for python wheels
* Update cuda for python wheels
* Update azure-pipelines-py-packaging.yml
* Update to cuda 10
* Only test win gpu
* Update cuda for python wheels
* Use manylinux2010 image to build linux python wheels
Allow wheels built to truly be compliant with a manylinux policy
* 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
* Adding versioned dlls to tar/zip packages
* fix syntax error
* fix version name of dylib
* minor fix in the target
* update pattern for versioned dylib files
* 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
* cross compile x86 linux
* fix comments
* install multilib for ubuntu cross compile
* remove tailing slash
* fix -fPIC relocations for x86 target too
* add asm make flag
* fix x86 compile err
* test x86 with zlib and png
* Disable zlib from x86
* install x86 python header
* remove cross-compiling changes
* test 32bit ubuntu
* add x86 ubuntu docker file
* add x86 as arch parametr for docker build
* config pipeline
* avoid dotnet install
* install cmake
* skip dep install
* use latest ubuntu
* install latest cmake
* install x86 deps
* configure cmake
* install ninja
* correct ninja dir
* apt get re2c
* install onnx
* set processor x86
* disable warning
* skip test
* disable test
* disable test
* find lib
* fix typo
* restore test
* disable backend model test
* disable test
* fix test err
* stop installing onnx
* disable onnx test on x86
* restore yml
* mergef with master yml
* cancel needless config setting
* enable x86 flag
* restore all onnx tests
* fix yml typo
* install onnx
* add back x86 flag
* disable cases
* disable case
* disable cases
* add macro to disable cases
* fix typo
* print platform
* remove condition
* use CUDA 9.1 for both linux and windows
* added powershell scripts for cuda props setup/cleanup
* fix yml syntax
* set path to cuda9.1 bin
* correct label
* ad --cuda_version
* added some log to browse the directory
* disabled jobs other than win gpu to save some resource while testing
* add msvc_toolset=14.11
* added more logs
* log the props file
* remove setting vcvarsall
* try some modificationi n build.py
* fix typo
* let the config Step modify envoronment
* set some more env vars manually
* try reordering vcvars after cuda props copying
* use single script for build and test
* single line script
* remove extra quote
* cleanup trial changes
* 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
