* copy changes from trt_and_mem
* second edits
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* change to cuda 11.4
* build with cuda 11.4
* Update Dockerfile.ubuntu_cuda11_1_tensorrt7_2
* add cmake extra defines
* cmake architectures
* fix cmake arch
* Delete ubuntu-18.04.Dockerfile
* Rename Dockerfile.ubuntu_cuda11_1_tensorrt7_2 to Dockerfile.ubuntu_cuda11_4_tensorrt7_2
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* removing previous ort args
* rename to cuda 11.4
* remove cuda 10_2
* delete trt 7.1
* remove 7.1
* Passing in cuda architecture to reduce build time
* always add submodule sync due to recursive cloning
* fix run command
* add and
* take away unused arms and share python installation script
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml
* Update Dockerfile.tensorrt
* cleanup file
* install python directly on dockerfile - move to scripts in future
* Update Dockerfile.custom-trt-perf
* adding cuda 11.1 for missing Libnvrtc.so.11.1
* Delete install_python.sh
* Add memory check for TRT perf
* Revise test app
* Add memory check for TRT perf
* Revise test app
* add test cases
* Modify script and add pipeline YAML
* remove redundant code
* temporarily change
* Change YAML
* revise test app
* fix minor bug
* code refactor
* small fix
* temporarily change for test
* prepare result log
* rm container when it exits
* code refactor
- Allow anyone to kick off a perf test here. Customize: branch, eps, model selection, cuda version.
- Only run shape inference when required.
- Kill errored out memory processes.
- Remove warmup run.
- Clean up script.
- Standalone_TRT is it's own "EP" vs as an additional run with TRT EP
* Add YAML file for pipeline
* Modify typo
* Add working directory
* Modify and test
* Modfiy and test
* Modify and test
* Modify and test
* Modify
* Modify
* Modify
* Modify
* Make sure to copy all the result files
* Add clearn up
* Modify
* Modify agent pool name
* Upload only specific artifacts
* Modify
* Integrated CI Pipeline for running TRT perf as well as added the “large amount of models” into perf model target
* Fix bug
* Fix bug
* Add reading the information regarding previously known failing models
and then skip testing them during benchmark/validation
* Modify the script file for CI
* Replace print with logger.info
* Fix bug
* Fix bug
* Refine the code
* Modify the script so that it can capture script segmentation fault while
running ORT
* Fix bug
* fix bug
* fix bug
* Add debug info
* fix bug
* Refine perf code
* Refine the code
* fix bug
* Code refactoring
* change many-models path
* remove metadata after validation/benchmark are done
* Update README.md
* Fix bug so that metadata doesn't hold stale value
* Remove hardcode and update README
* Add arguments to the script to make it run correctly
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Update linux-gpu-tensorrt-ci-perf-pipeline.yml for Azure Pipelines
* Fix bug so that metadata doesn't hold stale value
* Fix small bug of finding test dataset directory for FP16 test data, as
well as modification of some output information
* use -i random for perf test of TRT changes
Co-authored-by: Olivia Jain <oljain@microsoft.com>
